Skip to Main Content
Skip Nav Destination

The complex nature of illicit substances makes for one of the most challenging sample matrices for forensic analytical chemists and this is coupled with the need to deal with the overlay of legal and health policies. This chapter looks at the key fundamental concepts that are the foundation for successful detection strategies towards traditional illicit substances. The analyst needs to consider a range of challenges associated with sampling regimens and how industry standards influence these across a range of jurisdictions. Significant technological advancement has occurred in this space in recent years and this chapter highlights the current forensic analyst's toolbox while aligning this with the policy considerations.

The description of illicit drug use needs to be placed in the context of the time and jurisdiction in which they are defined because many substances that are considered ‘illicit’ in our current time may have been used for legitimate purposes in the past. Further to that, some substances, such as diacetylmorphine (commonly known as heroin) are used legally by the medical profession and simultaneously used illicitly outside of this setting.1,2  The access to diacetylmorphine for Australian medical practitioners is controlled while it is accessible for those in the United Kingdom, meaning that the legislative environment needs to be considered when one defines the impact of an illicit substance. It is also important to note that the trends of illicit drug use within a country change considerably across time and this leads to changes in the response from authorities on the control of illicit substances. If we consider two illicit substances, heroin and methamphetamine, in the Australian context since the mid-1990s, a dramatic change can be observed. Within Australia 14 341 heroin- and opiate-related arrests were documented in 1998 and 1999 3,4  which dropped to 2161 arrests between 2006 and 2007; in the same time period the arrests for methamphetamine use rose from 8000 to 15 000. This prompted the Australian authorities to probe the reasons behind the increasing methamphetamine use and, with the aid of a federal forum, a large-scale funding plan to deal with the issue was developed.

The approach taken by a forensic analyst will be influenced by the legislative environment they find themselves in and factors such as the trigger threshold limit for a drug prosecution may also need to be considered. However, when it comes to the foundational analytical approach analysts worldwide need to be focussed on the figures of merit: sensitivity and selectivity afforded by the instrument in question. It is with this in mind that this chapter will describe the considerations for chemical detection of illicit substances with a focus on recent technological advancements.

One of the great challenges for illicit drug detection is understanding the implications that arise due to the nature of the sample matrix. In order for the analysis results to be used in a court of law the context of the sample collection, and how sampling may have an impact on further steps in the analytical procedure, need to be understood. When a large sample has been seized it may be important to not only determine the type of illicit substance present but also the total mass due to the fact that for some analytes sentencing can vary based on these factors. Substances that are produced illicitly vary in purity and some products contain excipients which lead to a more challenging sample matrix. However, it is worth noting that in most jurisdictions it is the total weight, including the excipients, that is used in the court of law.

Illicit or controlled substances are typically categorised into groups according to their effects on the user, how they are ingested or by their chemical structure backbone. Table 1.1 displays the chemical structures of numerous traditional substances that will be mentioned throughout this chapter and are grouped according to effect (stimulants, depressants, hallucinogens/psychedelics), method of intake (inhalants), and structural identity (narcotics/opioids, benzodiazepines).

Table 1.1

Chemical structures of traditional substances that are abused: amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA, ecstasy), cocaine, nicotine, ethanol, ketamine, diazepam, alprazolam, amyl nitrite, heroin, codeine, lysergic acid diethylamide (LSD) and cannabis (tetrahydrocannabinol, Δ9-THC)

StimulantsDepressantsNarcotics/opioids
Amphetamine  Ethanol  Heroin  
Ketamine  
Methamphetamine  
Codeine  
Benzodiazepines 
MDMA  Valium (Diazepam)  
Hallucinogens/psychedelics 
Cocaine  LSD  
Xanax (Alprazolam)  
Nicotine  
Cannabis (Δ9-THC)  
Inhalants 
Amyl Nitrite  
StimulantsDepressantsNarcotics/opioids
Amphetamine  Ethanol  Heroin  
Ketamine  
Methamphetamine  
Codeine  
Benzodiazepines 
MDMA  Valium (Diazepam)  
Hallucinogens/psychedelics 
Cocaine  LSD  
Xanax (Alprazolam)  
Nicotine  
Cannabis (Δ9-THC)  
Inhalants 
Amyl Nitrite  

It is important to note that not every substance mentioned in Table 1.1 and throughout this chapter are deemed ‘illicit’, such as nicotine and ethanol (alcohol), however they are abused in a similar fashion to the controlled substances and therefore are often reported as a drug. Also, substances often have more than one effect on the body, such as cannabis, which can act as a stimulant, depressant and a mild hallucinogen, that further adds to the complex nature and impact of these substances.

When large seizure samples or multiple exhibits are concerned the analyst needs to consider a sampling regime that will allow for an accurate representation of the entire material. The United Nations Office on Drugs and Crime (UNODC) has prepared detailed guidelines on representative drug sampling in collaboration with the Drugs Working Group.5  The group defines some key items for the analysts to follow including:

seizure; the entire amount of the drugs collected

population; the collection of the seized items under examination

package; a container for a single unit or number of units of the seized drug

unit; a single component of the population such as an individual tablet

sample; a unit or number of units selected from the population forming the analytical sample of interest.

The representative sampling process allows for some direction on behalf of the analyst, where units are described as being similar based on external characteristics, such as the size of the crystals or the colour of the powder. Consideration needs to be made around how homogeneous the sample is and for a liquid sample this is less of an issue than for a solid powder where the range of particle size needs to also be established.6  Beyond this general characterisation via an observation process, a randomised sampling approach needs to be developed that allows for the sample to truly reflect the population and that each unit to be sampled from the population has an even probability of being selected. The UNODC team acknowledge that this is quite a difficult process in practice and that analysts typically use a black box approach to randomisation.

It is possible to develop an arbitrary sampling regime however this has the potential to result in a large number of samples from the population being sampled in order to generate a representative sample. To overcome this, a statistical sampling approach should be considered and a large body of work has been done in this area. This can be divided into frequentists' approaches, such as hypergeometric distribution and binomial distribution, and Bayesian approaches, with Bayesian approaches increasing in use within modern forensic chemistry. The overall aim is to develop a clearly definable and accountable sampling method that gives a true representation of the sample and is suitable for presentation in a court of law.

When a small sample forms part of an investigation several aspects need to be considered; firstly, jurisdictions require ‘usable’ quantities of the illicit drug to be defined in order to lead to a prosecution. Secondly, with a small sample size the analyst needs to be mindful to prioritise the steps in the analysis. The analyst in these cases needs to consider using non-destructive analytical procedures, for example nuclear magnetic resonance (NMR) spectroscopy prior to destructive approaches such as ionisation of the sample when mass spectrometry (MS) is used. The prioritisation of the analytical approach may have implications for the capacity to fully realise the evidence in a court of law; however it is important to recognise much of the work of an analyst is focussed on understanding illicit drug systems, public water recycling monitoring for example, and as such the research contributes to the broader view of illicit substances within society. Typically illicit drug users have a higher rate of chronic diseases and the consolidation of a societal view of the use of illicit substances has the capacity to inform public health responses and coroners' proceedings.7  This burden to the public health system can be considerable and interestingly opioids presented 0.9% of the total disease and injury burden in Australia in 2018, including through drug dependence and accidental poisoning.8 

The sample matrices presented to an illicit drug analyst bring a range of challenges and the nature of the matrix varies considerably depending on the area of research. The illicit drug seizure samples most commonly include pills, powders and liquids and these often contain excipients and filler compounds that require an understanding by the analysts. It is worth bearing in mind that illicit drug detection forms a key component of the toxicological studies performed during the coroner's process and in analysing environmental contamination by illicit drugs such as in waterways. The matrices presented by these sample types include biological materials and complications from temporal processes such as metabolism need to be considered. Here we will describe the general considerations for dealing with the sample matrix and further discussion relevant to each forensic field will be discussed later.

The matrix that is co-extracted from the illicit drug sample can influence the signal response that is used for the determination of the target analyte. This can have either a positive or negative effect and as such directly impacts the accuracy, linear range and reproducibility of the analysis.9  The International Union of Pure and Applied Chemistry (IUPAC) define this as ‘the combined effect of all components of the sample other than the analyte on the measurement of the quantity. If a specific component can be identified as causing an effect then this is referred to as interference.’10  The central issue is presented clearly by B. K. Matuszewski et al.11  where they define that it is important to consider the experimental and the validation approach. The team goes further to state that the thinking in order to eliminate the issue needs to include assessment of the matrix effect and that it is important for the researcher to outline the way in which they deal with the matrix effect as this may be important for translation to future studies. In order to remove the issues associated with the matrix effect, a quantitative process was developed including looking at the absolute and relative matrix effects on a series of novel drugs. This was performed using high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) and the group developed the technology to look at the true recovery of the target analyte from a complex sample matrix. Matuszewski et al. extend on this to state that determining the extent of the matrix effect means that the reliability and selectivity of the HPLC-MS/MS method can be defined.11  This can then be used by the analyst to develop an approach to test and improve the selectivity afforded by the system. This important approach, while focussed on HPLC-MS/MS in this case, can be more broadly used across the range of illicit drug detection instrumentation.

The use of internal standards is well established in analytical chemistry and they are important in the determination of illicit substances, dealing directly with reproducibility issues or the loss of solution during sample handling. In all cases one is dealing with an analyte (X) and a standard (S) and the analyst is looking to determine a relationship between proportions of these two. The goal is to look at the factor (F) that defines this proportion noting that the signal response from the analyte and the standard are concentration dependent which is described in the equation below:

graphic
where Ax is the area of the analyte signal, As is the area of the standard signal and F is the proportionate response factor.

One challenge that arises with the use of internal standards is the ability to find one that is suitable as it needs to respond similarly to sample pre-treatment, have a signal intensity that is in a similar range to the analyte of interest and is not present in the sample matrix. It is desirable that the internal standard will respond to factors that affect the analyte of interest in a similar fashion including its capacity to be ionised for mass spectral detection, as an example. Internal standards are subsequently utilised to generate either the calibration curves for dealing with simple systems or, for more complex sample matrices, with a standard addition approach.

Standard addition is a quantitative analytical approach where the standard of the molecule of interest is added directly to aliquots of the sample in question in order to account for the matrix effect, particularly with complex samples where a simple calibration curve is not a feasible option. Once the standard has been added one can observe the increase in signal intensity allowing for the determination of the amount of analyte present in the original sample. This approach requires the analyst to generate a series of standards with increasing amounts of the standard present and are typically small in order to limit the effect on the matrix at large.

The following equation can be used to determine the analyte concentration:

graphic
where Xi refers to the concentration of the analyte in the sample of interest, Sf + Xf is the concentration of the analyte and the standard in the final solution, Ix is the signal intensity from the initial solution and Is+x is the signal from the final solution.

This establishes the data set that can be used to generate a standard addition curve (see Figure 1.1)12,13  and the straight line that is formed is extrapolated to the x axis. This value is indicative of the concentration of the analyte that would need to be added to have observed the signal intensity relative to that of the analyte in the genuine sample.

Figure 1.1

Standard addition curve where the data is extrapolated to the x axis in order to determine concentration of unknown. This standard additions calibration method was performed on calibrant-loaded paper-based devices: (a) iron(iii) thiocyanate assay, (b) proteins–bromophenol blue assay. Reproduced from ref. 13 with permission from Elsevier, Copyright 2017.

Figure 1.1

Standard addition curve where the data is extrapolated to the x axis in order to determine concentration of unknown. This standard additions calibration method was performed on calibrant-loaded paper-based devices: (a) iron(iii) thiocyanate assay, (b) proteins–bromophenol blue assay. Reproduced from ref. 13 with permission from Elsevier, Copyright 2017.

Close modal

While this approach is more complex than a traditional calibration curve it gives the analysts an understanding of the matrix effect impacting the sample.

When describing the matrix effects in illicit drug analysis mass spectrometry is often used as the guiding instrument because, even though it offers the analyst great sensitivity and selectivity, it is prone to matrix effects, particularly with electrospray ionisation.14  The matrix effect has been studied for some time in mass spectrometry although it should be noted here that it is a very complex process and that in many circumstances a full understanding of the physicochemical process leading to the effect is not always fully defined. However, this does not limit the analyst from correcting for the effect, and because of this the general principles of understanding the matrix effect will focus on mass spectrometry below. Importantly, in any system the matrix effect must first be assessed so that it can be minimised, and the United States Food and Drug Administration (FDA) state that is should be dealt with via post-column infusion and post-extraction addition.

Post-column infusion was first proposed for environmental analyses by a team lead by Choi, generating an approach for introducing internal standards via direct infusion into the flow stream of the liquid chromatography system.15  This approach was shown to be useful in characterising and adjusting for the quantitative errors that were generated by a matrix effect that led to a reduction in signal intensity. Importantly, this approach is independent of the internal standard because it is detected in the instrument at the same time as the analyte of interest. Further, this approach gives the analyst the capacity to use isotopically labelled internal standards to bring greater control to the system. This approach has been shown to be particularly useful when looking at a range of analytes as it allows for the use of one internal standard rather than one per analyte required with more simplistic approaches.16  In addition, the approach is reliable for the trace level detection of analytes and with the aid of high throughput approaches it has been used on complex sample matrices such as urine.17 

Post-extraction addition is designed to quantitatively understand the determination of the analyte of interest in a standard solution, compared with that of one spiked into the system with a known concentration after extraction. An example of the importance of this has been described by a team lead by Saar who describe the influence of the matrix effect on the determination of antipsychotics in human blood.18  The matrix effects in this study were determined using a post-extraction addition upon samples that were extracted from blood bank donors, where the extracts were reconstituted in the mobile phase which had a known concentration of analyte. The group showed that it was important to consider the impact of matrix effects for the determination of drugs in complex biological specimens.

This chapter, while focussing on advanced detection approaches, needs to place some context on the considerations that an analyst in the field takes to gain a comprehensive understanding of the illicit substance. Typically, forensic chemists are required to get an insight into the substance they are presented with and this is usually informed with the use of preliminary testing. These preliminary tests include chemical colour tests, microcrystal tests and simple separation approaches such as thin layer chromatography (TLC); and they are helpful towards informing the best approach for sampling and analysis of the bulk product.

Chemical colour tests are typically the first screening method on seized materials as they are quick, sensitive, only require small amounts of sample and can be done in the field with instant results.19  The tests involve visualisations of colour changes when samples containing illicit substances are reacted with certain reagents. As an example, for materials suspected of containing amphetamines, there are three widely utilised colour test reagents, including Marquis, Simon, and Chen. Gallic acid can also be used to presumptively identify amphetamines however common precursors such as safrole are likely to produce false positive results, therefore it is not widely utilised. An example of a set of complementary colour tests includes results for amphetamine-based analogues via the Marquis test which generates the following: amphetamine (orange, brown), methamphetamine (orange, brown), MDMA (dark blue/black). A second test with the Simon reagent generates a deep blue colour with methamphetamine and MDMA, and finally the Chen reagent elicits a purple colour for ephedrine and pseudoephedrine.20 

The Marquis test can distinguish between amphetamines and their ring-substituted derivatives such as MDMA, while the Simon's test is also used for secondary amines such as MDMA. The Chen's test is useful in distinguishing between starting materials such as ephedrine or pseudoephedrine with amphetamine or methamphetamine; however, in all cases, numerous adulterants can give false negative or false positive results which is why these tests are strictly presumptive.

Microcrystal tests are similar in that they are quick and sensitive however they are not typically used at scene and therefore not as utilised as the chemical colour tests. A microcrystal test involves the reaction of the target compound with a chemical reagent to form crystals which are then examined and compared against reference materials under a polarizing microscope. Similarly, TLC is not commonly utilised at scene despite being a rapid and sensitive separation technique, which is flexible in terms of both stationary and mobile phases. It involves the separation of components within a sample as a solvent (mobile phase) diffuses along a TLC plate (stationary phase) on which the sample and reference standards have been spotted. Once spotted and developed the individual components can be visualised under UV light at 254 nm or by application of chemical reagents such as ninhydrin or Dragendorff.20 

Coupled with these simple approaches the advanced characterisation technologies have been interrogated by the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) who have prescribed a range of validated methods targeting traditional illicit substances. The importance of this work beyond the forensic field has been well described by Harper et al. who have prepared an excellent overview of forensic drug testing methodology and its suitability towards harm reduction for point-of-care services.21  In this work the team has considered a range of factors that are of interest to laboratory managers including the flexibility of the instruments, the cost associated with using the instruments and analysis time. A key table from this work is shown in Table 1.2.

