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A sensor is defined as a single or series of instruments that are founded on the utilization of chemical and/or physical principles such as electrochemistry, fluorescence, thermal, surface plasmon resonance, piezo, reflectometry, chemo- and bioluminescence, as well as optical sensors (e.g. visible and vibrational spectroscopy). More specifically, the term chemical sensor refers to or describes a small appliance that is capable of delivering instantaneous information about the chemicals (e.g. concentration) and other compounds present in either a food sample or food system throughout the different steps of the manufacturing, storage, process (e.g. on-line) and marketing of the food. This chapter presents a summary of the main characteristics of the most frequently used and novel sensors applied by the food industry to measure and monitor issues related to food contamination and spoilage, aroma and taste, among other applications.

The term sensor refers to a single or series of instruments that are based on the utilization of chemical and physical principles such as electrochemistry, fluorescence, thermal, surface plasmon resonance, piezo, reflectometry, chemo- or bioluminescence, and optics (e.g. vibrational spectroscopy).1–13  More specifically, the term chemical sensor refers to or describes a small appliance that is able to deliver instant information about the chemicals and other compounds present in a food sample or food system that can be used throughout the different steps of the supply and value chain, including the processing, storage and marketing (e.g. on-line) of food ingredients and products.1–13 

Current advances in sensing technologies have allowed for the development of novel applications in a wide range of fields, including food sciences.1–13  Some of these advances that have boosted the implementation and utilization of these devices in food analysis include instrument miniaturization, the use of low sample volumes, improvements in sample integrity over sample destruction (non-destructive), advances in chemical recognition, ease of manufacture using recognition methods such as molecularly imprinted polymers (MIPS), automation, and remote sensing (e.g. at/in/on-line).1–13  These developments have paved the way for developments in many low-cost and autonomous sensors that can be economically produced and deployed in a wide range of food applications.1–13  One advantage of the use of these sensors is that they can continuously collect and report data, providing information and knowledge about the sample (e.g. chemical changes during manufacturing, spoilage) or system.1–13  This has resulted in an unprecedented amount of real-time and continuous measurements that can be collected and implemented during the processing, storage and marketing of food and food ingredients allowing a better understanding of the production system, including the supply and value chain.1–13  However, the utilization of these new technologies has presented new challenges, such as how information can be extracted from the application of sensing technologies to evaluate complex data sets.14,15  These challenges can be overcome through the application of significantly more advanced data analytical techniques to accurately interpret the data collected and delivered by applying efficient management systems along the supply and value chains.14,15 

Food safety and security have occurred as gradually significant public concerns appeared worldwide due to sub-quality foods being linked to increased food security issues (e.g. fraud, contamination), mortality, human distress, well-being, and economic burden.16–19  Consequently, in the so-called digital economy where consumer awareness and expectations of safety are high, the food industry must comply with and meet the needs of consumers as well as food quality control guidelines and standards imposed by government and food authorities.16–19  Consumers will also use this information to make informed purchase decisions about their preferences for specific foods and/or food ingredients with high quality and reasonable prices, and at the same time, must maintain high-quality standards and assurance of product safety.16–19 

Fitting the end-user conformity with regulatory guidelines on food quality, sensing technologies designers and the food industry itself have responded with continuous improvements and developments of analytical methodologies and instrumental techniques.16–19  For example, analytical methods such as liquid chromatography (LC), gas chromatography (GC) and high liquid performance chromatography (HPLC) have gained great importance in the analysis of foods and have been used in a wide number of applications in the field of food analytics.16–19,21,26  However, chromatographic analysis is constrained by the rigidities of often elaborate sample preparation steps, homogenization, extraction and clean up before the analytical component can be tested and quantified.16–19  These processing steps are often repeated multiple times, as many samples are needed to give an accurate result due to the number of interferences in the food matrix (e.g. food extracts, freeze dry material), which can result in inaccurate identification and false positives.16–19  Issues such as widespread set-up and extraction, and clean-up processes required during HPLC analysis can also hamper the analysis of foods in issues such as contamination or fraud monitoring.16–19  The cumbersome time requirements imposed during the use of HPLC and other instrumental analytical techniques make them unsuitable to be utilized to evaluate and monitor fresh food ingredients and products due to their intrinsic short shelf-life. Therefore, sensing technologies devices and platforms may present a viable alternative to the current instrumental methods used by the food industry.16–19 

Concomitantly, public health and consumer safety authorities are demanding the development of rapid screening techniques that can be used in both the field and laboratory to evaluate not only the composition and quality of foods but also to guarantee their safety. These techniques should be relatively inexpensive, easy to operate and require little or no sample preparation, allowing them to be used in-line or at-line. Sensing techniques have demonstrated their potential to characterize the complexity of food and food ingredients rather than relying solely on subjective analysis by humans.20–25  It has been defined by several scientists and researchers that the ideal instrumental method must be objective, cost-effective and provide rapid, reproducible results, with continuous operation.20–25  However, up to now, sensing technologies have not been extensively used by the food industry due to the lack of training and knowledge about their capability as an analytical method, in addition to issues associated with their reliability that is highly dependent on the food matrix being analysed.20–25 

This chapter offers a summary of the main characteristics of the most frequently used and novel sensors applied by the food industry to measure and monitor issues related to food contamination and spoilage, aroma and taste, among other applications.

A biosensor is defined as an analytical tool that combines a biologically sensitive recognition component (e.g. antibodies, nucleic acids, enzymes, whole cells and aptamers) restrained on a physicochemical transducer, and connected to a detector that allows the identification and quantification (e.g. concentration) of a single or several specific analytes in a food.4,27–33  For example, electrochemical biosensors utilize an electrode transducer to identify electrons released by the reaction of the bioreceptor and analyte to acquire a measurable analysis of the contaminant present in a food.4,21,27–36  One of the most important achievements of biosensors is that they can recognize biological elements, which provides them with a superior level of specificity and binding affinity with the target molecule.35–40  These binding properties are termed specific binding or coupling and determine whether an interaction occurs, which creates the electrical signal that is recorded and amplified by the sensing technology during the analysis of food.35–40  Because of the particularity of the recognition entity towards the analyte, a high level of selectivity is achieved which results in signals generated solely from such precise interactions, irrespective of the matrix complexity.35–40 

Food security and safety demand high-speed, accurate and selective identification of the analytes present within the food matrix (e.g. pathogenic bacteria, presence of pesticides and toxins).40–44  Developments in biosensors have enabled the quantitative detection and screening of different microorganisms and molecules in food. In addition to their inherent specificity, biosensors have demonstrated that they can overcome the multifactorial food industry challenges of high analytical accuracy in complex food matrices, overcoming issues such as differences in density, pH and temperature.40–43  Another advantage of biosensors is that they have the potential for miniaturization, which results in the sensor requiring greatly reduced sample sizes or volumes.40–43  However, miniaturization has only partially been achieved due to restrictions in the structural integrity of very fine electrode tips associated with microelectrodes.40–43 

Recent developments in novel bio-recognition molecules, such as synthetic aptamers, DNA, proteins and viruses have increased the selectivity of molecules during analysis.45–47  Improvements in the immobilization of bio-recognition molecules because of robust attachment methods like electrodeposition and nanoparticle-bound entities at the working electrode interface are a significant step in the increased application of biosensors in food analysis.45–47  This is due in no small part to their greater specificity, selectivity and affinity for their target analytes.45–47  The utilization of biosensors in the field of food analysis has grown over the last two decades as a result of improved accuracy in target detection, intensifying demands about quality and safety from stakeholders, such as safety regulators, traders and consumers as well as a significant reduction in the analysis times associated with electrochemical detection.45–47  Enzymatic-based biosensors have been recognized as being of high selectivity, where the next stage in biosensor design includes gene-based sensors involving DNA, such as the recognition or coupling entity (via hybridization), antibody- or antigen-based biosensors, as well as whole-cell sensors.45–47 

Within the agri-food industry, pathogen detection trends have focused on the utilization of a single-sensor platform for the detection of multiple pathogens/toxins.48–50  More recently, biotechnology has shifted into ever smaller systems to allow for portability, cost reduction, analysis time reduction and commercial viability of this type of sensor.48–50  Improvements in microfabrication systems have similarly aided in advancing biosensor technology and utility.48–50 

Emerging nanomaterials, such as nanoparticles and nanofibres, play an important role in paving the way for this miniaturization trend.51–60  These functional nanomaterials improve electrochemical biosensors by refining the response features of the electrode, increasing its surface area and assisting in robust attachment of the bioreceptor or recognition unit.51–60  With a greater surface area to volume ratio, nanomaterials lend greater catalytic ability, warrant biocompatibility and achieve lower mass transfer resistance. These properties improve the selectivity, sensitivity, time efficiency and cost-effectiveness of the biosensor.51–60  Similarly, the increase in transducer surface area delivers greater conductivity and sensitivity, promoting greater interaction capacity, and lowering the detection limits.51–60 

A recent example of a nano-biosensor is the detection of pesticide residues at low concentrations (e.g. below 0.4 pM).51  The inclusion of other nanomaterials at the transducer level (e.g. carbon nanotubes) can increase electron transfer and increase the transducer activity.51–60  The evidence of these improvements is in the slow but gradual replacement of traditional enzyme–substrate biosensors by nano-biosensor technology.51–60  Nano-biosensors have been developed for the agriculture and food-processing industries to identify and quantify pesticides, herbicides, pathogenic microorganisms and other microbial contaminants or unwanted microorganisms such as viruses and bacteria, insects and fungi, as well as hormones and other foreign substances.51–60 

Microfluidics have been also incorporated into biosensors as they can be utilized throughput processing, reducing sample and reagent volumes (down to the nanolitre), increase sensitivity, and employ a single platform for both sample preparation and detection.61–64  The main advantage of microfluidics is that they can be portable, disposable and offer real-time detection, allowing for the simultaneous analysis of different analytes in a single device with exceptional accuracy.61–64  The detection of pathogenic species present in food such as Salmonella is an example of the application of microfluidic nano-biosensors.61–64 

Existing techniques and methods used during the routine analysis of foods are founded on the determination and evaluation of the chemical composition of a given sample based on the analysis of a single parameter. From this, either the overall quality or the changes that have occurred during food processing and/or storage are inferred.65–72  Current innovations in instrumentation (e.g. handheld, portable, hyperspectral, multispectral), computing and software are enhancing the development of novel tools and applications.65–72  These technological advances are determining a fundamental change in the direction of the analytical methods currently in use and on the underlying assumptions of how food ingredients and products are evaluated.65–72  These fundamental changes are accentuated by the current challenges facing the food industry such as food security, traceability, safety of food ingredients, prediction of functionality and consumer choices, that are generally difficult to measure using the current analytical approaches used by the food industry.65–72  This has determined that the quantification of parameters associated with food quality is beyond the measurement of a single analyte, where the evaluation and monitoring of issues associated with food security and sustainability require changes in the way that food ingredients and products are analysed, to assure consumers about all aspects of food safety and security.65–72 

It is not surprising that the amalgamation of sensing technologies (e.g. NIR spectroscopy) with data analytics is driving these current changes in the food industry towards so-called smart and sustainable food systems.65–72  The incorporation of sensing technologies, the Internet of Things and Big Data has also been proven to reduce waste and losses during the transport and storage of food ingredients and products along the value chain. An example of this is the incorporation of wireless sensing devices that enable monitoring of fluctuations in both temperature and humidity during the transport and storage of foods.73 

The capability of sensing technologies based on the utilization of vibrational spectroscopy to measure various parameters simultaneously, along with the speed of analysis, has demonstrated the huge potential value of these methods for incorporation in a sustainable food industry. These characteristics are also influential in the development of new approaches to food analysis and safety monitoring.65–72  Progress in these types of applications is allowing the food industry to incorporate novel analytical methods that can be used to evaluate the composition of food as well as to monitor or detect changes (e.g. unwanted problems) providing rapid means of qualitative rather than quantitative analysis.65–72 

Vibrational spectroscopic techniques have been developed to determine and measure biochemical, chemical and functional properties in different biological samples, including food ingredients and products.65–72  Vibrational spectroscopy is the shared term utilized to describe the family of different techniques that include near-infrared (NIR), mid-infrared (MIR), Raman, terahertz spectroscopies and hyperspectral imaging.65–72  Once food is analysed by these techniques, the vibrational spectrum behaves as the so-called “fingerprint” of the sample, providing the means to quantify, qualify, characterize and elucidate the intrinsic characteristics of food ingredients and products.65–74  The main characteristics of vibrational spectroscopy are associated with the simple steps required for sample preparation, the non-destructive and rapid nature of the analysis, and it has been extensively used to analyse a wide range of food ingredients and products qualitatively and quantitatively.65–78 

Vibrational spectroscopy (e.g. NIR, MIR and Raman) was first embraced by the agricultural and food sector as an analytical technique to predict the proximate composition of foods and raw ingredients in the early 1970s.65–78  In vibrational spectroscopy, bonds such as C–H, O–H and N–H in the sample have high absorbance in both NIR and MIR regions. For example, the simple sample preparation required during the analysis of food samples in NIR spectroscopy makes it more feasible to monitor samples during processing (e.g. on-, in- and at-line), quality control and/or farm applications (e.g. from farm to fork).65–78  On the other hand, MIR spectroscopy might require some level of sample preparation and can be influenced by sample moisture, however, it shows better specificity and reproducibility.65–78  Therefore, the choice between NIR and MIR spectroscopy depends on the purpose of the specific application. Raman is often considered to be a complementary technique to both NIR and MIR spectroscopy.65–78  Raman spectroscopy generates intense bands of functional groups with high polarizability (CCl, CC, and CN) in contrast to the strong absorbance bands of functional groups with strong polarization (O–H and CO) in the infrared region.78,79  The main bands in the spectra are due to local vibrational modes that emerge strongly in the infrared spectra, while stretching modes appear intensively in Raman spectra.78,79  Raman spectroscopy is based on inelastic scattering from the interaction of incident radiations with vibrating molecules, where the main issue encountered in the application of this technique is its low sensitivity due to weak Raman scattering.78,79  A wide range of techniques have been developed to overcome and improve the Raman signal such as stimulated Raman scattering, coherent anti-Stokes Raman scattering, resonance Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS).78,79 