Table 1.2

Factors that are of interest to laboratory managers when considering instrumentation for illicit drug detection. Reproduced from ref. 21, https://doi.org/10.1186/s12954-017-0179-5, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/

MethodDiscriminationSubstancesIdentify (qualify)Amount (quantify)Destroy sample?LabPoint of careCost (USD)Ease of useTime required for results
Most discriminatory Mass spectrometry ★★★★★ Virtually any ✓ ✓ Yes ✓  $5000 (used, older)–200 000+ (new, advanced models), Plus recurring costs (reagents, servicing) Intermediate–advanced (depending on model) Minutes 
Infrared spectrometry ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $4000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Second to minutes (including portable) 
Raman spectroscopy ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $5000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Seconds to minutes (Including portable) 
X-ray diffractometry ★★★★ Crystalline (solids) ✓ ✓ No ✓  $50 000–250 000+, Plus recurring costs (reagents, servicing) Advanced–expert Minutes to hours 
Least discriminatory Thin-layer chromatography ★★★ Most common drugs of abuse; possibly not some novel psychoactive substances ✓  Yes ✓ ✓ Initial supplies: $1000–3000, Recurring costs: $100–1000 per month depending on volume of usage (bulk reagents) Basic–intermediate Minutes to hours 
Ultraviolet spectroscopy ★★★ Most common drugs of abuse ✓  No ✓ ✓ $3000–10 000 Basic–intermediate Minutes 
Spot/color tests ★★ Most common drugs of abuse; must be already characterized (i.e., possibly not some) ✓  Yes ✓ ✓ Approximately 2–5 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Basic–intermediate Seconds to minutes 
Microcrystalline tests ★★ Several ✓  Yes ✓ ✓ Approximately 2–4 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Immunoassay ★★ Various metabolized drugs in urine samples ✓  Yes ✓  $5000–20 000 for initial equipment (analyzer), Recurring costs: $300–1000 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Urine dipstick test ★★ Fentanyl ✓  Yes  ✓ Approximately 1–5 dollars per test (in house), Recurring costs: $50–400 per month, depending on volume of usage Basic–intermediate Seconds to minutes 
MethodDiscriminationSubstancesIdentify (qualify)Amount (quantify)Destroy sample?LabPoint of careCost (USD)Ease of useTime required for results
Most discriminatory Mass spectrometry ★★★★★ Virtually any ✓ ✓ Yes ✓  $5000 (used, older)–200 000+ (new, advanced models), Plus recurring costs (reagents, servicing) Intermediate–advanced (depending on model) Minutes 
Infrared spectrometry ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $4000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Second to minutes (including portable) 
Raman spectroscopy ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $5000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Seconds to minutes (Including portable) 
X-ray diffractometry ★★★★ Crystalline (solids) ✓ ✓ No ✓  $50 000–250 000+, Plus recurring costs (reagents, servicing) Advanced–expert Minutes to hours 
Least discriminatory Thin-layer chromatography ★★★ Most common drugs of abuse; possibly not some novel psychoactive substances ✓  Yes ✓ ✓ Initial supplies: $1000–3000, Recurring costs: $100–1000 per month depending on volume of usage (bulk reagents) Basic–intermediate Minutes to hours 
Ultraviolet spectroscopy ★★★ Most common drugs of abuse ✓  No ✓ ✓ $3000–10 000 Basic–intermediate Minutes 
Spot/color tests ★★ Most common drugs of abuse; must be already characterized (i.e., possibly not some) ✓  Yes ✓ ✓ Approximately 2–5 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Basic–intermediate Seconds to minutes 
Microcrystalline tests ★★ Several ✓  Yes ✓ ✓ Approximately 2–4 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Immunoassay ★★ Various metabolized drugs in urine samples ✓  Yes ✓  $5000–20 000 for initial equipment (analyzer), Recurring costs: $300–1000 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Urine dipstick test ★★ Fentanyl ✓  Yes  ✓ Approximately 1–5 dollars per test (in house), Recurring costs: $50–400 per month, depending on volume of usage Basic–intermediate Seconds to minutes 

Basic—requires simple (hours to days) training by someone who knows the technique or theory, but is not an expert in the field, i.e., someone with intermediate, advanced, or expert skill/knowledge. Intermediate—requires a higher level of knowledge or skill, although that may be obtained from either following previous instructions obtained (i.e., gaining experience) while a basic user, or from further instruction from an advanced or expert level user. Usually requires days to weeks of experience depending on technique. Advanced—requires some college or university level theory or experience. Usually taught directly or indirectly by an expert in the subject/field. Occasionally, an intermediate user may become advanced without advanced education through diligence and interest. Requires weeks to months (a typical semester). Expert—an expert in the area, almost always has post-secondary education related to the field. May be a bachelor, master, or PhD holder or very highly specialized training. Instruction may also be provided by someone from a device manufacturing company who provides a seminar or some sort of direct training in usage of a technique or device. Typically always requires months to years depending on difficulty of the subject.21 

Analytical instrumentation has advanced significantly in the last decade and, with the aid of spectral data libraries, characterisation for the forensic analyst typically uses some form of automation. It is important to note two aspects of discussion from Table 1.2; the ease of use and the time required for results. The decreasing cost over time of the instruments coupled with simpler interfaces has enabled drug testing facilities to work at scale where typically a chief analyst oversees a group of technicians who run and maintain the instruments. This has enabled greater efficiency due to the speed of analysis which has a significant effect on the management of the analytical data. Typically forensic analytical laboratories have a bank of instruments supplied from the one manufacturer in order to aid the development of standard operating procedures and these flow on to the data analysis where complex automated data analysis is commonly employed. While for an individual case the reporting is focussed on the analytes of interest the broader collection of the data enables analysts to build more complete profiles of the illicit substances within their jurisdiction. As outlined by Harper et al., beyond direct policing this type of approach enables improvements to in-community harm reduction strategies, including improving point-of-care support.21  This has extended in recent years to the monitoring of illicit substances at events such as music festivals in order to directly inform users of the chemical identity of the illicit substance in question. As the focus of some aspects of chemical analysis leave the traditional laboratory environment and moves to point-of-care locations, such as the music festival, the portability and robustness of the analytical detection system becomes even more critical.

The point-of-care approach makes for an interesting focal point for the illicit drug analyst because it is placed squarely at the nexus between high-quality analytical science, public opinion, policing strategies and the legal system. As such the current environment for point-of-care approaches does vary considerably between different jurisdictions which allows for detailed comparison of the respective approaches. A recent study looked into the approach in Canada focussing on events and music festivals in British Columbia.22  The group highlighted that the key approach for point-of-care is to measure the typical types of illicit substances at music festivals, and they showed that the vast majority were psychedelics or stimulants. The work revealed that drug checking has the potential to help with harm reduction for users, based on the fact that the team found the majority of the samples contained harmful adulterants and novel psychoactive substances which would have led to complications for the user. From a detection point of view the study used Fourier transform infrared (FTIR) spectroscopy to probe the samples of interest and coupled this approach with a targeted fentanyl immunoassay strip. The analysts performed 336 tests and coupled the results with the expectation of the user who noted that the illicit substances tested were likely to be psychedelics (69.3%) or stimulants (19.6%). From the psychedelic samples, a large proportion of 72.5% contained the expected substance, and 11.6% contained contaminants. From the stimulant sample set, 62.1% contained the assumed while 36.4% presented some contamination. Importantly, the team found adulterants such as fentanyl, levamisole, and phenacetin in the contaminated samples which are of concern to the user but also highlights the challenges faced by a forensic analyst due to the unknown complexity of the samples containing these novel psychoactive substances. Other approaches that do not directly engage with the user have been developed to monitor the illicit substances at music festivals which focus on analysing the waste water and pooled urine from the festival.23–25  This approach enables a good body of chemical information to be gained but has a reduced capacity to influence harm reduction strategies. These types of studies have proven to be effective at monitoring illicit substances and can not only target the drug molecule but also focus on the metabolites which allows the analysts to demonstrate that the substance was taken. With the aid of liquid chromatography coupled with mass spectrometry instrumentation, the researchers showed in these cases drugs such as ecstasy, cannabis and ketamine were the most widely used amongst a total of more than 25 substances identified. Further, this type of approach enabled the analysts to monitor the drugs in the waste water as the music festival progressed over several days which gives insight into the drug use trends and enables peak usage to be identified. The work lead by Makulak really demonstrated the power of analytical detection protocols when large data sets are processed with the aid of principal component analysis.25  Interestingly, the researchers were able to show a relationship between type of music festival and the significant use of the drugs across the respective festival as outlined in Table 1.3.

Table 1.3

Dominant and change in drug consumption at festivals with differing music styles. Adapted from ref. 25 with permission from Elsevier, Copyright 2019

Dominant musical styleCross-section surveys/dominant drugsWastewater analysis/dominant drugsDrugs with significant change in consumption/festival in our study
Pop/rock Alcohol, cannabis26  MDMA/ecstasy, methamphetamine, cocaine27  MDMA/ecstasy, cocaine, cannabis 
MDMA/ecstasy28  Top fest, Pohoda 
Dance MDMA/ecstasy, speed, tobacco, alcohol, solvents, cannabis, inhalants, amyl nitrite, cocaine, LSD, benzodiazepines, ketamine29–31   MDMA/ecstasy, cocaine, cannabis 
Grape 
Multi-music Alcohol, tobacco, cocaine, cannabis MDMA/ecstasy32  MDMA/ecstasy, cocaine 
MDMA/ecstasy, cocaine33  Skalické dni 
Country/folk  Cocaine, codeine32  No significant change 
Guláš fest 
Metal Alcohol, tobacco, cannabis34  Tobacco35  No significant change 
VanDaal fest 
Dominant musical styleCross-section surveys/dominant drugsWastewater analysis/dominant drugsDrugs with significant change in consumption/festival in our study
Pop/rock Alcohol, cannabis26  MDMA/ecstasy, methamphetamine, cocaine27  MDMA/ecstasy, cocaine, cannabis 
MDMA/ecstasy28  Top fest, Pohoda 
Dance MDMA/ecstasy, speed, tobacco, alcohol, solvents, cannabis, inhalants, amyl nitrite, cocaine, LSD, benzodiazepines, ketamine29–31   MDMA/ecstasy, cocaine, cannabis 
Grape 
Multi-music Alcohol, tobacco, cocaine, cannabis MDMA/ecstasy32  MDMA/ecstasy, cocaine 
MDMA/ecstasy, cocaine33  Skalické dni 
Country/folk  Cocaine, codeine32  No significant change 
Guláš fest 
Metal Alcohol, tobacco, cannabis34  Tobacco35  No significant change 
VanDaal fest 

These studies give an example of how modern analytical approaches towards illicit drug detection are not only focussed on policing but can also be used to inform public policy. Importantly, approaches that use spectroscopic and mass spectral detection are most commonly used, and typically chromatographic separation forms a key component of the analytical approach. Another way to view the extensive work in this area is to consider one illicit substance and understand what analytical work has been done to characterise the compound for forensic purposes. The reader could look into any illicit substance in this way; however, to display the breadth of analytical detection procedures, as an example we shall look at methamphetamine determination. Methamphetamine has received a great deal of attention from research groups all over the world as the ice epidemic continues to grow and make headlines. Identifying methamphetamine in different mediums such as urine,36  saliva,37  blood,38  plasma,39  hair,40  finger nails,41  fingerprints,42  sweat43  and semen44  have been areas of interest for forensic analysts. Due to the range of sample types and their complex nature, a variety of instrumentation has been used for the analysis of samples containing methamphetamine, details of which are summarised in Table 1.4.

Table 1.4

Reports in the literature towards improved detection of methamphetamine

Detection methodMedium typeLiterature reference
Gas chromatography–mass spectrometry Plasma and urine 39  
Gas chromatography–mass spectrometry Blood 38  
Gas chromatography–mass spectrometry Hair 40  
Gas chromatography–mass spectrometry Insects 45  
Gas chromatography–mass spectrometry Nails 46  
High performance liquid chromatography Commercial tablets 47  
Two-dimensional high performance liquid chromatography Street samples 48  
High performance liquid chromatography–mass spectrometry Urine 49  
High performance liquid chromatography–mass spectrometry Fingerprint 50  
High performance liquid chromatography–tandem mass spectrometry Urine 51  
Matrix-assisted laser desorption ionisation mass spectrometry Fingerprint 42  
Chemiluminescence Urine 52  
Electrochemiluminescence Street samples 53  
High performance liquid chromatography–electrospray ionisation mass spectrometry Human urine 54  
High performance liquid chromatography–fluorescence detector Human urine 36  
Fluorescence polarisation immunoassay Purchased standards 55  
Gas chromatography–Fourier transform infrared spectroscopy Purchased standards 56  
Head-space solid phase microextraction gas chromatography–mass spectrometry Purchased standards 57  
Ion mobility spectrometry Hair 58  
Isotope ratio mass spectrometry Street samples 59  
Laser microscopy Hair 60  
Desorption ionisation on silicon mass spectrometry imaging Fingerprint sweat 61  
Nuclear magnetic resonance spectroscopy Purchased standards 62  
2H Nuclear magnetic resonance spectroscopy Synthetic pathway products 63  
Capillary electrophoresis Urine 64  
Electromembrane surrounded-solid phase microextraction Urine and blood 65  
Surface plasmon resonance Purchased standards 66  
Field asymmetric ion mobility spectrometry Air 67  
Colorimetric test Mobile telephone 68  
Detection methodMedium typeLiterature reference
Gas chromatography–mass spectrometry Plasma and urine 39  
Gas chromatography–mass spectrometry Blood 38  
Gas chromatography–mass spectrometry Hair 40  
Gas chromatography–mass spectrometry Insects 45  
Gas chromatography–mass spectrometry Nails 46  
High performance liquid chromatography Commercial tablets 47  
Two-dimensional high performance liquid chromatography Street samples 48  
High performance liquid chromatography–mass spectrometry Urine 49  
High performance liquid chromatography–mass spectrometry Fingerprint 50  
High performance liquid chromatography–tandem mass spectrometry Urine 51  
Matrix-assisted laser desorption ionisation mass spectrometry Fingerprint 42  
Chemiluminescence Urine 52  
Electrochemiluminescence Street samples 53  
High performance liquid chromatography–electrospray ionisation mass spectrometry Human urine 54  
High performance liquid chromatography–fluorescence detector Human urine 36  
Fluorescence polarisation immunoassay Purchased standards 55  
Gas chromatography–Fourier transform infrared spectroscopy Purchased standards 56  
Head-space solid phase microextraction gas chromatography–mass spectrometry Purchased standards 57  
Ion mobility spectrometry Hair 58  
Isotope ratio mass spectrometry Street samples 59  
Laser microscopy Hair 60  
Desorption ionisation on silicon mass spectrometry imaging Fingerprint sweat 61  
Nuclear magnetic resonance spectroscopy Purchased standards 62  
2H Nuclear magnetic resonance spectroscopy Synthetic pathway products 63  
Capillary electrophoresis Urine 64  
Electromembrane surrounded-solid phase microextraction Urine and blood 65  
Surface plasmon resonance Purchased standards 66  
Field asymmetric ion mobility spectrometry Air 67  
Colorimetric test Mobile telephone 68  

Interestingly, the development of analytical technology for forensic applications is often limited by the uptake of new methods, due to the implications in the court room. New analytical methodologies need to be thoroughly tested and come under strict scrutiny in cross-examination of expert witnesses. Regarding methamphetamine determination, gas chromatography–mass spectrometry (GC-MS) is the most commonly used analysis method due to it being one of the first utilised in this space and tested under law. Typically, GC-MS systems use quadrupole mass spectrometers affording only low resolution data, whereas LC-MS commonly employs high resolution analysers (such as time-of-flight), which has improved the determination of methamphetamine. Bespoke techniques such as the others listed in Table 1.4 are typically used in the research domain. While these are not commonly used for prosecution purposes, they play a strong role informing the forensic analysts towards reliable methamphetamine detection.