Terahertz waves lie between the end of the IR region and the start of the microwave region.80  The application of terahertz in the analysis of biological samples is still at an early stage.80  The main advantage of THz is that this technique can be useful as a potential tool to characterize far-infrared vibrational modes in food with the main objective of water measurement. This is because THz waves coincide with the vibration of the hydrogen bond and it is vested with high sensitivity to water molecules.80  However, this is also the limitation for applying THz spectroscopy to evaluate other compounds in biological samples with high moisture contents.80  Therefore, sensors based on the utilization of vibrational spectroscopy can only provide point-based spectral information, and thus no spatial information for a large area will be available, although spatial information will be of especial interest in some biological applications.80 

Hyperspectral imaging techniques can be considered a blend of the main characteristics of vibrational spectroscopy and imaging technique into one system, providing both spatial and spectral information about the sample.81–86  During the implementation of this technique a hyperspectral image is constructed by a hypercube with a three-dimensional data set consisting of one spectral and two spatial dimensions.81–86  Hyperspectral reflectance imaging is often accomplished by combining the visible and NIR wavelength ranges and has been widely applied to quantify different attributes, whereas Raman or Terahertz spectroscopy can also be integrated with imaging technology to form Raman or THz imaging.81–86  Raman imaging is sensitive to specific chemicals at low concentrations, enabling visualization of the chemical distribution in samples where THz imaging might be an alternative to X-rays.81–86 

The ultimate analytical tool to measure and monitor food aroma and flavour can be defined as such a technique that can quantify changes in the temporal dimension that are directly associated with perception.87–94  To realize this in a timely manner and at low cost, analytical systems that provide high selectivity and sensitivity comparable with the human olfactory receptor are required during the analysis of foods.87–94  Although techniques such as electroencephalography (EEG) and magnetic resonance imaging (MRI) can be used to monitor participants’ brain activity directly associated with a taste or aroma stimulus, these instruments are not practical for application in industrial settings (e.g. due to excessive cost, lack of portability, time consuming).87–97 

Electronic noses (EN) are instrumental devices that have been developed to mimic the perception of the mammalian olfactory system.13  The definition and concept of EN is often associated with the recognition of odours (e.g. volatile compounds) or an attempt to mimic the smell process with an instrumental device.13  The EN instruments offer the capability to detect different volatiles or gases with no odour activity. These instruments can be adapted to monitor or measure substances of importance to humans and animals, such as the scent of other animals, food ingredients or spoilage.13 

A typical EN instrument consists of a series of an array of sensors.87–94  This is the most common type of EN, although new technologies have been evaluated and reported by several authors such as metal oxide sensors (MOX),95  mass spectrometry (MS), ion mobility spectrometry (IMS) and gas chromatography (GC)-based sensors.13,20,22 

The utilization of EN has been evaluated by many researchers as a means to differentiate food and food ingredients based on the aroma and quantification of volatile compounds.87  Food ingredients and products characterization is based on the analysis and detection of aroma properties.87  For example, analytical solutions for food and wine composition often involve the use of GC-MS techniques, but analysis can be time consuming, due to sample preparation steps and complex data interpretation.98,99  Electronic noses based on MS can detect mass fragments formed during the ionization of volatile compounds where some of these volatiles can be directly responsible for explaining or monitoring changes in the sensory properties of the samples.98,99  Overall, EN instruments have found a niche application in the detection of food spoilage. It is well known that when food is stored, it releases specific volatile compounds, such as hydrocarbons, ethanol, aldehydes, acids, ethers and esters.98,99  The presence and concentrations of these volatile compounds are different and depend, among other factors, on the raw material, brand and type of food, storage conditions (e.g. temperature, humidity) and issues associated with spoilage (e.g. the presence of microorganisms).98–100  These volatile compounds have been proposed as fingerprints of the factors affecting food during storage and processing.98–100 

Conducting organic polymers (COP) have been also explored as EN.101–103  Instruments based on COP consist of a series of active mechanisms which detect odours and convert chemical vapours into electrical signals.101–103  Conducting polymer can be affected by the doping levels, the ion size of the dopant, the water content and protonation levels. These types of sensors can be also classified depending on their mode of transduction and/or the application.101–103 

Metal oxide semiconductors (MOS) also were evaluated as EN. Instruments based on MOS consist of a gas sensor which can detect changes in the concentration of volatile compounds in the sample as a function of a treatment, spoilage, etc.102  Real-time quantitative detection of volatiles can be achieved using an electron impact source with a membrane separator between the source and the external environment coupled with a mass spectrometer.88  The advantages of MS (relative to the electronic nose) are its fast response rate and greater sensitivity.88,102  However, often the separator membrane reduces the technique's overall sensitivity, while its selective permeability reduces its application potential.88,102 

More recently, further developments in instruments based on mass spectrometry systems, such as atmospheric pressure chemical ionization mass spectrometry (APCI-MS) and proton transfer reaction mass spectrometry (PTR-MS) are envisioned as being capable of the real-time detection of compounds at ppb concentrations.22,88 

The utilization of EN devices has allowed unique volatile compounds to be distinguished within a wide range of food ingredients, beverages and products.22,88,104,105  The application of EN provides a certain specificity, mainly due to the fact that one sensor might respond to more specific compounds such as an ester than aldehydes.22,88,104,105  Unfortunately, none of the EN and sensor arrays available can respond in a manner comparable with that of the human olfactory system.22,88,104,105  Furthermore, they lack both sensitivity (ppm/ppb concentration) and, in some cases, selectivity.22,88,104,105  For example, EN coupled with their comparatively slow response rate make existing instrumentation incapable of appropriately monitoring rapid changes in breath volatile concentration.22,88,104,105 