Further chemical characterisation is often required when novel substances are found; other techniques, such as nuclear magnetic resonance spectroscopy, complement the data generated with mass spectrometry and are most often used for structural elucidation of the novel molecule. As such the next section will focus on the standard approaches taken for routine illicit drug detection and specific discussion of more recent advancements will follow. The SWGDRUG working group defines the appropriate spectral data for each illicit substance; FTIR, mass spectrometry and nuclear magnetic resonance spectroscopy form a key part of this data collection. The discussion below will focus on methamphetamine as an example traditional illicit substance and we shall see how it is determined with the aid of these key detection systems.

Fourier transform infrared (FTIR) spectroscopy has been widely applied for the interrogation of illicit drug samples and these approaches have been informed by the broad use of the technique in other fields such as pharmaceutical analysis, polymers and surface science. Importantly, the technique affords a chemical characterisation based on the fact that many types of molecule are infrared active and the signal generated is related to the specific functional groups associated with the molecule. The modern systems allow for an excellent signal-to-noise ratio and the accuracy and wavenumber resolution is very high. The typical FTIR spectra of several drugs (heroin, methamphetamine and ketamine)69  are shown in Figure 1.2, where the characteristic features are observed within the mid-IR region (4000 cm−1 to 400 cm−1) typically used for illicit drug detection.

Figure 1.2

FTIR spectrum of (A) heroin hydrochloride, (B) methamphetamine hydrochloride, and (C) ketamine hydrochloride. Reproduced from ref. 69 with permission from Elsevier, Copyright 2020.

Figure 1.2

FTIR spectrum of (A) heroin hydrochloride, (B) methamphetamine hydrochloride, and (C) ketamine hydrochloride. Reproduced from ref. 69 with permission from Elsevier, Copyright 2020.

Close modal

FTIR has the potential for use in applications requiring rapid quantification and, when coupled with a chemometric approach, it is a particularly powerful detection protocol. One of the great challenges for illicit drug detection is the complex batch-to-batch variation in sample matrices and this was clearly defined by Hughes et al. who presented the differences in the FTIR spectra for high and low concentration methamphetamine samples (Figure 1.3).70  Even with a simple model system the changes observed between the wavenumbers 2400 cm−1 and 3000 cm−1 are quite dramatic.

Figure 1.3

Typical attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FTIR) spectra of methamphetamine: (a) high concentration (78.6%), (b) low concentration (10.3% cut with MSM (methylsulphonylmethane)). Reproduced from ref. 70, https://doi.org/10.1371/journal.pone.0069609, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Figure 1.3

Typical attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FTIR) spectra of methamphetamine: (a) high concentration (78.6%), (b) low concentration (10.3% cut with MSM (methylsulphonylmethane)). Reproduced from ref. 70, https://doi.org/10.1371/journal.pone.0069609, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Close modal

The team used 1604 cm−1, 2460 cm−1, 2723 cm−1 and 2966 cm−1 for sample interrogation and described the key considerations for chemometric approaches towards comprehensive sample identification. The key driver of this approach was the long turnaround time with the identification and quantification methods at that point within the Queensland Health Forensic and Scientific Services. The police who were working undercover required drug analysis results within 24 hours and at the time this was taking up to one month; the police called for indicative results to be achieve in a timelier manner. The impact with this approach was linked to improving the court process and saving as much police time as possible and is an excellent example of scientific approaches being aligned with modern policing and policy positions. Up until this time the use of FTIR spectra for illicit drug identification had been performed with the aid of library spectra, and challenges presented by the complex excipients and precursor's contribution to the spectra meant that quantification was extremely difficult. Building on work done in the pharmaceutical industry the coupling of two partial least squares (PLS) models was used to develop a robust rapid quantification approach to methamphetamine determination. One of the models was based on the principal infrared spectroscopy peaks from the methamphetamine samples and the second approach used a hierarchical partial least squares model. Some important data pre-treatment considerations need to be addressed before chemometric data processing can be done. This includes manipulations such as baseline corrections and, depending on the analytical technique in question, internal standards and retention locking might need to be considered.

The power of this type of research identifies that spectral detection capacity can be enhanced by an advanced data handling process with the methamphetamine principal peaks PLS model and the hierarchical partial least squares model being robust approaches for methamphetamine quantification. These approaches gave an excellent root-mean-square error for the prediction (3.8 and 5.2), R2 values (0.9779 and 0.9637) and lower limit of quantification for methamphetamine (7% and 0.3%) from the test set, respectively. Importantly, the group showed that there was excellent correlation between the routine ultra-performance liquid chromatography-UV method typically employed within the Queensland services and the FTIR approach. One challenge for analysts in the forensic field is the time lag that comes from getting improved methods into regular use in the judicial process, due to the fact that the methodologies need to be established as robust for cross-examination in court. Further, a great deal of work is required in terms of developing standard operating procedures and understanding the complexities associated with matrix effects and sampling protocols, which often slows down the uptake of modern technological approaches for detection of illicit substances.

Fourier transform infrared spectroscopy has advanced to imaging capacity and some of the first reports of applications to model drug mixtures for forensic science were in 2006 by a team led by Ricci.71  The team was able to generate an image of model drugs collected using lifting tape typically employed by forensic scientists for fingerprint lifting. The FTIR images that were generated for the model drugs, in this case paracetamol and ibuprofen, were a significant advancement in the field at the time. A more advanced approach has been developed by Quayle and co-workers who were able to utilise ATR-FTIR coupled with elemental profiling and chemometric analysis highlighting the power and adaptability of FTIR detection.72  The work was performed on illicit tobacco and the complexity of the surface of these types of samples can be observed in Figure 1.4 which is an important consideration for forensic detection.

Figure 1.4

Complexity of forensic samples: (i) scanning electron microscopy (SEM) image of whole leaf licit cigarette tobacco with stomata visible, (ii) SEM image of treated niche Swedish Snüs tobacco with visible crystalline structures. Reproduced from ref. 72 with permission from Elsevier, Copyright 2016.

Figure 1.4

Complexity of forensic samples: (i) scanning electron microscopy (SEM) image of whole leaf licit cigarette tobacco with stomata visible, (ii) SEM image of treated niche Swedish Snüs tobacco with visible crystalline structures. Reproduced from ref. 72 with permission from Elsevier, Copyright 2016.

Close modal

This approach was the basis for applications of spectral imaging of illicit drugs and much of the focus on generating quality images revolves around dealing with the complex sample matrices. In the work by Ricci, the chemicals in the lifting tape were shown to interfere with some spectral peaks and so the team also focused on direct imaging of the sample prior to lifting. The advent of improved technology, affording advancement in the resolution of the system, enabled more complex samples to be explored. This type of approach has been used to look at the localisation of licit substances73  and has been refined for more recent case work applications, an example of which was developed by Lanzarotta and co-workers74  for cocaine HCl salt identification within complex samples. The advantage of this type of approach can be seen in Figure 1.5 where the cocaine is visually distinguished from the excipients allowing the forensic scientist to get a better handle on the sample matrix and aid training in this area.

Figure 1.5

ATR-FTIR Chemimap comparison of Powder 1 (a) and representative spectra from the cyan (b), purple (c), and grey (d) regions compared to a pure compound reference spectrum of cocaine HCl (e), IR image of Powder 2 (f), and representative spectra from the cyan (g), red (h), blue (i) and yellow (j) regions compared to a pure compound reference spectrum of methandrostenolone (k). Reproduced from ref. 74, https://doi.org/10.3390/s16030278, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Figure 1.5

ATR-FTIR Chemimap comparison of Powder 1 (a) and representative spectra from the cyan (b), purple (c), and grey (d) regions compared to a pure compound reference spectrum of cocaine HCl (e), IR image of Powder 2 (f), and representative spectra from the cyan (g), red (h), blue (i) and yellow (j) regions compared to a pure compound reference spectrum of methandrostenolone (k). Reproduced from ref. 74, https://doi.org/10.3390/s16030278, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Close modal

In a study by Johnston, contaminants representative of explosive residues and illicit drugs were placed in a latent fingermark in order to look at a very complex forensic-based sample.75  Adding to the complexity the fingermarks were aged in controlled light conditions and were analysed with the aid of FTIR spectromicroscopy over a period of 30 days. One of the images generated as part of this work highlights how the approach can aid the detection of illicit drugs and other substances of forensic importance to gain a temporal understanding of the system.

Johnston states the study demonstrates that contaminants are detectable up to 30 days after deposition within latent fingermarks and that fingermark chemistry has a minimal effect on sample integrity. This type of approach can aid the judicial process and may be coupled with biological fluid transfer which is an expanding field of interest to forensic practitioners, particularly in relation to DNA evidence where gaining an understanding of who has handled and item may be important to a case. Importantly, with this approach the fingermarks are also clear allowing for the drug detection component to be related to the more traditional fingermark examination requirements of law enforcement.

The flexibility of FTIR as a detection strategy is evinced through the coupling of the detection system with gas chromatography systems. This is not common practice in routine illicit drug analysis laboratories however the discrimination power afforded by the FTIR spectrum coupled with a chemometric data analysis approach has been successfully used by Praisler et al. to probe a range of drug moieties including methamphetamine.56  The chemometric approach using principal component analysis has been successful in the analysis of drugs in the vapour phase and is applicable to many drugs of abuse that are volatile enough to be analysed with a gas chromatography system. The FTIR detection approach was shown to be useful when the structure of a previously unknown molecule needs to be determined if limited information is present on the molecule from within the traditional database. This FTIR approach when developed with quantitative spectrum–activity relationships (QSAR) proved to be a valuable prospect for the determination of analytes in complex sample matrices.56 

Mass spectrometry is one of the most highly utilised detection techniques for illicit drugs because it affords information that can aid the structural elucidation of a molecule of interest, and when coupled with nuclear magnetic resonance spectroscopy is a powerful asset. There is a large range of mass spectrometry instrumentation available and this chapter will focus broadly on this topic and also introduce some interesting fieldwork examples later in the text. The technology allows determination of the molecular weight of a molecule or fragment of interest, and in its most advanced forms, either tandem or MSn, it has the capacity to accurately identify molecules. The use of spectral libraries is an integral part of mass spectral analysis although it is worth noting that data obtained with hard ionisation sources (such as electron ionisation) are generally more easily linked back to a database than data resulting from soft ionisation processes (such as chemical ionisation). The take-home message here is that mass spectrometry is a broad technology and the inherent ionisation processes lend the systems to be preferred by analysts for particular types of samples. It is important to note that mass spectrometry is a particularly sensitive detection system and is universal due to the large number of molecules that can be ionised. Often, having an instrument with such universal sample capacity leads to challenges with interferences, however with mass spectral data there is great capacity to retrospectively mine data to overcome this issue or use inherent technology to select for ions of interest, thus limiting the signal-to-noise issues. Due to this, and the fact that mass spectrometry lends itself to coupling with chromatographic technology, it is a mainstay in illicit drug detection within forensic science agencies.

An example of a standard methamphetamine mass spectrum is presented in Figure 1.6.76  It is generated with a hard ionisation source which leads to a higher proportion of fragment ions and a limited amount of (and often no) molecular ion.

Figure 1.6

Hard ionisation mass spectrum of methamphetamine. Reproduced from ref. 76 with permission from Elsevier, Copyright 2009.

Figure 1.6

Hard ionisation mass spectrum of methamphetamine. Reproduced from ref. 76 with permission from Elsevier, Copyright 2009.

Close modal

This mass spectrum is very different to that formed with a soft ionisation process such as electrospray ionisation (see Figure 1.7). In this case the pseudo-molecular ion [M+H]+ can be clearly observed at m/z 150.1279 together with two fragment ions at m/z 91.0543 and m/z 119.0857.

Figure 1.7

Soft ionisation mass spectrum of methamphetamine. Reproduced from ref. 77, https://mona.fiehnlab.ucdavis.edu/, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/.

Figure 1.7

Soft ionisation mass spectrum of methamphetamine. Reproduced from ref. 77, https://mona.fiehnlab.ucdavis.edu/, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/.

Close modal

With these two simple examples the complexity of mass spectral interpretation is clear and an analyst needs to fully understand the instrument they are working with and must report the data accordingly. Even when considering one instrument with one type of ionisation several parameters can affect the mass spectral output including temperature, flow rate of mobile phases, chemistry of the mobile phase including additives, pH and ionisation potentials to name a few. For this reason, mass spectrometers are routinely calibrated on a daily basis and calibration mixtures are often used in conjunction with real-time sample analysis to ensure spectral drift is accounted for. It is worth noting that inter-laboratory studies are important for analysts that allow them to work closely with other institutes. A great example of the importance of inter-laboratory studies and strategies for mass spectral investigations of biomarkers of illicit substances in waste water is presented by Hernández et al.78  The team specifically note that with liquid chromatography coupled to tandem mass spectrometry as the predominant technology in the forensic field, there is great importance in performing inter‐laboratory studies and a need for quality controls procedures within analytical laboratories. This type of work has great value for improving the quality of data generated from laboratories. In the case of waste water analysis, five exercises were performed between 2011 and 2015 and some excellent insights were gained that are applicable to general mass spectral determination of illicit substances. These types of activities lead to stepwise improvements in the life cycle of the studies and can aid optimisation of the analytical procedures in the laboratories involved, directly aiding the reliability of data sets.

Mass spectral data allows the analyst to gain a range of insights into the make-up of the molecule of interest including the molecular mass, fragment mass, number of double bonds or rings in the structure, whether an odd or even number of nitrogen atoms are present or whether particular atoms are present through isotopic ratios. One of the key components of any mass spectral analysis is the resolution of the data and where possible it is best to achieve a resolution of greater than 5 ppm for characterisation to be confirmed. Further fragmentation data can aid illicit drug profiling and is especially useful for monitoring routes of synthesis for illicit substances including information on the use of controlled precursors.

The power of this approach was neatly shown by Andrighetto when looking at unpurified samples from a benzaldehyde and nitroethane pathway to methamphetamine.79  The overall synthetic pathway can be observed in Figure 1.8.

Figure 1.8

Synthetic pathway from benzaldehyde and nitroethane to methamphetamine.

Figure 1.8

Synthetic pathway from benzaldehyde and nitroethane to methamphetamine.

Close modal

Andrighetto used high resolution mass spectrometry after multidimensional chromatographic separation to study the key components of this reaction. As unidimensional liquid chromatography–mass spectrometry is a more widely used technique for the analysis of drugs, it was used as a comparison for the two-dimensional profiling of synthetic routes to methamphetamine, to ensure the sensitivity was sufficient to be used by law enforcement agencies. A range of proposed components were identified by the researcher with the aid of mass spectrometry and these are shown in Figure 1.9.

Figure 1.9

Eleven components (structures A–K) identified by mass spectrometry from the synthetic pathway to methamphetamine shown in Figure 1.8.

Figure 1.9

Eleven components (structures A–K) identified by mass spectrometry from the synthetic pathway to methamphetamine shown in Figure 1.8.

Close modal

In order to understand the type of information that can be gained by monitoring each stage of the synthetic route the compounds were monitored at various stages throughout the synthesis which is highlighted in Figure 1.10.

Figure 1.10

Mass spectral information collected at each step of the synthetic pathway for methamphetamine production.

Figure 1.10

Mass spectral information collected at each step of the synthetic pathway for methamphetamine production.