Another sensing technology widely used is the electronic tongue. This instrument was designed in combination with biosensors that utilized electrochemical transducers to function.106–110  Several types of chemical sensors have been used or design as electronic tongue instruments, such as a sensor array: electrochemical (potentiometric, voltammetric, amperometric, impedimetric, conductimetric), optical, mass and enzymatic biosensors.106–110  An electronic tongue can be defined as an advanced analytical instrument made up of an array of sensors combined with pattern recognition technologies that are able to execute quantitative and qualitative analyses.106–110  A full detailed characterization of the properties of the different electronic tongue instruments can be found elsewhere, where the most utilized are the potentiometric and voltammetric electronic tongue instruments.106–110  It is well recognized that potentiometric electronic tongues are the most widely used, while voltammetric instruments have been proved to be more adaptable with high resistance to electrical imbalances and better signal-to-noise ratio.106–110  Voltammetric electronic tongues are limited to redox-active substances and are sometimes associated with low detection limits.106–110  The potentiometric electronic tongues have achieved notoriety in food quality assurance.106–110  This type of electronic nose is made up of an array of non-selective chemical sensors with partial specificity to different constituents in solution.106–110  Similarly to EN, signals from the sensors are interpreted using pattern recognition techniques that provide the potential to quantify or qualify the composition of a food.106–110  Depending on the sensitivity and specificity, different materials have been used to build electronic noses, particularly if a high performance is desired.106–110  Although electronic noses do not require special interactions with the analyte, the sensing unit is still required to respond electrically to small changes in the liquid under analysis.106–110  However, depending on the method of detection, a level of electrical conductivity and/or electroactivity may be required.106–110  These materials include lipid membranes, chalcogenide glasses, ion-sensitive field effect transistors (ISFET) and metal oxide semiconductor field effect transistors (MOSFET).106–110 

Many other sensors have been developed or explored, such as colorimetric, a wide variety of electrochemical and chemical sensors, smartphone devices, among others. Please note that the in-depth characterization of the different sensors available is beyond the scope of this chapter, and it is suggested that the reader seek information from the vast number of publications, scientific and industry reports, and patents available in the field. Figure 1.1 schematically represents the applications and uses of sensors in food sciences (research and industry).

Figure 1.1

Applications and uses of sensors in food sciences (research and industry).

Figure 1.1

Applications and uses of sensors in food sciences (research and industry).

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One important aspect or characteristic of the use of sensors is the need for the integration with mathematical and statistical methods that must be required to analyse and interpret the data collected during analysis.111–118  The utilization of both data analytics and chemometrics incorporates an extensive range of mathematical methods and algorithms that can be applied to analyse and optimize the data collected.111–118  These methods are the result of the utilization solely or in combination of both qualitative and quantitative models and can often identify underlying relationships that exist between environmental variables, which cannot be independently identified using simple, univariate procedures.111–118 

Several data analytics methods and techniques have been shown to be useful in the mining of data that have been obtained during the application and implementation of different types of sensors.111–118  Some examples include the data mining of underlying factors that can be used to identify or monitor food contamination and spoilage, prediction of volatiles or other chemicals relevant to the food ingredient or food, to monitor the process, among others.111–118  Most of the methods and techniques employed to extract information from these instrumental techniques have become part of so-called artificial intelligence (AI) or machine learning (ML) tools.111–118  Machine learning tools are associated with the utilization of statistical methods to identify patterns in a data set and can be classified as unsupervised and supervised approaches.111–118  These techniques allow the user to isolate and interpret information from complex data sets, illustrate patterns within the data, as well as the development of mathematical relationships between different sources of data (e.g. calibration and validation models).111–118  Some of the most common and well-known techniques are principal component analysis (PCA) cluster analysis, K-nearest neighbours (k-NN), linear discriminant analysis (LDA), factorial discriminate analysis (FDA), partial least squares discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA), artificial neural networks (ANN), soft independent modelling of class analogy (SIMCA), and more recently the group of machine learning methods and techniques [e.g. support vector machines (SVM)].111–118 

It is well accepted that sensing technologies have clear advantages over traditional analytical methods, however, the perfect sensing technique does not yet exist as there are many hindrances in its development and translation into real-world applications. Some of these issues are mostly associated with the implementation and commercial availability of these types of instrumentation. Nevertheless, it is inevitable that the future of sensing technologies will involve partnership with information communications technology to assist food producers, retailers, authorities and even consumers, in their decision-making process, preparing them with the necessary tools and data to improve their decision-making process. In addition, the implementation and utilization of sensing technologies will enable and improve our understanding of food systems, allowing for better management of natural resources, making food production more sustainable.

Networks of sensors will influence the development of new sensing systems that have the potential to revolutionize food analysis. These systems (e.g. single sensors or in combination) will be able to detect multiple signals allowing for a more holistic analysis of food quality and safety, and therefore the whole supply and value chain. The utilization of sensing technologies will also improve the ability and efficiency of current analytical systems, allowing for the evaluation and detection of multiple analytes simultaneously.

There is no doubt that the integration and implementation of different types of sensors have great promise in the field of food analytics. For example, it has been proposed that the fusion of electronic tongues with electronic noses could have a great impact as they increase the identification capabilities of these monitoring systems, coming close to mimicking the biological system (e.g. food). In addition, advantages such as real-time monitoring throughout food manufacture (e.g. dairy, brewing) further enhance the usefulness of sensing technologies driving the push for their commercial availability to the consumer. The intrinsic specificity, sensitivity and adaptability of most of the sensing technologies make them the ideal tool for use as a safety net throughout the food industry, improving product quality with minimal investment. The opportunity afforded through the utilization of sensing technologies, particularly in situ and safety analysis at all levels of the supply chain, as well as authenticity and quality analysis by consumers themselves, make food production sensors tools of the future.

The development and implementation of sensing technologies often requires the creation of mathematical models that can be used to quantify or monitor composition, contamination, fraud, etc. However, the importance of mathematical modelling is somehow ignored or underestimated. Sensing technologies have demonstrated that they can contribute to or can be used as tools in sustainable food systems, contributing to reducing the cost and time of analysis. As these techniques are reagent-less and have low energy consumption, they are defined as environmentally friendly.

Despite the huge potential of sensing technologies as analytical methods, their use by the food industry will require further developments in R&D. The current global demands for fully nutritious, sustainable and safe foods are on top of the agenda for the modern food industry and, more importantly, the consumer. Regrettably, these questions are hampered by several overarching issues, including rising complexity in the supply chains, the effects of climate change (e.g. composition of raw materials and ingredients), a growing ageing population, security issues (e.g. contamination, fraud, traceability, origin, waste) and continuous changes in consumer patterns or choices for sustainable and safe food ingredients and products.

Finally, the utilization of these sensing technologies will allow the extraction of knowledge derived from interrogation of the food system analysis (see Figure 1.2). This new approach will allow the development of novel solutions that will challenge as well as increase the efficiency, sustainability, flexibility, agility and resilience of the food supply and value chains, from the farmer to the consumer (e.g. a farm to fork approach).

Figure 1.2

The utilization of sensing technologies and the creation of knowledge in the modern food industry.

Figure 1.2

The utilization of sensing technologies and the creation of knowledge in the modern food industry.

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AI

Artificial intelligence

ANN

Artificial neural networks

APCI-MS

Atmospheric pressure chemical ionization mass spectrometry

COP

Conducting organic polymers

EN

Electronic nose

EEG

Electroencephalography

FDA

Factorial discriminate analysis

GC

Gas chromatography

GC-MS

Gas chromatography mass spectrometry

HPLC

High-performance liquid chromatography

IMS

Ion mobility spectrometry

ISFET

Ion-sensitive field effect transistors

k-NN

K-Nearest neighbours

LC

Liquid chromatography

LDA

Linear discriminant analysis

MIPS

Molecularly imprinted polymers

MIR

Mid infrared

ML

Machine learning

MOX

Metal oxide sensors

MOSFET

Metal oxide semiconductor field effect transistor

MRI

Magnetic resonance imaging

MS

Mass spectrometry

NIR

Near infrared

PCA

Principal component analysis

PLS-DA

Partial least squares discriminant analysis

PTR-MS

Proton transfer reaction mass spectrometry

QDA

Quadratic discriminant analysis

SERS

Surface-enhanced Raman spectroscopy

SVM

Support vector machines

SIMCA

Soft independent modelling of class analogy

THz

Terahertz

1.
Wu
 
M. Y.-C.
Hsu
 
M.-Y.
Chen
 
S.-J.
Hwang
 
D.-K.
Yen
 
T.-H.
Cheng
 
C.-M.
Point-of-care detection devices for food safety monitoring: Proactive disease prevention
Trends Biotechnol.
2017
, vol. 
35
 
4
(pg. 
288
-
300
)
2.
Yeni
 
F.
Acar
 
S.
Polat
 
Ö. G.
Soyer
 
Y.
Alpas
 
H.
Rapid and standardized methods for detection of foodborne pathogens and mycotoxins on fresh produce
Food Control
2014
(pg. 
40359
-
40367
)
3.
Bahadır
 
E. B.
Sezgintürk
 
M. K.
Applications of commercial biosensors in clinical, food, environmental, and biothreat/biowarfare analyses
Anal. Biochem.
2015
(pg. 
478107
-
478120
)
4.
Perumal
 
V.
Hashim
 
U.
Advances in biosensors: Principle, architecture and applications
J. Appl. Biomed.
2014
, vol. 
12
 
1
(pg. 
1
-
15
)
5.
Chapman
 
J.
Power
 
A.
Kiran
 
K.
Chandra
 
S.
New twists in the plot: Recent advances in electrochemical genosensors for disease screening
J. Electrochem. Soc.
2017
, vol. 
164
 
13
(pg. 
B665
-
B673
)
6.
Korotkaya
 
E.
Biosensors: Design, classification, and applications in the food industry
Foods Raw Mater.
2014
, vol. 
2
 
2
7.
McGrath
 
T. F.
Elliott
 
C. T.
Fodey
 
T. L.
Biosensors for the analysis of microbiological and chemical contaminants in food
Anal. Bioanal. Chem.
2012
, vol. 
403
 
1
(pg. 
75
-
92
)
8.
Justino
 
C. I. L.
Freitas
 
A. C.
Pereira
 
R.
Duarte
 
A. C.
Rocha Santos
 
T. A. P.
Recent developments in recognition elements for chemical sensors and biosensors
TrAC, Trends Anal. Chem.
2015
, vol. 
68
 (pg. 
2
-
17
)
9.
Ampuero
 
S.
Bosset
 
J.
The electronic nose applied to dairy products: a review
Sens. Actuators, B
2003
, vol. 
94
 
1
(pg. 
1
-
12
)
10.
Bartlett
 
P. N.
Elliott
 
J. M.
Gardner
 
J. W.
Electronic noses and their application in the food industry
Food Technol
1997
, vol. 
51
 (pg. 
44
-
48
)
11.
Deisingh
 
A. K.
Stone
 
D. C.
Thompson
 
M.
Applications of electronic noses and tongues in food analysis
Int. J. Food Sci. Technol.
2004
, vol. 
39
 
6
(pg. 
587
-
604
)
12.
Loutfi
 
A.
Coradeschi
 
S.
Mani
 
G. K.
Shankar
 
P.
Rayappan
 
J. B. B.
Electronic noses for food quality: A review
J. Food Eng.
2015
, vol. 
144
 (pg. 
103
-
111
)
13.
Röck
 
F.
Barsan
 
N.
Weimar
 
U.
Electronic nose: current status and future trends
Chem. Rev.
2008
, vol. 
108
 
2
(pg. 
705
-
725
)
14.
Cozzolino
 
D.
Food for thought: the digital disruption and the future of food production
Curr. Res. Nutr. Food Sci.
2019
, vol. 
7
 (pg. 
607
-
609
)
15.
Truong
 
V. K.
Dupont
 
M.
Elbourne
 
A.
Gangadoo
 
S.
Rajapaksha Pathirannahalage
 
P.
Cheeseman
 
S.
Chapman
 
J.
Cozzolino
 
D.
From academia to reality check: a theoretical framework on the use of chemometric
Foods
2019
, vol. 
8
 (pg. 
1
-
10
)
16.
Vigneshvar
 
S.
Sudhakumari
 
C. C.
Senthilkumaran
 
B.
Prakash
 
H.
Recent advances in biosensor technology for potential applications – an overview
Front. Bioeng. Biotechnol.
2016
, vol. 
4
 (pg. 
11
-
18
)
17.
Pilolli
 
R.
Monaci
 
L.
Visconti
 
A.
Advances in biosensor development based on integrating nanotechnology and applied to food-allergen management
TrAC, Trends Anal. Chem.
2013
(pg. 
4712
-
4726
)
18.
Qin
 
C.
Tao
 
L.
Phang
 
Y. H.
Zhang
 
C.
Chen
 
S. Y.
Zhang
 
P.
Tan
 
Y.
Jiang
 
Y. Y.
Chen
 
Y. Z.
The assessment of the readiness of molecular biomarker-based mobile health technologies for healthcare applications
Sci. Rep.
2015
, vol. 
5
 pg. 
17854
 
19.
Sekhon
 
B. S.
Nanotechnology in agri-food production: An overview
Nanotechnol., Sci. Appl.
2014
, vol. 
7
 (pg. 
31
-
53
)
20.
Guadarrama
 
A.
Fernandez
 
J. A.
Iñiguez
 
M.
Souto
 
J.
de Saja
 
J. A.
Discrimination of wine aroma using an array of conducting polymer sensors in conjunction with solid-phase micro-extraction (SPME) technique
Sens. Actuators
2001
, vol. 
77
 (pg. 
401
-
408
)
21.
Turner
 
A. P. F.
Biosensors: Sense and sensibility
Chem. Soc. Rev.
2013
, vol. 
42
 (pg. 
3184
-
3196
)
22.
Nychas
 
G.-J. E.
Panagou
 
E. Z.
Mohareb
 
F.
Novel approaches for food safety management and communication
Curr. Opin. Food Sci.
2016
(pg. 
1213
-
1220
)
23.
Deisingh
 
A. K.
Stone
 
D. C.
Thompson
 
M.
Applications of electronic noses and tongues in food analysis
Int. J. Food Sci. Technol.
2004
, vol. 
39
 (pg. 
587
-
604
)
24.
Gutierrez
 
A.
Burgos
 
J. A.
Garcera
 
C.
Padilla
 
A. I.
Zarzo
 
M.
Chirivella
 
C.
Ruiz
 
M. L.
Molto
 
E.
Optimization of an aroma sensor for assessing grape quality for wine making
Span. J. Agric. Res.
2007
, vol. 
5
 (pg. 
157
-
163
)
25.
Röck
 