Close modal

Interestingly, 13 compounds were identified in the final sample (m/z 60, m/z 134, m/z 136, m/z 164, m/z 166, m/z 178, m/z 180, m/z 194, m/z 196, m/z 206, m/z 256, m/z 284, m/z 307) that can also be detected in previous steps. Therefore, analysts can identify these compounds and it may act as an indicator that a particular synthetic pathway has been used. This type of data has the capacity to inform investigations allowing for the identification of samples from within a particular pathway.

The structural information gained through fragmentation profiles of molecules is a very powerful tool for forensic analysts and it is an approach that can lead to differentiation between compounds with similar chromatographic retention times and with the same molecular mass. A neat example of this was developed by Harris et al. who were able to use fragmentation data, including the abundance of the key ions coupled with ab initio calculations on potential ions of interest, for differentiating some key synthetic cannabinoids.80  The team looked at three synthetic cannabinoids (JWH-250, JWH-302 and JWH-201) that vary in the methoxy group at the ortho, meta and para positions; see Figure 1.11 for the structure of each and the respective mass spectra obtained. The work performed on synthetic cannabinoids was described as being of great interest to forensic analysts and complements the work done on other classes of drugs such as the ring-substituted amphetamines, highlighting the broad applicability of this type of information.

Figure 1.11

Mass spectra and chemical structures for three synthetic cannabinoids: JWH-250 (top), JWH-302 (middle) and JWH-201 (bottom). Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Figure 1.11

Mass spectra and chemical structures for three synthetic cannabinoids: JWH-250 (top), JWH-302 (middle) and JWH-201 (bottom). Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Close modal

The base peak in all the mass spectra appears at m/z 214 and the generation of this ion was proposed showing how the mass spectral interpretation is important in understanding the chemistry of the molecules at hand because the base peak is the most stable fragment ion. This is particularly important when the base peak is the molecular ion because the forensic researchers can gain direct insight into the stability of the molecule which can inform its likelihood to persist in the environment for example. In addition to the retention time differences, the molecules were shown to contain positional differences of the methoxy moieties and the fragmentation pathways were shown to be dependent upon this aspect of the chemistry. For example, it was shown that the formation of the tropylium ion, m/z 91, could be achieved by two pathways; one of which is dependent on the ortho, meta and para positions of the methoxy and is described in Figure 1.12. The use of a statistical approach complemented this experimental work showing that it is robust and an excellent way to utilise mass spectral fragmentation data to characterise molecules that have very similar retention times and molecular masses.

Figure 1.12

Formation of the tropylium ion, m/z 91. Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Figure 1.12

Formation of the tropylium ion, m/z 91. Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Close modal

The approach is qualitatively aligned with other work done individually on these molecules and sets a platform for novel synthetic cannabinoids that may be developed in the future. This is of particular interest to those working in synthetic cannabinoid detection because of the large number of known compounds (above 400) that is proving challenging to analysts.

Adding dimensions to the mass spectral information (tandem MS or MSn) is another way in which important distinguishing fragmentation pathways can be identified to elucidate a molecule of interest. This type of approach for example has proven to be important for the determination of the structures of cathinones. Recent work driven by Davidson demonstrates that the approach can lead to pathway rationalisation and when coupled with high mass accuracy and isotope labelling the mass spectral information for forensic investigations is very powerful.81  For example, the production of the tropylium ion in this case was shown to be produced by one particular fragmentation pathway and a detailed model of the progress of this is shown in Figure 1.13.

Figure 1.13

Production of the tropylium ion, m/z 91, produced by one fragmentation pathway. Reproduced from ref. 81 with permission from Elsevier, Copyright 2020.

Figure 1.13

Production of the tropylium ion, m/z 91, produced by one fragmentation pathway. Reproduced from ref. 81 with permission from Elsevier, Copyright 2020.

Close modal

Isotope ratio mass spectrometry is commonly used in forensic applications including illicit drug detection and brings comprehensive discrimination power.82  The technology yields information that can lead to regionally specific determination and gives a guide to authenticity and origin of the sample of interest. It has been used to study the ephedra plant, ephedrine samples and their subsequent use in methamphetamine production based on the carbon and nitrogen stable isotopes.83  In this case the δ15N values generated from the plants aligned directly with the extracted ephedrine and the group was able to show that this carried through to enable discrimination of naturally extracted and synthetically produced ephedrine. The precursor was then able to be linked to the finished product methamphetamine via the δ13C values, including being able to determine that the product was formed through the phenyl 2-propanone (P2P) pathway. This approach was used to study methamphetamine seizure samples from the Australian Border Force with the δ2H and δ13C values leading to clear discrimination between semi-synthetic samples, those that were produced with ephedrine as precursor in a fermentation with sugar and benzaldehyde, and from those of synthetic–natural origin produced from ephedrine extracted from the ephedra plant.84  The differentiation can be seen in Figure 1.14 highlighting the discrimination power of this approach.

Figure 1.14

Differentiation between semi-synthetic and synthetic or natural origin for methamphetamine seizure samples from the Australian Border Force. Reproduced from ref. 84 with permission from Elsevier, Copyright 2015.

Figure 1.14

Differentiation between semi-synthetic and synthetic or natural origin for methamphetamine seizure samples from the Australian Border Force. Reproduced from ref. 84 with permission from Elsevier, Copyright 2015.

Close modal

From a chemical intelligence perspective this aspect is important because despite what other chemical information is available for a seizure sample, such as excipients and by-products, the authors state the light element stable isotope ratios of carbon, hydrogen and nitrogen are available in every molecule that makes the approach universal. The δ15N and δ13C values have been successfully used to determine the growth conditions of cannabis and have been well studied in a range of jurisdictions.85  The original work in this area by West et al.86  is outlined in an excellent review82  which brings together the information graphically to highlight this data as outlined in Figure 1.15.

Figure 1.15

Differentiation of cannabis plants based on their growing conditions. Reproduced from ref. 82 with permission from Elsevier, Copyright 2019.

Figure 1.15

Differentiation of cannabis plants based on their growing conditions. Reproduced from ref. 82 with permission from Elsevier, Copyright 2019.

Close modal

This type of detection approach is complementary to the botanical analysis and is crucial for strengthening the prosecution case in large-scale, cross international border seizures of cannabis samples. We have seen here that mass spectral data is a very rich source of information for the forensic analysts and when coupled with nuclear magnetic resonance spectroscopy it is an advanced structural elucidation tool set.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for structural characterisation because it does not need a standard for comparison. To date the technique is not used for routine drug testing, limited mostly by the cost of the instrumentation and the reduced resolution of benchtop devices, although it should be noted that improvements are continuing in this aspect. The technology is excellent for the research analyst when developing new chromatographic and detection approaches for novel compounds of interest. Again, when thinking of the complexity associated with the range of synthetic cannabinoid analogues and how quickly new products have entered the market in recent years, nuclear magnetic resonance has been invaluable. A detailed report on the use of nuclear magnetic resonance spectroscopy and how it is applied to the identification of synthetic drugs of abuse has been prepared by Souza et al. and this chapter will focus on recent advancements in the field.87  Importantly, the technique has had a long history in informing illicit drug characterisation including understanding the excipients present.88 

The technique has been applied to a wide variety of drug samples, has enabled detection of compounds in complex matrices and has stripped out key chemical information from high purity samples. When considering high purity samples, it is important to note that the excipients and by-products are often in low concentrations and as such batch-to-batch sample comparisons are more difficult. High purity cocaine seizure samples were analysed by Benidito et al. with the aid of 1H quantitative NMR which showed excellent correlation to the standard gas chromatography coupled to a flame ionisation detector approaches used routinely by forensic analysts.89  The analysts in this case were able to interrogate the variety of cocaine cultivar used for illicit drug manufacture which is important because most of the cocaine alkaloid is extracted globally from one of two cultivars. The extraction process for cocaine varies considerably and as such has the capacity to generate illicit drug samples that contain a mixture of several alkaloids which Benidito identified as benzoylmethylecgonine (cocaine), benzoylecgonine, cis- and trans-cinnamoylcocaine, benzoylecgonine, 3,4,5-trimethoxycocaine, tropacocaine and truxilines amongst others; additionally, the team showed that 1H NMR was useful for cocaine analysis. An example of the type of data generated is shown in Figure 1.16.

Figure 1.16

1H NMR spectra of a cocaine hydrochloride sample obtained at 600 MHz in D2O. The inset shows signals at 5.57 ppm (A), 5.98 ppm (B) and 6.54 ppm (C), used in quantification of cocaine, cis-cinnamoylcocaine and trans-cinnamoylcocaine, respectively. Reproduced from ref. 89 with permission from the Royal Society of Chemistry, Copyright 2018.

Figure 1.16

1H NMR spectra of a cocaine hydrochloride sample obtained at 600 MHz in D2O. The inset shows signals at 5.57 ppm (A), 5.98 ppm (B) and 6.54 ppm (C), used in quantification of cocaine, cis-cinnamoylcocaine and trans-cinnamoylcocaine, respectively. Reproduced from ref. 89 with permission from the Royal Society of Chemistry, Copyright 2018.

Close modal

Nagi et al. note that the gold standard for illicit pill testing is mass spectral detection after HPLC and GC separations.90  In 2019, they set out to demonstrate that 1H NMR can be used quantitatively for the determination and subsequent characterisation of novel synthetic compounds. The team was interested in using this type of approach to understand the chemical complexity within the pills obtained in seizures from UK night clubs. This included a focus on the primary drug MDMA and other novel psychoactive substances methylone and trifluoromethylpiperazine (TFMPP), amongst others found in these pills. The group was able to use 1H NMR spectra to identify key differences in the ratio of these compounds in drug samples (see Figure 1.17).

Figure 1.17

Reference spectra of 1H NMR (in D2O) between 5.70 and 7.80 ppm of (A) sample HN157 and (B) sample HN153T, revealing significant differences in the ratio of each component. Reproduced from ref. 90 with permission from the Royal Society of Chemistry, Copyright 2019.

Figure 1.17

Reference spectra of 1H NMR (in D2O) between 5.70 and 7.80 ppm of (A) sample HN157 and (B) sample HN153T, revealing significant differences in the ratio of each component. Reproduced from ref. 90 with permission from the Royal Society of Chemistry, Copyright 2019.

Close modal

This type of approach is a key example where analytical chemistry is at the nexus of both forensic drug detection and public health. The chemical information gained from these types of studies enables prosecutors to obtain the evidence they need while also aiding health systems, as well as gaining an understanding of what other novel substances may appear in the illicit drug market allowing preparedness for medical processes and treatment of overdose cases.

The majority of nuclear magnetic resonance spectroscopic characterisation is performed with a focus on the protons and carbons within the molecule however some targeted approaches on other elements has proven effective for forensic analysis. Many synthetic cannabinoids for example contain fluorine atoms and 19F NMR has been used by several groups to selectively identify these molecules including the determination of third-generation synthetic cannabinoids.91  This was performed in conjunction with ultra-performance LC-MS (UPLC-MS) and the approach has proven to be robust and quantitative. Importantly 19F nuclear magnetic resonance spectroscopy affords excellent selectivity with synthetic cannabinoid samples due to the sample matrix. These drugs are typically sprayed onto an herbal substrate and smoked by the user, and as such extraction of the compound from this herbal mixture is required. Nagi and co-workers showed that after extraction with methanol no interference from the molecules co-extracted from the substrate were observed under 19F NMR highlighting the selective nature of the approach.

This approach was taken a step further by Burns et al. who used solid-state 13C and 19F NMR (SSNMR) spectroscopy providing a non-destructive, highly selective approach that led to the detection of the synthetic cannabinoids directly on the substrate.92  Importantly, well-resolved 13C spectra were obtained by this approach which were complemented by 19F spectra enabling structure elucidation. The approach described has potential for related applications such as the direct detection of pesticides on plants.

The correlation of 13C chemical shifts in relation to molecular structure means that these signals can be used to distinguish the class of synthetic cannabinoid structure. Solid state NMR is not a technique routinely used in forensic analysis for high volume samples as minimal sample preparation is needed in these cases; however SSNMR may be a simpler approach than those requiring extractions such as chromatographic and mass spectral detection. One thing also to consider is that this approach allows for the sample integrity to be maintained which we have previously described as being important for forensic sampling hierarchical planning and it is anticipated the appeal of this approach will grow, even if the focus is towards the identification of new compounds on the illicit drug market.

This chapter describes the key considerations for the determination of illicit drugs when using a range of analytical approaches. Here we have also defined the key considerations for dealing with a range of sample matrices however it is important to note that there are some unique challenges that currently confront analysts.

Hair analysis can be used to gain an understanding of previous drug use (up to 12 weeks) by an individual however it has had limited uptake for a number of reasons including the prohibitive cost of the analysis. Within the Australian context it has not been widely used because a library of hair samples has not been fully established. In this process a small hair sample is assessed (typically 200 mg) and is taken from close to the scalp which has led to drug users often shaving their heads regularly to avoid detection. There has been some uptake of routine hair analysis for workplace drug testing and it is viewed as a good sample matrix because it is much harder to manipulate than a urine or saliva sample. Hair analysis is complementary to other testing regimens and it is not often used as a stand-alone procedure due to the concerns addressed above. This approach is most commonly used to inform workplace safety therefore illicit substances such as amphetamines, cannabis, opiates, benzodiazepines and cocaine are typically the target analytes. A key study in this area was presented by Gryczynski et al. who set out to determine how effective the approach is for measuring drug use within moderate-risk level users based on the internationally-validated Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST).93  The group found that, while useful with this type of drug user, limitations were identified with under identification of low-frequency users, noting that low detection cut-offs should be used and hair testing should be done in conjunction with self-reporting processes.

A recently acknowledged public health issue has arisen due to the turnover of properties (both rental and purchased) that had previously been used for methamphetamine synthesis and use. It has been shown that significant amounts of contamination can occur leading to ingestion and adverse effects for the residents. Kuhn et al. define this clearly in their recent research and highlight that currently there is no international standard protocol for measuring the contamination levels.94  Further the team has shown a substrate effect is at play and that different building materials influence the ability to recover the methamphetamine for analytical determination and that work is needed to improve recovery methods. This is one dynamic area in illicit drug analysis because to date there are limited guidelines on how to approach this problem. This is a key area where there is scope for forensic analysts to work closely with health authorities to develop a synergistic approach to the problem. In Australia, in 2011, the National Clandestine Drug Laboratory Remediation Guidelines were prepared showing that the limit for methamphetamine in a residence must not exceed 0.5 µg per 100 cm2 which is one third of that recommended in New Zealand. Kuhn prepared a detailed analysis comparing Australia, New Zealand and the individual states of the USA showing that the recommended level varied from as low as 0.05 µg per 100 cm2 in Arkansas to 4 µg per 100 cm2 for limited use areas in Colorado, highlighting the opportunity for a consistent approach to this problem. From an analyst's perspective the development of a robust and optimal sampling protocol is required noting that the resulting analysis is typically performed with the aid of gas chromatography coupled with mass spectrometry (GC-MS) and liquid chromatography coupled with mass spectrometry (LC-MS).95 

A third challenge for forensic analysts is the capacity to determine illicit drugs in the environment. Here we have previously mentioned how this aids policing through the monitoring of waterways, soil and the air although it is important to note that illicit drugs can have devastating impact on organisms in the environment.96  Illicit drugs and metabolite bioaccumulation have been identified in fish and it is noted as a problem due to urban development that is having a direct impact on protected areas.97  The level found in fish has been shown to be high enough to have an adverse impact on marine ecosystems and has the potential to move through to the human food chain.98  Even National Geographic have reported some rivers presenting such high levels of illicit drugs that critically endangered eels were so hyped up on cocaine that they had limited capacity to make the long trip to locations upstream for reproduction.99  Further, the challenge facing analysts in this area is that the metabolites of the illicit substances also need to be determined (especially in waste water) and with an ever increasing number of illicit substances coming onto the market tracking these is a great challenge for the modern analyst.