F.
Barsan
 
N.
Weimar
 
U.
Electronic nose: current status and future trends
Chem. Rev.
2008
, vol. 
108
 (pg. 
705
-
725
)
26.
Cacciola
 
F.
Dugo
 
P.
Mondello
 
L.
Multidimensional liquid chromatography in food analysis
TrAC, Trends Anal. Chem.
2017
, vol. 
96
 (pg. 
116
-
123
)
27.
Thévenot
 
D. R.
Toth
 
K.
Durst
 
R. A.
Wilson
 
G. S.
Electrochemical biosensors: Recommended definitions and classification
Biosens. Bioelectron.
2001
, vol. 
16
 
1
(pg. 
121
-
131
)
28.
Yoo
 
E.-H.
Lee
 
S.-Y.
Glucose biosensors: An overview of use in clinical practice
Sensors
2010
, vol. 
10
 
5
(pg. 
4558
-
4576
)
29.
Bazin
 
I.
Tria
 
S. A.
Hayat
 
A.
Marty
 
J.-L.
New biorecognition molecules in biosensors for the detection of toxins
Biosens. Bioelectron.
2017
(pg. 
87285
-
87298
)
30.
Gaudin
 
V.
Advances in biosensor development for the screening of antibiotic residues in food products of animal origin – a comprehensive review
Biosens. Bioelectron.
2017
(pg. 
90363
-
90377
)
31.
Velusamy
 
V.
Arshak
 
K.
Korostynska
 
O.
Oliwa
 
K.
Adley
 
C.
An overview of foodborne pathogen detection: In the perspective of biosensors
Biotechnol. Adv.
2010
, vol. 
28
 
2
(pg. 
232
-
254
)
32.
Bettazzi
 
F.
Marrazza
 
G.
Minunni
 
M.
Palchetti
 
I.
Scarano
 
S.
Biosensors and related bioanalytical tools
Compr. Anal. Chem.
2017
, vol. 
77
 (pg. 
1
-
33
)
33.
Ali
 
J.
Najeeb
 
J.
Ali
 
M. A.
Aslam
 
M. F.
Raza
 
A.
Biosensors: Their fundamentals, designs, types and most recent impactful applications: A review
J. Biosens. Bioelectron.
2017
, vol. 
8
 (pg. 
235
-
244
)
34.
Bhalla
 
N.
Jolly
 
P.
Formisano
 
N.
Estrela
 
P.
Introduction to biosensors
Essays Biochem.
2016
, vol. 
60
 
1
(pg. 
1
-
15
)
35.
Sharma
 
A.
Goud
 
K. Y.
Hayat
 
A.
Bhand
 
S.
Marty
 
J. L.
Recent advances in electrochemical-based sensing platforms for aflatoxins detection
Chemosensors
2017
, vol. 
5
 (pg. 
1
-
15
)
36.
Singh
 
R.
Mukherjee
 
M. D.
Sumana
 
G.
Gupta
 
R. K.
Sood
 
S.
Malhotra
 
B. D.
Biosensors for pathogen detection: A smart approach towards clinical diagnosis
Sens. Actuators, B
2014
(pg. 
197385
-
197404
)
37.
Rotariu
 
L.
Lagarde
 
F.
Jaffrezic-Renault
 
N.
Bala
 
C.
Electrochemical biosensors for fast detection of food contaminants – trends and perspective
TrAC, Trends Anal. Chem.
2016
(pg. 
7980
-
7987
)
38.
Andjelković
 
U.
Gavrović-Jankulović
 
M.
Martinović
 
T.
Josić
 
D.
Omics methods as a tool for investigation of food allergies
TrAC, Trends Anal. Chem.
2017
, vol. 
96
 (pg. 
105
-
117
)
39.
T.
Lavecchia
,
A.
Tibuzzi
and
M. T.
Giardi
,
Biosensors for functional food safety and analysis
, ed. M. T. Giardi, G. Rea and B. Berra,
Springer US
,
Boston, MA
,
2010
, pp. 267–281
40.
Adley
 
C. C.
Past, present and future of sensors in food production
Foods
2014
, vol. 
3
 
3
(pg. 
491
-
510
)
41.
Jayas
 
D. S.
The role of sensors and bio-imaging in monitoring food quality
Resour. Mag.
2017
pg. 
12
 
42.
Moreb
 
N. A.
Priyadarshini
 
A.
Jaiswal
 
A. K.
Knowledge of food safety and food handling practices amongst food handlers in the republic of ireland
Food Control
2017
(pg. 
80341
-
80349
)
43.
Thakur
 
M. S.
Ragavan
 
K. V.
Biosensors in food processing
J. Food Sci. Technol.
2013
, vol. 
50
 
4
(pg. 
625
-
641
)
44.
Poltronieri
 
P.
Mezzolla
 
V.
Primiceri
 
E.
Maruccio
 
G.
Biosensors for the detection of food pathogens
Foods
2014
, vol. 
3
 
3
(pg. 
511
-
526
)
45.
Zeng
 
Y.
Zhu
 
Z.
Du
 
D.
Lin
 
Y.
Nanomaterial-based electrochemical biosensors for food safety
J. Electroanal. Chem.
2016
(pg. 
781147
-
781154
)
46.
Luo
 
C.
Lei
 
Y.
Yan
 
L.
Yu
 
T.
Li
 
Q.
Zhang
 
D.
Ding
 
S.
Ju
 
H.
A rapid and sensitive aptamer-based electrochemical biosensor for direct detection of escherichia coli O111
Electroanalysis
2012
, vol. 
24
 
5
(pg. 
1186
-
1191
)
47.
Ma
 
X.
Jiang
 
Y.
Jia
 
F.
Yu
 
Y.
Chen
 
J.
Wang
 
Z.
An aptamer-based electrochemical biosensor for the detection of salmonella
J. Microbiol. Methods
2014
(pg. 
9894
-
9898
)
48.
Cho
 
I.-H.
Radadia
 
A. D.
Farrokhzad
 
K.
Ximenes
 
E.
Bae
 
E.
Singh
 
A. K.
Oliver
 
H.
Ladisch
 
M.
Bhunia
 
A.
Applegate
 
B.
et al., Nano/micro and spectroscopic approaches to food pathogen detection
Annu. Rev. Anal. Chem.
2014
, vol. 
7
 
1
(pg. 
65
-
88
)
49.
Warriner
 
K.
Reddy
 
S. M.
Namvar
 
A.
Neethirajan
 
S.
Developments in nanoparticles for use in biosensors to assess food safety and quality
Trends Food Sci. Technol.
2014
, vol. 
40
 
2
(pg. 
183
-
199
)
50.
Derkus
 
B.
Applying the miniaturization technologies for biosensor design
Biosens. Bioelectron.
2016
(pg. 
79901
-
79913
)
51.
Verma
 
M. L.
Nanobiotechnology advances in enzymatic biosensors for the agri-food industry
Environ. Chem. Lett.
2017
(pg. 
1
-
6
)
52.
Zhu
 