There has been a great body of work for the determination of traditional illicit substances and with human nature in question this will remain a challenge for analytical chemists into the future. Importantly, researchers in this area need to work closely with policing, health and policy advocates in order to fully realise the potential to deal with this problem. The research area is dynamic and analysts need to be flexible in their approach although with such a strong platform already developed a real impact is possible.

1.
Cicero
 
T. J.
Ellis
 
M. S.
Dialogues Clin. Neurosci.
2017
, vol. 
19
 (pg. 
259
-
269
)
2.
Gossop
 
M.
Keaney
 
F.
Sharma
 
P.
Jackson
 
M.
Eur. Addict. Res.
2005
, vol. 
11
 (pg. 
76
-
82
)
3.
J.
Stafford
and
C.
Breen
,
Australian Drug Trends 2016: Findings from the Illicit Drug Reporting System (IDRS)
, ed. National Drug and Alcohol Research Centre,
UNSW Australia
,
Sydney
,
2017
,
vol. 163
4.
Illicit drug data report
, ed. Australian Crime Commission,
Australian Crime Commission
,
Sydney
,
2003
5.
United Nations Office on Drugs and Crime
, Laboratory, Scientific Section and Drugs Working Group of the European Network of Forensic Science Institutes,
Guidelines on Representative Drug Sampling
,
United Nations
,
2009
6.
M. G.
Bovens
,
J.
Nagy
,
T.
Csesztregi
,
L.
Dujourdy
and
A.
Franc
,
Guidelines on Sampling of Illicit Drugs for Quantitative Analysis
,
2014
7.
O.
Drummer
and
D.
Gerostamoulos
,
Forensic Drug Analysis
,
2013
, pp. 2–9
8.
Gao
 
C.
Ogeil
 
R.
Drug Alcohol Rev.
2018
, vol. 
37
 (pg. 
819
-
820
)
9.
Cappiello
 
A.
Famiglini
 
G.
Palma
 
P.
Pierini
 
E.
Termopoli
 
V.
Trufelli
 
H.
Anal. Chem.
2008
, vol. 
80
 (pg. 
9343
-
9348
)
10.
Guilbault
 
G.
Hjelm
 
N.
Pure Appl. Chem.
1989
, vol. 
61
 (pg. 
1657
-
1664
)
11.
Matuszewski
 
B. K.
Constanzer
 
M. L.
Chavez-Eng
 
C. M.
Anal. Chem.
2003
, vol. 
75
 (pg. 
3019
-
3030
)
12.
C. H.
Daniel
,
Quantitative Chemical Analysis
,
W. H. Freeman and Company
,
New York
, 8th edn,
2010
13.
Kappi
 
F. A.
Tsogas
 
G. Z.
Christodouleas
 
D. C.
Giokas
 
D. L.
Sens. Actuators, B
2017
, vol. 
253
 (pg. 
860
-
867
)
14.
Zhou
 
W.
Yang
 
S.
Wang
 
P.
Bioanalysis
2017
, vol. 
9
 (pg. 
1839
-
1844
)
15.
Choi
 
B. K.
Gusev
 
A. I.
Hercules
 
D. M.
Anal. Chem.
1999
, vol. 
71
 (pg. 
4107
-
4110
)
16.
Stahnke
 
H.
Reemtsma
 
T.
Alder
 
L.
Anal. Chem.
2009
, vol. 
81
 (pg. 
2185
-
2192
)
17.
Rossmann
 
J.
Renner
 
L. D.
Oertel
 
R.
El-Armouche
 
A.
J. Chromatogr. A
2018
, vol. 
1535
 (pg. 
80
-
87
)
18.
Saar
 
E.
Gerostamoulos
 
D.
Drummer
 
O. H.
Beyer
 
J.
Anal. Bioanal. Chem.
2009
, vol. 
393
 (pg. 
727
-
734
)
19.
O'Neal
 
C. L.
Crouch
 
D. J.
Fatah
 
A. A.
Forensic Sci. Int.
2000
, vol. 
109
 (pg. 
189
-
201
)
20.
United Nations Office on Drugs and Crime
,
Guidelines on Representative Drug Sampling
,
2006
21.
Harper
 
L.
Powell
 
J.
Pijl
 
E. M.
Harm Reduct. J.
2017
, vol. 
14
 pg. 
52
 
22.
McCrae
 
K.
Tobias
 
S.
Tupper
 
K.
Arredondo
 
J.
Henry
 
B.
Mema
 
S.
Wood
 
E.
Ti
 
L.
Drug Alcohol Depend.
2019
, vol. 
205
 pg. 
107589
 
23.
Bijlsma
 
L.
Celma
 
A.
Castiglioni
 
S.
Salgueiro-González
 
N.
Bou-Iserte
 
L.
Baz-Lomba
 
J. A.
Reid
 
M. J.
Dias
 
M. J.
Lopes
 
A.
Matias
 
J.
Pastor-Alcañiz
 
L.
Radonić
 
J.
Turk Sekulic
 
M.
Shine
 
T.
van Nuijs
 
A. L. N.
Hernandez
 
F.
Zuccato
 
E.
Sci. Total Environ.
2020
, vol. 
725
 pg. 
138376
 
24.
Kinyua
 
J.
Negreira
 
N.
Miserez
 
B.
Causanilles
 
A.
Emke
 
E.
Gremeaux
 
L.
de Voogt
 
P.
Ramsey
 
J.
Covaci
 
A.
van Nuijs
 
A. L. N.
Sci. Total Environ.
2016
, vol. 
573
 (pg. 
1527
-
1535
)
25.
Mackuľak
 
T.
Brandeburová
 
P.
Grenčíková
 
A.
Bodík
 
I.
Staňová
 
A. V.
Golovko
 
O.
Koba
 
O.
Mackuľaková
 
M.
Špalková
 
V.
Gál
 
M.
Grabic
 
R.
Sci. Total Environ.
2019
, vol. 
659
 (pg. 
326
-
334
)
26.
Dent
 
C. W.
Galaif
 
J.
Sussman
 
S.
Stacy
 
A. W.
Burton
 
D.
Flay
 
B. R.
Am. J. Public Health
1992
, vol. 
82
 pg. 
124
 
27.
Bijlsma
 
L.
Serrano
 
R.
Ferrer
 
C.
Tormos
 
I.
Hernández
 
F.
Sci. Total Environ.
2014
, vol. 
487
 (pg. 
703
-
709
)
28.
Jiang
 
J.-J.
Lee
 
C.-L.
Fang
 
M.-D.
Tu
 
B.-W.
Liang
 
Y.-J.
Environ. Sci. Technol.
2015
, vol. 
49
 (pg. 
792
-
799
)
29.
Almeida
 
S. P.
Araujo Silva
 
M. T.
Subst. Use Misuse
2005
, vol. 
40
 (pg. 
395
-
404
)
30.
Forsyth
 
A. J. M.
Barnard
 
M.
McKeganey
 
N. P.
Addiction
1997
, vol. 
92
 (pg. 
1317
-
1325
)
31.
Pedersen
 
W.
Skrondal
 
A.
Addiction
1999
, vol. 
94
 (pg. 
1695
-
1706
)
32.
Mackuľak
 
T.
Škubák
 
J.
Grabic
 
R.
Ryba
 
J.
Birošová
 
L.
Fedorova
 
G.
Špalková
 
V.
Bodík
 
I.
Sci. Total Environ.
2014
, vol. 
494–495
 (pg. 
158
-
165
)
33.
Lai
 
F. Y.
Thai
 
P. K.
O'Brien
 
J.
Gartner
 
C.
Bruno
 
R.
Kele
 
B.
Ort
 
C.
Prichard
 
J.
Kirkbride
 
P.
Hall
 
W.
Carter
 
S.
Mueller
 
J. F.
Drug Alcohol Rev.
2013
, vol. 
32
 (pg. 
594
-
602
)
34.
Martin
 
G.
Clarke
 
M.
Pearce
 
C.
J. Am. Acad. Child Adolesc. Psychiatry
1993
, vol. 
32
 (pg. 
530
-
535
)
35.
Mackuľak
 
T.
Grabic
 
R.
Gál
 
M.
Gál
 
M.
Birošová
 
L.
Bodík
 
I.
Environ. Toxicol. Pharmacol.
2015
, vol. 
40
 (pg. 
1015
-
1020
)
36.
Al-Dirbashi
 
O.
Kuroda
 
N.
Nakashima
 
K.
Menichini
 
F.
Noda
 
S.
Minemoto
 
M.
Analyst
1998
, vol. 
123
 (pg. 
2333
-
2337
)
37.
Di Rago
 
M.
Chu
 
M.
Rodda
 
L. N.
Jenkins
 
E.
Kotsos
 
A.
Gerostamoulos
 
D.
Anal. Bioanal. Chem.
2016
, vol. 
408
 (pg. 
3737
-
3749
)
38.
Rasmussen
 
S.
Cole
 
R.
Spiehler
 
V.
J. Anal. Toxicol.
1989
, vol. 
13
 (pg. 
263
-
267
)
39.
Huestis
 
M. A.
Cone
 
E. J.
Ann. N. Y. Acad. Sci.
2007
, vol. 
1098
 (pg. 
104
-
121
)
40.
Suwannachom
 
N.
Thananchai
 
T.
Junkuy
 
A.
O'Brien
 
T. E.
Sribanditmongkol
 
P.
Forensic Sci. Int.
2015
, vol. 
254
 (pg. 
80
-
86
)
41.
Lin
 
D.-L.
Yin
 
R.-M.
Liu
 
H.-C.
Wang
 
C.-Y.
Liu
 
R. H.
J. Anal. Toxicol.
2004
, vol. 
28
 (pg. 
411
-
417
)
42.
Groeneveld
 
G.
de Puit
 
M.
Bleay
 
S.
Bradshaw
 
R.
Francese
 
S.
Sci. Rep.
2015
, vol. 
5
 pg. 
11716
 
43.
Fay
 
J.
Fogerson
 
R.
Schoendorfer
 
D.
Niedbala
 
R. S.
Spiehler
 
V.
J. Anal. Toxicol.
1996
, vol. 
20
 (pg. 
398
-
403
)
44.
Smith
 
F. P.
Forensic Sci. Int.
1981
, vol. 
17
 (pg. 
225
-
228
)
45.
Magni
 
P. A.
Pacini
 
T.
Pazzi
 
M.
Vincenti
 
M.
Dadour
 
I. R.
Forensic Sci. Int.
2014
, vol. 
241
 (pg. 
96
-
101
)
46.
Suzuki
 
O.
Hattori
 
H.
Asano
 
M.
Forensic Sci. Int.
1984
, vol. 
24
 (pg. 
9
-
16
)
47.
Shabir
 
G. A.
Indian J. Pharm. Sci.
2011
, vol. 
73
 (pg. 
430
-
435
)
48.
Andrighetto
 
L. M.
Stevenson
 
P. G.
Pearson
 
J. R.
Henderson
 
L. C.
Conlan
 
X. A.
Forensic Sci. Int.
2014
, vol. 
244
 (pg. 
302
-
305
)
49.
Cheng
 
W.-C.
Mok
 
V. K.-K.
Chan
 
K.-K.
Li
 
A. F.-M.
Forensic Sci. Int.
2007
, vol. 
166
 (pg. 
1
-
7
)
50.
Zhang
 
T.
Chen
 
X.
Yang
 
R.
Xu
 
Y.
Forensic Sci. Int.
2015
, vol. 
248
 (pg. 
10
-
14
)
51.
Concheiro
 
M.
Simões
 
S. M. D. S. S.
Quintela
 
Ó.
de Castro
 
A.
Dias
 
M. J. R.
Cruz
 
A.
López-Rivadulla
 
M.
Forensic Sci. Int.
2007
, vol. 
171
 (pg. 
44
-
51
)
52.
Hayakawa
 
K.
Imaizumi
 
N.
Ishikura
 
H.
Minogawa
 
E.
Takayama
 
N.
Kobayashi
 
H.
Miyazaki
 
M.
J. Chromatogr. A
1990
, vol. 
515
 (pg. 
459
-
466
)
53.
McGeehan
 
J.
Dennany
 
L.
Forensic Sci. Int.
2016
, vol. 
264
 (pg. 
1
-
6
)
54.
Katagi
 
M.
Nishikawa
 
M.
Tatsuno
 
M.
Miyazawa
 
T.
Tsuchihashi
 
H.
Suzuki
 
A.
Shirota
 
O.
Eisei Kagaku
1998
, vol. 
44
 (pg. 
107
-
115
)
55.
Cody
 
J. T.
Schwarzhoff
 
R.
J. Anal. Toxicol.
1993
, vol. 
17
 (pg. 
26
-
30
)
56.
Praisler
 
M.
Van Bocxlaer
 
J.
De Leenheer
 
A.
Massart
 
D. L.
J. Chromatogr. A
2002
, vol. 
962
 (pg. 
161
-
173
)
57.
Kuwayama
 
K.
Tsujikawa
 
K.
Miyaguchi
 
H.
Kanamori
 
T.
Iwata
 
Y.
Inoue
 
H.
Saitoh
 
S.
Kishi
 
T.
Forensic Sci. Int.
2006
, vol. 
160
 (pg. 
44
-
52
)
58.
Miki
 
A.
Keller
 
T.
Regenscheit
 
P.
Dirnhofer
 
R.
Tatsuno
 
M.
Katagi
 
M.
Nishikawa
 
M.
Tsuchihashi
 
H.
J. Chromatogr. B: Biomed. Sci. Appl.
1997
, vol. 
692
 (pg. 
319
-
328
)
59.
Toske
 
S. G.
Morello
 
D. R.
Berger
 
J. M.
Vazquez
 
E. R.
Forensic Sci. Int.
2014
, vol. 
234
 (pg. 
1
-
6
)
60.
Kimura
 
H.
Mukaida
 
M.
Mori
 
A.
J. Anal. Toxicol.
1999
, vol. 
23
 (pg. 
577
-
580
)
61.
Guinan
 
T.
Della Vedova
 
C.
Kobus
 
H.
Voelcker
 
N. H.
Chem. Commun.
2015
, vol. 
51
 (pg. 
6088
-
6091
)
62.
Hays
 
P.
J. Forensic Sci.
2005
, vol. 
50
 (pg. 
1342
-
1360
)
63.
Armellin
 
S.
Brenna
 
E.
Frigoli
 
S.
Fronza
 
G.
Fuganti
 
C.
Mussida
 
D.
Anal. Chem.
2006
, vol. 
78
 (pg. 
3113
-
3117
)
64.
Iwamuro
 
Y.
Iio-Ishimaru
 
R.
Chinaka
 
S.
Takayama
 
N.
Kodama
 
S.
Hayakawa
 
K.
Forensic Toxicol.
2010
, vol. 
28
 (pg. 
19
-
24
)
65.
Rezazadeh
 
M.
Yamini
 
Y.
Seidi
 
S.
J. Chromatogr. A
2015
, vol. 
1396
 (pg. 
1
-
6
)
66.
Smith
 
J. P.
Martin
 
A.
Sammons
 
D. L.
Striley
 
C.
Biagini
 
R.
Quinn
 
J.
Cope
 
R.
Snawder
 
J. E.
Toxicol. Mech. Methods
2009
, vol. 
19
 (pg. 
416
-
421
)
67.
Mohsen
 
Y.
Gharbi
 
N.
Lenouvel
 
A.
Guignard
 
C.
Procedia Eng.
2014
, vol. 
87
 (pg. 
536
-
539
)
68.
Choodum
 
A.
Parabun
 
K.
Klawach
 
N.
Daeid
 
N. N.
Kanatharana
 
P.
Wongniramaikul
 
W.
Forensic Sci. Int.
2014
, vol. 
235
 (pg. 
8
-
13
)
69.
He
 
X.
Wang
 
J.
You
 
X.
Niu
 
F.
Fan
 
L.
Lv
 
Y.
Spectrochim. Acta, Part A
2020
, vol. 
241
 pg. 
118665
 
70.
Hughes
 
J.
Ayoko
 
G.
Collett
 
S.
Golding
 
G.
PLoS One
2013
, vol. 
8
 pg. 
e69609
 
71.
Ricci
 
C.
Chan
 
K. L. A.
Kazarian
 
S. G.
Appl. Spectrosc.
2006
, vol. 
60
 (pg. 
1013
-
1021
)
72.
Quayle
 
K.
Clemens
 
G.
Sorribes
 
T. G.
Kinvig
 
H. M.
Stevenson
 
P. G.
Conlan
 
X. A.
Baker
 
M. J.
Forensic Sci. Int.
2016
, vol. 
266
 (pg. 
549
-
554
)
73.
Chan
 
K. L. A.
Kazarian
 
S. G.
Analyst
2006
, vol. 
131
 (pg. 
126
-
131
)
74.
Lanzarotta
 
A.
Sensors
2016
, vol. 
16
 pg. 
278
 
75.
Johnston
 
A.
J. Forensic Res.
2018
, vol. 
9
 (pg. 
418
-
423
)
76.
Awad
 
T.
Belal
 
T.
DeRuiter
 
J.
Kramer
 
K.
Clark
 
C. R.
Forensic Sci. Int.
2009
, vol. 
185
 (pg. 
67
-
77
)
77.
Methamphetamine: Soft Ionisation
,
MassBank of North America
,
MassBank of North America UC Davis
,
2020
78.
Hernández
 
F.
Castiglioni
 
S.
Covaci
 
A.
de Voogt
 
P.
Emke
 
E.
Kasprzyk-Hordern
 
B.
Ort
 
C.
Reid
 
M.
Sancho
 
J. V.
Thomas
 
K. V.
van Nuijs
 
A. L. N.
Zuccato
 
E.
Bijlsma
 
L.
Mass Spectrom. Rev.
2018
, vol. 
37
 (pg. 
258
-
280
)
79.
L. M.
Andrighetto
,
Platform technology towards the chemical fingerprinting methamphetamine from ephedrine pathways
,
Deakin University
,
2016
80.
Harris
 
D. N.
Hokanson
 
S.
Miller
 
V.
Jackson
 
G. P.
Int. J. Mass Spectrom.
2014
, vol. 
368
 (pg. 
23
-
29
)
81.
Tyler Davidson
 
J.
Piacentino
 
E. L.
Sasiene
 
Z. J.
Abiedalla
 
Y.
DeRuiter
 
J.
Clark
 
C. R.
Berden
 
G.
Oomens
 
J.
Ryzhov
 
V.
Jackson
 
G. P.
Forensic Chem.
2020
, vol. 
19
 pg. 
100245
 
82.
Matos
 
M. P. V.
Jackson
 
G. P.
Forensic Chem.
2019
, vol. 
13
 pg. 
100154
 
83.
Liu
 
C.
Liu
 
P.
Jia
 
W.
Fan
 
Y.
J. Forensic Sci.
2018
, vol. 
63
 (pg. 
1053
-
1058
)
84.
Collins
 
M.
Salouros
 
H.
Sci. Justice
2015
, vol. 
55
 (pg. 
2
-
9
)
85.
Shibuya
 
E. K.
Souza Sarkis
 
J. E.
Neto
 
O. N.
Moreira
 
M. Z.
Victoria
 
R. L.
Forensic Sci. Int.
2006
, vol. 
160
 (pg. 
35
-
43
)
86.
West
 
J. B.
Hurley
 
J. M.
Ehleringer
 
J. R.
J. Forensic Sci.
2009
, vol. 
54
 (pg. 
84
-
89
)
87.
L. F.
Souza
and
L. M.
Lião
, in
Forensic Analytical Methods
,
The Royal Society of Chemistry
,
2019
, pp. 79–114
88.
LeBelle
 
M. J.
Dawson
 
B.
Lauriault
 
G.
Savard
 
C.
Analyst
1991
, vol. 
116
 (pg. 
1063
-
1065
)
89.
Benedito
 
L. E. C.
Maldaner
 
A. O.
Oliveira
 
A. L.
Anal. Methods
2018
, vol. 
10
 (pg. 
489
-
495
)
90.
Naqi
 
H. A.
Husbands
 
S. M.
Blagbrough
 
I. S.
Anal. Methods
2019
, vol. 
11
 (pg. 
4795
-
4807
)
91.
Naqi
 
H. A.
Woodman
 
T. J.
Husbands
 
S. M.
Blagbrough
 
I. S.
Anal. Methods
2019
, vol. 
11
 (pg. 
3090
-
3100
)
92.
Burns
 
N. K.
Theakstone
 
A. G.
Zhu
 
H.
O'Dell
 
L. A.
Pearson
 
J. R.
Ashton
 
T. D.
Pfeffer
 
F. M.
Conlan
 
X. A.
Anal. Chim. Acta
2020
, vol. 
1104
 (pg. 
105
-
109
)
93.
Gryczynski
 
J.
Schwartz
 
R. P.
Mitchell
 
S. G.
O'Grady
 
K. E.
Ondersma
 
S. J.
Drug Alcohol Depend.
2014
, vol. 
141
 (pg. 
44
-
50
)
94.
Kuhn
 
E. J.
Walker
 
G. S.
Whiley
 
H.
Wright
 
J.
Ross
 
K. E.
Int. J. Environ. Res. Public Health
2019
, vol. 
16
 pg. 
4676
 
95.
Martyny
 
J. W.
Arbuckle
 
S. L.
McCammon
 
C. S.
Esswein
 
E. J.
Erb
 
N.
Van Dyke
 
M.
J. Chem. Health Saf.
2007
, vol. 
14
 (pg. 
40
-
52
)
96.
Zuccato
 
E.
Castiglioni
 
S.
Philos. Trans. R. Soc., A
2009
, vol. 
367
 (pg. 
3965
-
3978
)
97.
Ondarza
 
P. M.
Haddad
 
S. P.
Avigliano
 
E.
Miglioranza
 
K. S. B.
Brooks
 
B. W.
Sci. Total Environ.
2019
, vol. 
649
 (pg. 
1029
-
1037
)
98.
S.
Gaw
,
K.
Thomas
and
T. H.
Hutchinson
, in
Pharmaceuticals in the Environment
,
The Royal Society of Chemistry
,
2016
, pp. 70–91
99.
Capaldo
 
A.
Gay
 
F.
Lepretti
 
M.
Paolella
 
G.
Martucciello
 
S.
Lionetti
 
L.
Caputo
 
I.
Laforgia
 
V.
Sci. Total Environ.
2018
, vol. 
640–641
 (pg. 
862
-
873
)

Figures & Tables

Figure 1.1

Standard addition curve where the data is extrapolated to the x axis in order to determine concentration of unknown. This standard additions calibration method was performed on calibrant-loaded paper-based devices: (a) iron(iii) thiocyanate assay, (b) proteins–bromophenol blue assay. Reproduced from ref. 13 with permission from Elsevier, Copyright 2017.

Figure 1.1

Standard addition curve where the data is extrapolated to the x axis in order to determine concentration of unknown. This standard additions calibration method was performed on calibrant-loaded paper-based devices: (a) iron(iii) thiocyanate assay, (b) proteins–bromophenol blue assay. Reproduced from ref. 13 with permission from Elsevier, Copyright 2017.

Close modal
Figure 1.2

FTIR spectrum of (A) heroin hydrochloride, (B) methamphetamine hydrochloride, and (C) ketamine hydrochloride. Reproduced from ref. 69 with permission from Elsevier, Copyright 2020.

Figure 1.2

FTIR spectrum of (A) heroin hydrochloride, (B) methamphetamine hydrochloride, and (C) ketamine hydrochloride. Reproduced from ref. 69 with permission from Elsevier, Copyright 2020.

Close modal
Figure 1.3

Typical attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FTIR) spectra of methamphetamine: (a) high concentration (78.6%), (b) low concentration (10.3% cut with MSM (methylsulphonylmethane)). Reproduced from ref. 70, https://doi.org/10.1371/journal.pone.0069609, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Figure 1.3

Typical attenuated total reflectance (ATR) Fourier transform infrared spectroscopy (FTIR) spectra of methamphetamine: (a) high concentration (78.6%), (b) low concentration (10.3% cut with MSM (methylsulphonylmethane)). Reproduced from ref. 70, https://doi.org/10.1371/journal.pone.0069609, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Close modal
Figure 1.4

Complexity of forensic samples: (i) scanning electron microscopy (SEM) image of whole leaf licit cigarette tobacco with stomata visible, (ii) SEM image of treated niche Swedish Snüs tobacco with visible crystalline structures. Reproduced from ref. 72 with permission from Elsevier, Copyright 2016.

Figure 1.4

Complexity of forensic samples: (i) scanning electron microscopy (SEM) image of whole leaf licit cigarette tobacco with stomata visible, (ii) SEM image of treated niche Swedish Snüs tobacco with visible crystalline structures. Reproduced from ref. 72 with permission from Elsevier, Copyright 2016.

Close modal
Figure 1.5

ATR-FTIR Chemimap comparison of Powder 1 (a) and representative spectra from the cyan (b), purple (c), and grey (d) regions compared to a pure compound reference spectrum of cocaine HCl (e), IR image of Powder 2 (f), and representative spectra from the cyan (g), red (h), blue (i) and yellow (j) regions compared to a pure compound reference spectrum of methandrostenolone (k). Reproduced from ref. 74, https://doi.org/10.3390/s16030278, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Figure 1.5

ATR-FTIR Chemimap comparison of Powder 1 (a) and representative spectra from the cyan (b), purple (c), and grey (d) regions compared to a pure compound reference spectrum of cocaine HCl (e), IR image of Powder 2 (f), and representative spectra from the cyan (g), red (h), blue (i) and yellow (j) regions compared to a pure compound reference spectrum of methandrostenolone (k). Reproduced from ref. 74, https://doi.org/10.3390/s16030278, under the terms of the CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

Close modal
Figure 1.6

Hard ionisation mass spectrum of methamphetamine. Reproduced from ref. 76 with permission from Elsevier, Copyright 2009.

Figure 1.6

Hard ionisation mass spectrum of methamphetamine. Reproduced from ref. 76 with permission from Elsevier, Copyright 2009.

Close modal
Figure 1.7

Soft ionisation mass spectrum of methamphetamine. Reproduced from ref. 77, https://mona.fiehnlab.ucdavis.edu/, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/.

Figure 1.7

Soft ionisation mass spectrum of methamphetamine. Reproduced from ref. 77, https://mona.fiehnlab.ucdavis.edu/, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/.

Close modal
Figure 1.8

Synthetic pathway from benzaldehyde and nitroethane to methamphetamine.

Figure 1.8

Synthetic pathway from benzaldehyde and nitroethane to methamphetamine.

Close modal
Figure 1.9

Eleven components (structures A–K) identified by mass spectrometry from the synthetic pathway to methamphetamine shown in Figure 1.8.

Figure 1.9

Eleven components (structures A–K) identified by mass spectrometry from the synthetic pathway to methamphetamine shown in Figure 1.8.

Close modal
Figure 1.10

Mass spectral information collected at each step of the synthetic pathway for methamphetamine production.

Figure 1.10

Mass spectral information collected at each step of the synthetic pathway for methamphetamine production.

Close modal
Figure 1.11

Mass spectra and chemical structures for three synthetic cannabinoids: JWH-250 (top), JWH-302 (middle) and JWH-201 (bottom). Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Figure 1.11

Mass spectra and chemical structures for three synthetic cannabinoids: JWH-250 (top), JWH-302 (middle) and JWH-201 (bottom). Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Close modal
Figure 1.12

Formation of the tropylium ion, m/z 91. Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Figure 1.12

Formation of the tropylium ion, m/z 91. Reproduced from ref. 80 with permission from Elsevier, Copyright 2014.

Close modal
Figure 1.13

Production of the tropylium ion, m/z 91, produced by one fragmentation pathway. Reproduced from ref. 81 with permission from Elsevier, Copyright 2020.

Figure 1.13

Production of the tropylium ion, m/z 91, produced by one fragmentation pathway. Reproduced from ref. 81 with permission from Elsevier, Copyright 2020.

Close modal
Figure 1.14

Differentiation between semi-synthetic and synthetic or natural origin for methamphetamine seizure samples from the Australian Border Force. Reproduced from ref. 84 with permission from Elsevier, Copyright 2015.

Figure 1.14

Differentiation between semi-synthetic and synthetic or natural origin for methamphetamine seizure samples from the Australian Border Force. Reproduced from ref. 84 with permission from Elsevier, Copyright 2015.

Close modal
Figure 1.15

Differentiation of cannabis plants based on their growing conditions. Reproduced from ref. 82 with permission from Elsevier, Copyright 2019.

Figure 1.15

Differentiation of cannabis plants based on their growing conditions. Reproduced from ref. 82 with permission from Elsevier, Copyright 2019.

Close modal
Figure 1.16

1H NMR spectra of a cocaine hydrochloride sample obtained at 600 MHz in D2O. The inset shows signals at 5.57 ppm (A), 5.98 ppm (B) and 6.54 ppm (C), used in quantification of cocaine, cis-cinnamoylcocaine and trans-cinnamoylcocaine, respectively. Reproduced from ref. 89 with permission from the Royal Society of Chemistry, Copyright 2018.

Figure 1.16

1H NMR spectra of a cocaine hydrochloride sample obtained at 600 MHz in D2O. The inset shows signals at 5.57 ppm (A), 5.98 ppm (B) and 6.54 ppm (C), used in quantification of cocaine, cis-cinnamoylcocaine and trans-cinnamoylcocaine, respectively. Reproduced from ref. 89 with permission from the Royal Society of Chemistry, Copyright 2018.

Close modal
Figure 1.17

Reference spectra of 1H NMR (in D2O) between 5.70 and 7.80 ppm of (A) sample HN157 and (B) sample HN153T, revealing significant differences in the ratio of each component. Reproduced from ref. 90 with permission from the Royal Society of Chemistry, Copyright 2019.

Figure 1.17

Reference spectra of 1H NMR (in D2O) between 5.70 and 7.80 ppm of (A) sample HN157 and (B) sample HN153T, revealing significant differences in the ratio of each component. Reproduced from ref. 90 with permission from the Royal Society of Chemistry, Copyright 2019.