C.
Yang
 
G.
Li
 
H.
Du
 
D.
Lin
 
Y.
Electrochemical sensors and biosensors based on nanomaterials and nanostructures
Anal. Chem.
2015
, vol. 
87
 
1
(pg. 
230
-
249
)
53.
Anu Bhushani
 
J.
Anandharamakrishnan
 
C.
Electrospinning and electrospraying techniques: Potential food based applications
Trends Food Sci. Technol.
2014
, vol. 
38
 
1
(pg. 
21
-
33
)
54.
Eivazzadeh-Keihan
 
R.
Pashazadeh
 
P.
Hejazi
 
M.
de la Guardia
 
M.
Mokhtarzadeh
 
A.
Recent advances in nanomaterial-mediated bio and immune sensors for detection of aflatoxin in food products
TrAC, Trends Anal. Chem.
2017
(pg. 
87112
-
87128
)
55.
Rai
 
M.
Jogee
 
P. S.
Ingle
 
A. P.
Emerging nanotechnology for detection of mycotoxins in food and feed
Int. J. Food Sci. Nutr.
2015
, vol. 
66
 
4
(pg. 
363
-
370
)
56.
Barsan
 
M. M.
Ghica
 
M. E.
Brett
 
C. M. A.
Electrochemical sensors and biosensors based on redox polymer/carbon nanotube modified electrodes: A review
Anal. Chim. Acta
2015
(pg. 
8811
-
8823
)
57.
Yang
 
N.
Chen
 
X.
Ren
 
T.
Zhang
 
P.
Yang
 
D.
Carbon nanotube based biosensors
Sens. Actuators, B
2015
(pg. 
207690
-
207715
)
58.
Sharma
 
T. K.
Ramanathan
 
R.
Rakwal
 
R.
Agrawal
 
G. K.
Bansal
 
V.
Moving forward in plant food safety and security through nanobiosensors: Adopt or adapt biomedical technologies?
Proteomics
2015
, vol. 
15
 
10
(pg. 
1680
-
1692
)
59.
Pashazadeh
 
P.
Mokhtarzadeh
 
A.
Hasanzadeh
 
M.
Hejazi
 
M.
Hashemi
 
M.
De La Guardia
 
M.
Nano-materials for use in sensing of salmonella infections: Recent advances
Biosens. Bioelectron.
2017
(pg. 
871050
-
871064
)
60.
Sharma
 
R.
Ragavan
 
K. V.
Thakur
 
M. S.
Raghavarao
 
K. S. M. S.
Recent advances in nanoparticle based aptasensors for food contaminants
Biosens. Bioelectron.
2015
(pg. 
74612
-
74627
)
61.
Luka
 
G.
Ahmadi
 
A.
Najjaran
 
H.
Alocilja
 
E.
DeRosa
 
M.
Wolthers
 
K.
Malki
 
A.
Aziz
 
H.
Althani
 
A.
Hoorfar
 
M.
Microfluidics integrated biosensors: A leading technology towards lab-on-a-chip and sensing applications
Sensors
2015
, vol. 
15
 (pg. 
1
-
15
)
62.
Rackus
 
D. G.
Shamsi
 
M. H.
Wheeler
 
A. R.
Electrochemistry, biosensors and microfluidics: A convergence of fields
Chem. Soc. Rev.
2015
, vol. 
44
 
15
(pg. 
5320
-
5340
)
63.
Huang
 
Y.
Shi
 
Y.
Yang
 
H. Y.
Ai
 
Y.
A novel single-layered mos2 nanosheet based microfluidic biosensor for ultrasensitive detection of DNA
Nanoscale
2015
, vol. 
7
 
6
(pg. 
2245
-
2249
)
64.
Kim
 
G.
Moon
 
J.-H.
Moh
 
C.-Y.
Lim
 
J.-G.
A microfluidic nano-biosensor for the detection of pathogenic salmonella
Biosens. Bioelectron.
2015
, vol. 
67
 
Supplement C
(pg. 
243
-
247
)
65.
Bec
 
K. B.
Grabska
 
J.
Huck
 
C. W.
Review near-infrared spectroscopy in bio-applications
Molecules
2020
, vol. 
25
 pg. 
2948
 
66.
Bec
 
K. B.
Huck
 
C. W.
Breakthrough potential in near-infrared spectroscopy: spectra simulation. A review of recent developments
Front. Chem.
2019
, vol. 
7
 (pg. 
48
-
53
)
67.
Cattaneo
 
T. M. P.
Stellari
 
A.
Review: NIR spectroscopy as a suitable tool for the investigation of the horticultural field
Agronomy
2019
, vol. 
9
 pg. 
503
 
68.
Pasquini
 
C.
Near infrared spectroscopy: A mature analytical technique with new perspectives—A review
Anal. Chim. Acta
2018
, vol. 
1026
 (pg. 
8
-
36
)
69.
Walsh
 
K. B.
McGlone
 
V. A.
Hanc
 
D. H.
The uses of near infra-red spectroscopy in postharvest decision support: A review
Postharvest Biol. Technol.
2020
, vol. 
163
 pg. 
111139
 
70.
Cozzolino
 
D.
Roberts
 
J. J.
Applications and developments on the use of vibrational spectroscopy imaging for the analysis, monitoring and characterisation of crops and plants
Molecules
2016
, vol. 
21
 (pg. 
755
-
763
)
71.
Nicolai
 
B. M.
Beullens
 
K.
Bobelyn
 
E.
Peirs
 
A.
Saeys
 
W.
Theron
 
K. I.
Lammertyn
 
J.
Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review
Postharvest Biol. Technol.
2007
, vol. 
46
 (pg. 
99
-
118
)
72.
Saeys
 
W.
Do Trong
 
N. N.
Van Beers
 
R.
Nicolai
 
B. M.
Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: A review
Postharvest Biol. Nanotechnol.
2019
, vol. 
158
 pg. 
110981
 
73.
Crocombe
 
R. A.
Portable spectroscopy
Appl. Spectrosc.
2018
, vol. 
72
 (pg. 
1701
-
1751
)
74.
Naseer
 
K.
Ali
 
S.
Qazi
 
J.
ATR-FTIR spectroscopy as the future of diagnostics: a systematic review of the approach using bio-fluids
Appl. Spectrosc. Rev.
2021
, vol. 
56
 
2
(pg. 
85
-
97
)
75.
Cortes
 
V.
Blasco
 
J.
Aleixos
 
N.
Cubero
 
S.
Talensa
 
P.
Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review
Trends Food Sci. Technol.
2019
, vol. 
85
 (pg. 
138
-
148
)
76.
Kaavya
 
R.
Pandiselvam
 
R.
Mohammed
 
M.
Dakshayani
 
R.
Kothakota
 
A.
Ramesh
 
S. V.
Cozzolino
 
D.
Ashokkumar
 
C.
Application of infrared spectroscopy techniques for assessment of quality and safety in spices – state-of-the-art and future trends spectroscopy techniques- an emerging tool for spices and herbs authentication
Appl. Spectrosc. Rev.
2021
, vol. 
55
 (pg. 
593
-
611
)
77.
Karoui
 