Close modal
Table 1.1

Chemical structures of traditional substances that are abused: amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA, ecstasy), cocaine, nicotine, ethanol, ketamine, diazepam, alprazolam, amyl nitrite, heroin, codeine, lysergic acid diethylamide (LSD) and cannabis (tetrahydrocannabinol, Δ9-THC)

StimulantsDepressantsNarcotics/opioids
Amphetamine  Ethanol  Heroin  
Ketamine  
Methamphetamine  
Codeine  
Benzodiazepines 
MDMA  Valium (Diazepam)  
Hallucinogens/psychedelics 
Cocaine  LSD  
Xanax (Alprazolam)  
Nicotine  
Cannabis (Δ9-THC)  
Inhalants 
Amyl Nitrite  
StimulantsDepressantsNarcotics/opioids
Amphetamine  Ethanol  Heroin  
Ketamine  
Methamphetamine  
Codeine  
Benzodiazepines 
MDMA  Valium (Diazepam)  
Hallucinogens/psychedelics 
Cocaine  LSD  
Xanax (Alprazolam)  
Nicotine  
Cannabis (Δ9-THC)  
Inhalants 
Amyl Nitrite  
Table 1.2

Factors that are of interest to laboratory managers when considering instrumentation for illicit drug detection. Reproduced from ref. 21, https://doi.org/10.1186/s12954-017-0179-5, under the terms of the CC BY 4.0 license, http://creativecommons.org/licenses/by/4.0/

MethodDiscriminationSubstancesIdentify (qualify)Amount (quantify)Destroy sample?LabPoint of careCost (USD)Ease of useTime required for results
Most discriminatory Mass spectrometry ★★★★★ Virtually any ✓ ✓ Yes ✓  $5000 (used, older)–200 000+ (new, advanced models), Plus recurring costs (reagents, servicing) Intermediate–advanced (depending on model) Minutes 
Infrared spectrometry ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $4000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Second to minutes (including portable) 
Raman spectroscopy ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $5000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Seconds to minutes (Including portable) 
X-ray diffractometry ★★★★ Crystalline (solids) ✓ ✓ No ✓  $50 000–250 000+, Plus recurring costs (reagents, servicing) Advanced–expert Minutes to hours 
Least discriminatory Thin-layer chromatography ★★★ Most common drugs of abuse; possibly not some novel psychoactive substances ✓  Yes ✓ ✓ Initial supplies: $1000–3000, Recurring costs: $100–1000 per month depending on volume of usage (bulk reagents) Basic–intermediate Minutes to hours 
Ultraviolet spectroscopy ★★★ Most common drugs of abuse ✓  No ✓ ✓ $3000–10 000 Basic–intermediate Minutes 
Spot/color tests ★★ Most common drugs of abuse; must be already characterized (i.e., possibly not some) ✓  Yes ✓ ✓ Approximately 2–5 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Basic–intermediate Seconds to minutes 
Microcrystalline tests ★★ Several ✓  Yes ✓ ✓ Approximately 2–4 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Immunoassay ★★ Various metabolized drugs in urine samples ✓  Yes ✓  $5000–20 000 for initial equipment (analyzer), Recurring costs: $300–1000 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Urine dipstick test ★★ Fentanyl ✓  Yes  ✓ Approximately 1–5 dollars per test (in house), Recurring costs: $50–400 per month, depending on volume of usage Basic–intermediate Seconds to minutes 
MethodDiscriminationSubstancesIdentify (qualify)Amount (quantify)Destroy sample?LabPoint of careCost (USD)Ease of useTime required for results
Most discriminatory Mass spectrometry ★★★★★ Virtually any ✓ ✓ Yes ✓  $5000 (used, older)–200 000+ (new, advanced models), Plus recurring costs (reagents, servicing) Intermediate–advanced (depending on model) Minutes 
Infrared spectrometry ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $4000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Second to minutes (including portable) 
Raman spectroscopy ★★★★★ Virtually any ✓ ✓ No ✓ ✓ $5000 (used, older)–100 000 (new, advanced models), Portable: $10 000–60 000, Plus recurring costs (licensing, servicing) Basic–advanced (depending on model) Seconds to minutes (Including portable) 
X-ray diffractometry ★★★★ Crystalline (solids) ✓ ✓ No ✓  $50 000–250 000+, Plus recurring costs (reagents, servicing) Advanced–expert Minutes to hours 
Least discriminatory Thin-layer chromatography ★★★ Most common drugs of abuse; possibly not some novel psychoactive substances ✓  Yes ✓ ✓ Initial supplies: $1000–3000, Recurring costs: $100–1000 per month depending on volume of usage (bulk reagents) Basic–intermediate Minutes to hours 
Ultraviolet spectroscopy ★★★ Most common drugs of abuse ✓  No ✓ ✓ $3000–10 000 Basic–intermediate Minutes 
Spot/color tests ★★ Most common drugs of abuse; must be already characterized (i.e., possibly not some) ✓  Yes ✓ ✓ Approximately 2–5 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Basic–intermediate Seconds to minutes 
Microcrystalline tests ★★ Several ✓  Yes ✓ ✓ Approximately 2–4 dollars per test (in house), Recurring costs: $100–500 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Immunoassay ★★ Various metabolized drugs in urine samples ✓  Yes ✓  $5000–20 000 for initial equipment (analyzer), Recurring costs: $300–1000 per month depending on volume of usage (bulk reagents) Intermediate–advanced Minutes 
Urine dipstick test ★★ Fentanyl ✓  Yes  ✓ Approximately 1–5 dollars per test (in house), Recurring costs: $50–400 per month, depending on volume of usage Basic–intermediate Seconds to minutes 
Table 1.3

Dominant and change in drug consumption at festivals with differing music styles. Adapted from ref. 25 with permission from Elsevier, Copyright 2019

Dominant musical styleCross-section surveys/dominant drugsWastewater analysis/dominant drugsDrugs with significant change in consumption/festival in our study
Pop/rock Alcohol, cannabis26  MDMA/ecstasy, methamphetamine, cocaine27  MDMA/ecstasy, cocaine, cannabis 
MDMA/ecstasy28  Top fest, Pohoda 
Dance MDMA/ecstasy, speed, tobacco, alcohol, solvents, cannabis, inhalants, amyl nitrite, cocaine, LSD, benzodiazepines, ketamine29–31   MDMA/ecstasy, cocaine, cannabis 
Grape 
Multi-music Alcohol, tobacco, cocaine, cannabis MDMA/ecstasy32  MDMA/ecstasy, cocaine 
MDMA/ecstasy, cocaine33  Skalické dni 
Country/folk  Cocaine, codeine32  No significant change 
Guláš fest 
Metal Alcohol, tobacco, cannabis34  Tobacco35  No significant change 
VanDaal fest 
Dominant musical styleCross-section surveys/dominant drugsWastewater analysis/dominant drugsDrugs with significant change in consumption/festival in our study
Pop/rock Alcohol, cannabis26  MDMA/ecstasy, methamphetamine, cocaine27  MDMA/ecstasy, cocaine, cannabis 
MDMA/ecstasy28  Top fest, Pohoda 
Dance MDMA/ecstasy, speed, tobacco, alcohol, solvents, cannabis, inhalants, amyl nitrite, cocaine, LSD, benzodiazepines, ketamine29–31   MDMA/ecstasy, cocaine, cannabis 
Grape 
Multi-music Alcohol, tobacco, cocaine, cannabis MDMA/ecstasy32  MDMA/ecstasy, cocaine 
MDMA/ecstasy, cocaine33  Skalické dni 
Country/folk  Cocaine, codeine32  No significant change 
Guláš fest 
Metal Alcohol, tobacco, cannabis34  Tobacco35  No significant change 
VanDaal fest 
Table 1.4

Reports in the literature towards improved detection of methamphetamine

Detection methodMedium typeLiterature reference
Gas chromatography–mass spectrometry Plasma and urine 39  
Gas chromatography–mass spectrometry Blood 38  
Gas chromatography–mass spectrometry Hair 40  
Gas chromatography–mass spectrometry Insects 45  
Gas chromatography–mass spectrometry Nails 46  
High performance liquid chromatography Commercial tablets 47  
Two-dimensional high performance liquid chromatography Street samples 48  
High performance liquid chromatography–mass spectrometry Urine 49  
High performance liquid chromatography–mass spectrometry Fingerprint 50  
High performance liquid chromatography–tandem mass spectrometry Urine 51  
Matrix-assisted laser desorption ionisation mass spectrometry Fingerprint 42  
Chemiluminescence Urine 52  
Electrochemiluminescence Street samples 53  
High performance liquid chromatography–electrospray ionisation mass spectrometry Human urine 54  
High performance liquid chromatography–fluorescence detector Human urine 36  
Fluorescence polarisation immunoassay Purchased standards 55  
Gas chromatography–Fourier transform infrared spectroscopy Purchased standards 56  
Head-space solid phase microextraction gas chromatography–mass spectrometry Purchased standards 57  
Ion mobility spectrometry Hair 58  
Isotope ratio mass spectrometry Street samples 59  
Laser microscopy Hair 60  
Desorption ionisation on silicon mass spectrometry imaging Fingerprint sweat 61  
Nuclear magnetic resonance spectroscopy Purchased standards 62  
2H Nuclear magnetic resonance spectroscopy Synthetic pathway products 63  
Capillary electrophoresis Urine 64  
Electromembrane surrounded-solid phase microextraction Urine and blood 65  
Surface plasmon resonance Purchased standards 66  
Field asymmetric ion mobility spectrometry Air 67  
Colorimetric test Mobile telephone 68  
Detection methodMedium typeLiterature reference
Gas chromatography–mass spectrometry Plasma and urine 39  
Gas chromatography–mass spectrometry Blood 38  
Gas chromatography–mass spectrometry Hair 40  
Gas chromatography–mass spectrometry Insects 45  
Gas chromatography–mass spectrometry Nails 46  
High performance liquid chromatography Commercial tablets 47  
Two-dimensional high performance liquid chromatography Street samples 48  
High performance liquid chromatography–mass spectrometry Urine 49  
High performance liquid chromatography–mass spectrometry Fingerprint 50  
High performance liquid chromatography–tandem mass spectrometry Urine 51  
Matrix-assisted laser desorption ionisation mass spectrometry Fingerprint 42  
Chemiluminescence Urine 52  
Electrochemiluminescence Street samples 53  
High performance liquid chromatography–electrospray ionisation mass spectrometry Human urine 54  
High performance liquid chromatography–fluorescence detector Human urine 36  
Fluorescence polarisation immunoassay Purchased standards 55  
Gas chromatography–Fourier transform infrared spectroscopy Purchased standards 56  
Head-space solid phase microextraction gas chromatography–mass spectrometry Purchased standards 57  
Ion mobility spectrometry Hair 58  
Isotope ratio mass spectrometry Street samples 59  
Laser microscopy Hair 60  
Desorption ionisation on silicon mass spectrometry imaging Fingerprint sweat 61  
Nuclear magnetic resonance spectroscopy Purchased standards 62  
2H Nuclear magnetic resonance spectroscopy Synthetic pathway products 63  
Capillary electrophoresis Urine 64  
Electromembrane surrounded-solid phase microextraction Urine and blood 65  
Surface plasmon resonance Purchased standards 66  
Field asymmetric ion mobility spectrometry Air 67  
Colorimetric test Mobile telephone 68  

Contents

References

1.
Cicero
 
T. J.
Ellis
 
M. S.
Dialogues Clin. Neurosci.
2017
, vol. 
19
 (pg. 
259
-
269
)
2.
Gossop
 
M.
Keaney
 
F.
Sharma
 
P.
Jackson
 
M.
Eur. Addict. Res.
2005
, vol. 
11
 (pg. 
76
-
82
)
3.
J.
Stafford
and
C.
Breen
,
Australian Drug Trends 2016: Findings from the Illicit Drug Reporting System (IDRS)
, ed. National Drug and Alcohol Research Centre,
UNSW Australia
,
Sydney
,
2017
,
vol. 163
4.
Illicit drug data report
, ed. Australian Crime Commission,
Australian Crime Commission
,
Sydney
,
2003
5.
United Nations Office on Drugs and Crime
, Laboratory, Scientific Section and Drugs Working Group of the European Network of Forensic Science Institutes,
Guidelines on Representative Drug Sampling
,
United Nations
,
2009
6.
M. G.
Bovens
,
J.
Nagy
,
T.
Csesztregi
,
L.
Dujourdy
and
A.
Franc
,
Guidelines on Sampling of Illicit Drugs for Quantitative Analysis
,
2014
7.
O.
Drummer
and
D.
Gerostamoulos
,
Forensic Drug Analysis
,
2013
, pp. 2–9
8.
Gao
 
C.
Ogeil
 
R.
Drug Alcohol Rev.
2018
, vol. 
37
 (pg. 
819
-
820
)
9.
Cappiello
 
A.
Famiglini
 
G.
Palma
 
P.
Pierini
 
E.
Termopoli
 
V.
Trufelli
 
H.
Anal. Chem.
2008
, vol. 
80
 (pg. 
9343
-
9348
)
10.
Guilbault
 
G.
Hjelm
 
N.
Pure Appl. Chem.
1989
, vol. 
61
 (pg. 
1657
-
1664
)
11.
Matuszewski
 
B. K.
Constanzer
 
M. L.
Chavez-Eng
 
C. M.
Anal. Chem.
2003
, vol. 
75
 (pg. 
3019
-
3030
)
12.
C. H.
Daniel
,
Quantitative Chemical Analysis
,
W. H. Freeman and Company
,
New York
, 8th edn,
2010
13.
Kappi
 
F. A.
Tsogas
 
G. Z.
Christodouleas
 
D. C.
Giokas
 
D. L.
Sens. Actuators, B
2017
, vol. 
253
 (pg. 
860
-
867
)
14.
Zhou
 
W.
Yang
 
S.
Wang
 
P.
Bioanalysis
2017
, vol. 
9
 (pg. 
1839
-
1844
)
15.
Choi
 
B. K.
Gusev
 
A. I.
Hercules
 
D. M.
Anal. Chem.
1999
, vol. 
71
 (pg. 
4107
-
4110
)
16.
Stahnke
 
H.
Reemtsma
 
T.
Alder
 
L.
Anal. Chem.
2009
, vol. 
81
 (pg. 
2185
-
2192
)
17.
Rossmann
 
J.
Renner
 
L. D.
Oertel
 
R.
El-Armouche
 
A.
J. Chromatogr. A
2018
, vol. 
1535
 (pg. 
80
-
87
)
18.
Saar
 
E.
Gerostamoulos
 
D.
Drummer
 
O. H.
Beyer
 
J.
Anal. Bioanal. Chem.
2009
, vol. 
393
 (pg. 
727
-
734
)
19.
O'Neal
 
C. L.
Crouch
 
D. J.
Fatah
 
A. A.
Forensic Sci. Int.
2000
, vol. 
109
 (pg. 
189
-
201
)
20.
United Nations Office on Drugs and Crime
,
Guidelines on Representative Drug Sampling
,
2006
21.
Harper
 
L.
Powell
 
J.
Pijl
 
E. M.
Harm Reduct. J.
2017
, vol. 
14
 pg. 
52
 
22.
McCrae
 
K.
Tobias
 
S.
Tupper
 
K.
Arredondo
 
J.
Henry
 
B.
Mema
 
S.
Wood
 
E.
Ti
 
L.
Drug Alcohol Depend.
2019
, vol. 
205
 pg. 
107589
 
23.
Bijlsma
 
L.
Celma
 
A.
Castiglioni
 
S.
Salgueiro-González
 
N.
Bou-Iserte
 
L.
Baz-Lomba
 
J. A.
Reid
 
M. J.
Dias
 
M. J.
Lopes
 
A.
Matias
 
J.
Pastor-Alcañiz
 
L.
Radonić
 
J.
Turk Sekulic
 
M.
Shine
 
T.
van Nuijs
 
A. L. N.
Hernandez
 
F.
Zuccato
 
E.
Sci. Total Environ.
2020
, vol. 
725
 pg. 
138376
 
24.
Kinyua
 
J.
Negreira
 
N.
Miserez
 
B.
Causanilles
 
A.
Emke
 
E.
Gremeaux
 
L.
de Voogt
 
P.
Ramsey
 
J.
Covaci
 
A.
van Nuijs
 
A. L. N.
Sci. Total Environ.
2016
, vol. 
573
 (pg. 
1527
-
1535
)
25.
Mackuľak
 
T.
Brandeburová
 
P.
Grenčíková
 
A.
Bodík
 
I.
Staňová
 
A. V.
Golovko
 
O.
Koba
 
O.
Mackuľaková
 
M.
Špalková
 
V.
Gál
 
M.
Grabic
 
R.
Sci. Total Environ.
2019
, vol. 
659
 (pg. 
326
-
334
)
26.
Dent
 
C. W.
Galaif
 
J.
Sussman
 
S.
Stacy
 
A. W.
Burton
 
D.
Flay
 
B. R.
Am. J. Public Health
1992
, vol. 
82
 pg. 
124
 
27.
Bijlsma
 
L.
Serrano
 
R.
Ferrer
 
C.
Tormos
 
I.
Hernández
 
F.
Sci. Total Environ.
2014
, vol. 
487
 (pg. 
703
-
709
)
28.
Jiang
 
J.-J.
Lee
 
C.-L.
Fang
 
M.-D.
Tu
 
B.-W.
Liang
 
Y.-J.
Environ. Sci. Technol.
2015
, vol. 
49
 (pg. 
792
-
799
)
29.
Almeida
 
S. P.
Araujo Silva
 
M. T.
Subst. Use Misuse
2005
, vol. 
40
 (pg. 
395
-
404
)
30.
Forsyth
 
A. J. M.
Barnard
 
M.
McKeganey
 
N. P.
Addiction
1997
, vol. 
92
 (pg. 
1317
-
1325
)
31.
Pedersen
 
W.
Skrondal
 
A.
Addiction
1999
, vol. 
94
 (pg. 
1695
-
1706
)
32.
Mackuľak
 
T.
Škubák
 
J.
Grabic
 
R.
Ryba
 
J.
Birošová
 
L.
Fedorova
 
G.
Špalková
 
V.
Bodík
 
I.
Sci. Total Environ.
2014
, vol. 
494–495
 (pg. 
158
-
165
)
33.
Lai
 
F. Y.
Thai
 
P. K.
O'Brien
 
J.
Gartner
 
C.
Bruno
 
R.
Kele
 
B.
Ort
 
C.
Prichard
 
J.
Kirkbride
 
P.
Hall
 
W.
Carter
 
S.
Mueller
 
J. F.
Drug Alcohol Rev.
2013
, vol. 
32
 (pg. 
594
-
602
)
34.
Martin
 
G.
Clarke
 
M.
Pearce
 
C.
J. Am. Acad. Child Adolesc. Psychiatry
1993
, vol. 
32
 (pg. 
530
-
535
)
35.
Mackuľak
 
T.
Grabic
 
R.
Gál
 
M.
Gál
 
M.
Birošová
 
L.
Bodík
 
I.
Environ. Toxicol. Pharmacol.
2015
, vol. 
40
 (pg. 
1015
-
1020
)
36.
Al-Dirbashi
 
O.
Kuroda
 
N.
Nakashima
 
K.
Menichini
 
F.
Noda
 
S.
Minemoto
 
M.
Analyst
1998
, vol. 
123
 (pg. 
2333
-
2337
)
37.
Di Rago
 
M.
Chu
 
M.
Rodda
 
L. N.
Jenkins
 
E.
Kotsos
 
A.
Gerostamoulos
 
D.
Anal. Bioanal. Chem.
2016
, vol. 
408
 (pg. 
3737
-
3749
)
38.
Rasmussen
 
S.
Cole
 
R.
Spiehler
 
V.
J. Anal. Toxicol.
1989
, vol. 
13
 (pg. 
263
-
267
)
39.
Huestis
 
M. A.
Cone
 
E. J.
Ann. N. Y. Acad. Sci.
2007
, vol. 
1098
 (pg. 
104
-
121
)
40.
Suwannachom
 
N.
Thananchai
 
T.
Junkuy
 
A.
O'Brien
 
T. E.
Sribanditmongkol
 
P.
Forensic Sci. Int.
2015
, vol. 
254
 (pg. 
80
-
86
)
41.
Lin
 
D.-L.
Yin
 
R.-M.
Liu
 
H.-C.
Wang
 
C.-Y.
Liu
 
R. H.
J. Anal. Toxicol.
2004
, vol. 
28
 (pg. 
411
-
417
)
42.
Groeneveld
 
G.
de Puit
 
M.
Bleay
 
S.
Bradshaw
 
R.
Francese
 
S.
Sci. Rep.
2015
, vol. 
5
 pg. 
11716
 
43.
Fay
 
J.
Fogerson
 
R.
Schoendorfer
 
D.
Niedbala
 
R. S.
Spiehler
 
V.
J. Anal. Toxicol.
1996
, vol. 
20
 (pg. 
398
-
403
)
44.
Smith
 
F. P.
Forensic Sci. Int.
1981
, vol. 
17
 (pg. 
225
-
228
)
45.
Magni
 
P. A.
Pacini
 
T.
Pazzi
 
M.
Vincenti
 
M.
Dadour
 
I. R.
Forensic Sci. Int.
2014
, vol. 
241
 (pg. 
96
-
101
)
46.
Suzuki
 
O.
Hattori
 
H.
Asano
 
M.
Forensic Sci. Int.
1984
, vol. 
24
 (pg. 
9
-
16
)
47.
Shabir
 
G. A.
Indian J. Pharm. Sci.
2011
, vol. 
73
 (pg. 
430
-
435
)
48.
Andrighetto
 
L. M.
Stevenson
 
P. G.
Pearson
 
J. R.
Henderson
 
L. C.
Conlan
 
X. A.
Forensic Sci. Int.
2014
, vol. 
244
 (pg. 
302
-
305
)
49.
Cheng
 
W.-C.
Mok
 
V. K.-K.
Chan
 
K.-K.
Li
 
A. F.-M.
Forensic Sci. Int.
2007
, vol. 
166
 (pg. 
1
-
7
)
50.
Zhang
 
T.
Chen
 
X.
Yang
 
R.
Xu
 
Y.
Forensic Sci. Int.
2015
, vol. 
248
 (pg. 
10
-
14
)
51.
Concheiro
 
M.
Simões
 
S. M. D. S. S.
Quintela
 
Ó.
de Castro
 
A.
Dias
 
M. J. R.
Cruz
 
A.
López-Rivadulla
 
M.
Forensic Sci. Int.
2007
, vol. 
171
 (pg. 
44
-
51
)
52.
Hayakawa
 
K.
Imaizumi
 
N.
Ishikura
 
H.
Minogawa
 
E.
Takayama
 
N.
Kobayashi
 
H.
Miyazaki
 
M.
J. Chromatogr. A
1990
, vol. 
515
 (pg. 
459
-
466
)
53.
McGeehan
 
J.
Dennany
 
L.
Forensic Sci. Int.
2016
, vol. 
264
 (pg. 
1
-
6
)
54.
Katagi
 
M.
Nishikawa
 
M.
Tatsuno
 
M.
Miyazawa
 
T.
Tsuchihashi
 
H.
Suzuki
 
A.
Shirota
 
O.
Eisei Kagaku
1998
, vol. 
44
 (pg. 
107
-
115
)
55.
Cody
 
J. T.
Schwarzhoff
 
R.
J. Anal. Toxicol.
1993
, vol. 
17
 (pg. 
26
-
30
)
56.
Praisler
 
M.
Van Bocxlaer
 
J.
De Leenheer
 
A.
Massart
 
D. L.
J. Chromatogr. A
2002
, vol. 
962
 (pg. 
161
-
173
)
57.
Kuwayama
 
K.
Tsujikawa
 
K.
Miyaguchi
 
H.
Kanamori
 
T.
Iwata
 
Y.
Inoue
 
H.
Saitoh
 
S.
Kishi
 
T.
Forensic Sci. Int.
2006
, vol. 
160
 (pg. 
44
-
52
)
58.
Miki
 
A.
Keller
 
T.
Regenscheit
 
P.
Dirnhofer
 
R.
Tatsuno
 
M.
Katagi
 
M.
Nishikawa
 
M.
Tsuchihashi
 
H.
J. Chromatogr. B: Biomed. Sci. Appl.
1997
, vol. 
692
 (pg. 
319
-
328
)
59.
Toske
 
S. G.
Morello
 
D. R.
Berger
 
J. M.
Vazquez
 
E. R.
Forensic Sci. Int.
2014
, vol. 
234
 (pg. 
1
-
6
)
60.
Kimura
 
H.
Mukaida
 
M.
Mori
 
A.
J. Anal. Toxicol.
1999
, vol. 
23
 (pg. 
577
-
580
)
61.
Guinan
 
T.
Della Vedova
 
C.
Kobus
 
H.
Voelcker
 
N. H.
Chem. Commun.
2015
, vol. 
51
 (pg. 
6088
-
6091
)
62.
Hays
 
P.
J. Forensic Sci.
2005
, vol. 
50
 (pg. 
1342
-
1360
)
63.
Armellin
 
S.
Brenna
 
E.
Frigoli
 
S.
Fronza
 
G.
Fuganti
 
C.
Mussida
 
D.
Anal. Chem.
2006
, vol. 
78
 (pg. 
3113
-
3117
)
64.
Iwamuro
 
Y.
Iio-Ishimaru
 
R.
Chinaka
 
S.
Takayama
 
N.
Kodama
 
S.
Hayakawa
 
K.
Forensic Toxicol.
2010
, vol. 
28
 (pg. 
19
-
24
)
65.
Rezazadeh
 
M.
Yamini
 
Y.
Seidi
 
S.
J. Chromatogr. A
2015
, vol. 
1396
 (pg. 
1
-
6
)
66.
Smith
 
J. P.
Martin
 
A.
Sammons
 
D. L.
Striley
 
C.
Biagini
 
R.
Quinn
 
J.
Cope
 
R.
Snawder
 
J. E.
Toxicol. Mech. Methods
2009
, vol. 
19
 (pg. 
416
-
421
)
67.
Mohsen
 
Y.
Gharbi
 
N.
Lenouvel
 
A.
Guignard
 
C.
Procedia Eng.
2014
, vol. 
87
 (pg. 
536
-
539
)
68.
Choodum
 
A.
Parabun
 
K.
Klawach
 
N.
Daeid
 
N. N.
Kanatharana
 
P.
Wongniramaikul
 
W.
Forensic Sci. Int.
2014
, vol. 
235
 (pg. 
8
-
13
)
69.
He
 
X.
Wang
 
J.
You
 
X.
Niu
 
F.
Fan
 
L.
Lv
 
Y.
Spectrochim. Acta, Part A
2020
, vol. 
241
 pg. 
118665
 
70.
Hughes
 
J.
Ayoko
 
G.
Collett
 
S.
Golding
 
G.
PLoS One
2013
, vol. 
8
 pg. 
e69609
 
71.
Ricci
 
C.
Chan
 
K. L. A.
Kazarian
 
S. G.
Appl. Spectrosc.
2006
, vol. 
60
 (pg. 
1013
-
1021
)
72.
Quayle
 
K.
Clemens
 
G.
Sorribes
 
T. G.
Kinvig
 
H. M.
Stevenson
 
P. G.
Conlan
 
X. A.
Baker
 
M. J.
Forensic Sci. Int.
2016
, vol. 
266
 (pg. 
549
-
554
)
73.
Chan
 
K. L. A.
Kazarian
 
S. G.
Analyst
2006
, vol. 
131
 (pg. 
126
-
131
)
74.
Lanzarotta
 
A.
Sensors
2016
, vol. 
16
 pg. 
278
 
75.
Johnston
 
A.
J. Forensic Res.
2018
, vol. 
9
 (pg. 
418
-
423
)
76.
Awad
 
T.
Belal
 
T.
DeRuiter
 
J.
Kramer
 
K.
Clark
 
C. R.
Forensic Sci. Int.
2009
, vol. 
185
 (pg. 
67
-
77
)
77.
Methamphetamine: Soft Ionisation
,
MassBank of North America
,
MassBank of North America UC Davis
,
2020
78.
Hernández
 
F.
Castiglioni
 
S.
Covaci
 
A.
de Voogt
 
P.
Emke
 
E.
Kasprzyk-Hordern
 
B.
Ort
 
C.
Reid
 
M.
Sancho
 
J. V.
Thomas
 
K. V.
van Nuijs
 
A. L. N.
Zuccato
 
E.
Bijlsma
 
L.
Mass Spectrom. Rev.
2018
, vol. 
37
 (pg. 
258
-
280
)
79.
L. M.
Andrighetto
,
Platform technology towards the chemical fingerprinting methamphetamine from ephedrine pathways
,
Deakin University
,
2016
80.
Harris
 
D. N.
Hokanson
 
S.
Miller
 
V.
Jackson
 
G. P.
Int. J. Mass Spectrom.
2014
, vol. 
368
 (pg. 
23
-
29
)
81.
Tyler Davidson
 
J.
Piacentino
 
E. L.
Sasiene
 
Z. J.
Abiedalla
 
Y.
DeRuiter
 
J.
Clark
 
C. R.
Berden
 
G.
Oomens
 
J.
Ryzhov
 
V.
Jackson
 
G. P.
Forensic Chem.
2020
, vol. 
19
 pg. 
100245
 
82.
Matos
 
M. P. V.
Jackson
 
G. P.
Forensic Chem.
2019
, vol. 
13
 pg. 
100154
 
83.
Liu
 
C.
Liu
 
P.
Jia
 
W.
Fan
 
Y.
J. Forensic Sci.
2018
, vol. 
63
 (pg. 
1053
-
1058
)
84.
Collins
 
M.
Salouros
 
H.
Sci. Justice
2015
, vol. 
55
 (pg. 
2
-
9
)
85.
Shibuya
 
E. K.
Souza Sarkis
 
J. E.
Neto
 
O. N.
Moreira
 
M. Z.
Victoria
 
R. L.
Forensic Sci. Int.
2006
, vol. 
160
 (pg. 
35
-
43
)
86.
West
 
J. B.
Hurley
 
J. M.
Ehleringer
 
J. R.
J. Forensic Sci.
2009
, vol. 
54
 (pg. 
84
-
89
)
87.
L. F.
Souza
and
L. M.
Lião
, in
Forensic Analytical Methods
,
The Royal Society of Chemistry
,
2019
, pp. 79–114
88.
LeBelle
 
M. J.
Dawson
 
B.
Lauriault
 
G.
Savard
 
C.
Analyst
1991
, vol. 
116
 (pg. 
1063
-
1065
)
89.
Benedito
 
L. E. C.
Maldaner
 
A. O.
Oliveira
 
A. L.
Anal. Methods
2018
, vol. 
10
 (pg. 
489
-
495
)
90.
Naqi
 
H. A.
Husbands
 
S. M.
Blagbrough
 
I. S.
Anal. Methods
2019
, vol. 
11
 (pg. 
4795
-
4807
)
91.
Naqi
 
H. A.
Woodman
 
T. J.
Husbands
 
S. M.
Blagbrough
 
I. S.
Anal. Methods
2019
, vol. 
11
 (pg. 
3090
-
3100
)
92.
Burns
 
N. K.
Theakstone
 
A. G.
Zhu
 
H.
O'Dell
 
L. A.
Pearson
 
J. R.
Ashton
 
T. D.
Pfeffer
 
F. M.
Conlan
 
X. A.
Anal. Chim. Acta
2020
, vol. 
1104
 (pg. 
105
-
109
)
93.
Gryczynski
 
J.
Schwartz
 
R. P.
Mitchell
 
S. G.
O'Grady
 
K. E.
Ondersma
 
S. J.
Drug Alcohol Depend.
2014
, vol. 
141
 (pg. 
44
-
50
)
94.
Kuhn
 
E. J.
Walker
 
G. S.
Whiley
 
H.
Wright
 
J.
Ross
 
K. E.
Int. J. Environ. Res. Public Health
2019
, vol. 
16
 pg. 
4676
 
95.
Martyny
 
J. W.
Arbuckle
 
S. L.
McCammon
 
C. S.
Esswein
 
E. J.
Erb
 
N.
Van Dyke
 
M.
J. Chem. Health Saf.
2007
, vol. 
14
 (pg. 
40
-
52
)
96.
Zuccato
 
E.
Castiglioni
 
S.
Philos. Trans. R. Soc., A
2009
, vol. 
367
 (pg. 
3965
-
3978
)
97.
Ondarza
 
P. M.
Haddad
 
S. P.
Avigliano
 
E.
Miglioranza
 
K. S. B.
Brooks
 
B. W.
Sci. Total Environ.
2019
, vol. 
649
 (pg. 
1029
-
1037
)
98.
S.
Gaw
,
K.
Thomas
and
T. H.
Hutchinson
, in
Pharmaceuticals in the Environment
,
The Royal Society of Chemistry
,
2016
, pp. 70–91
99.
Capaldo
 
A.
Gay
 
F.
Lepretti
 
M.
Paolella
 
G.
Martucciello
 
S.
Lionetti
 
L.
Caputo
 
I.
Laforgia
 
V.
Sci. Total Environ.
2018
, vol. 
640–641
 (pg. 
862
-
873
)
Close Modal

or Create an Account

Close Modal
Close Modal