R.
Downey
 
G.
Blecker
 
Ch.
Mid-Infrared Spectroscopy Coupled with Chemometrics: A Tool for the Analysis of Intact Food Systems and the Exploration of Their Molecular Structure-Quality Relationships – A Review
Chem. Rev.
2010
, vol. 
110
 (pg. 
6144
-
6168
)
78.
Sorak
 
D.
Herberholz
 
L.
Iwascek
 
S.
Altinpinar
 
S.
Pfeifer
 
F.
Siesler
 
H. W.
New developments and applications of handheld Raman, mid-infrared, and near infrared spectrometers
Appl. Spectrosc. Rev.
2012
, vol. 
47
 (pg. 
83
-
115
)
79.
Jones
 
R. R.
Hooper
 
D. C.
Zhang
 
L.
Wolverson
 
D.
Valev
 
V.
Raman Techniques: Fundamentals and Frontiers
Nanoscale Res. Lett.
2019
, vol. 
14
 pg. 
231
 
80.
Afsah-Hejri
 
L.
Hajeb
 
P.
Ara
 
P.
Ehsan
 
R. J.
A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging
Compr. Rev. Food Sci. Food Saf.
2019
, vol. 
18
 pg. 
1563
 
81.
Amodio
 
M. L.
Chaudhry
 
M. A.
Colelli
 
G.
Spectral and hyperspectral technologies as an additional tool to increase information on quality and origin of horticultural crops
Agronomy
2020
, vol. 
10
 pg. 
7
 
82.
Feng
 
Y. Z.
Sun
 
D. W.
Application of hyperspectral imaging in food safety inspection and control: a review
Crit. Rev. Food Sci. Nutr.
2012
, vol. 
52
 (pg. 
1039
-
1058
)
83.
Manley
 
M.
Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials
Chem. Soc. Rev.
2014
, vol. 
43
 pg. 
8600
 
84.
Baiano
 
A.
Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: a review
J. Food Eng.
2017
, vol. 
214
 (pg. 
10
-
15
)
85.
Saha
 
D.
Manickavasagan
 
A.
Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review
Curr. Res. Food Sci.
2021
, vol. 
4
 (pg. 
28
-
44
)
86.
Amigo
 
J. M.
Martí
 
I.
Gowen
 
A.
Hyperspectral imaging and chemometrics: a perfect combination for the analysis of food structure, composition and quality
Data Handl. Sci. Technol.
2013
, vol. 
28
 (pg. 
343
-
370
)
87.
Bartlett
 
P. N.
Elliot
 
J. E.
Gardner
 
J. W.
Electronic noses and their application in the food industry
Food Technol.
1997
, vol. 
51
 (pg. 
44
-
48
)
88.
Linforth
 
R. S. T.
Developments in instrumental techniques for food flavour evaluation: future prospects
J. Sci. Food Agric.
2000
, vol. 
80
 
14
(pg. 
2044
-
2048
)
89.
Ross
 
C. F.
Sensory science at the human–machine interface
Trends Food Sci. Technol.
2009
, vol. 
20
 
2
(pg. 
63
-
72
)
90.
Pionnier
 
E.
Chabanet
 
C.
Mioche
 
L.
Le Quéré
 
J.-L.
Salles
 
C.
1. In vivo aroma release during eating of a model cheese: relationships with oral parameters
J. Agric. Food Chem.
2004
, vol. 
52
 
3
(pg. 
557
-
564
)
91.
T. C.
Pearce
,
S. S.
Schiffman
,
H. T.
Nagle
and
J. W.
Gardner
,
Handbook of Machine Olfaction: Electronic Nose Technology
,
Wiley-VCH Verlag GmbH & Co. KGaA
,
2006
92.
John Lewis
 
Z. Z.
George
 
B.
Zoltan
 
G.
Zoltan
 
K.
Emerging trends of advanced sensor based instruments for meat, poultry and fishquality– a review
Crit. Rev. Food Sci. Nutr.
2020
, vol. 
60
 
20
(pg. 
3443
-
3460
)
93.
Martí
 
M. P.
Busto
 
O.
Guasch
 
J.
Boqué
 
R.
Electronic noses in the quality control of alcoholic beverages
TrAC, Trends Anal. Chem.
2005
, vol. 
24
 
1
(pg. 
57
-
66
)
94.
Mielle
 
P.
Electronic noses: Towards the objective instrumental characterization of food aroma
Trends Food Sci. Technol.
1996
, vol. 
7
 
12
(pg. 
432
-
438
)
95.
Berna
 
A.
Trowell
 
S.
Cynkar
 
W.
Cozzolino
 
D.
Comparison of metal oxide based-electronic nose and mass spectrometry-based electronic nose to predict red wine spoilage
J. Agric. Food Chem.
2008
, vol. 
56
 (pg. 
3238
-
3244
)
96.
Gliszczy
 
A.
Electronic nose as a tool for monitoring the authenticity of food. A Review
Food Anal. Methods
2017
, vol. 
10
 (pg. 
1800
-
1816
)
97.
Majchrzak
 
T.
Wojnowski
 
W.
Dymerski
 
T.
Gębicki
 
J.
Namieśnik
 
J.
Electronic noses in classification and quality control of edible oils: A review
Food Chem.
2018
, vol. 
246
 (pg. 
192
-
201
)
98.
Gallo
 
M.
Ferranti
 
P.
The evolution of analytical chemistry methods in foodomics
J. Chromatogr. A
2016
, vol. 
1428
 (pg. 
3
-
15
)
99.
Ghasemi-Varnamkhasti
 
M.
Lozano
 
J.
Electronic nose as an innovative measurement system for the quality assurance and control of bakery products: a review
Eng. Agric. Environ. Food
2016
, vol. 
9
 
4
(pg. 
365
-
374
)
100.
Matindoust
 
S.
Baghaei-Nejad
 
M.
Zou
 
Z.
Zheng
 
L.-R.
Food quality and safety monitoring using gas sensor array in intelligent packaging
Sens. Rev.
2016
, vol. 
36
 
2
(pg. 
169
-
183
)
101.
Kukla
 
A.
Pavluchenko
 
A.
Shirshov
 
Y. M.
Konoshchuk
 
N.
Posudievsky
 
O. Y.
Application of sensor arrays based on thin films of conducting polymers for chemical recognition of volatile organic solvents
Sens. Actuators, B
2009
, vol. 
135
 
2
(pg. 
541
-
551
)
102.
Sanaeifar
 
A.
ZakiDizaji
 
H.
Jafari
 
A.
de la Guardia
 
M.
Early detection of contamination and defect in foodstuffs by electronic nose: A review
TrAC, Trends Anal. Chem.
2017
, vol. 
97
 (pg. 
257
-
271
)
103.
Suppes
 
G. M.
Deore
 
B. A.
Freund
 
M. S.
Porous conducting polymer/heteropolyoxometalate hybrid material for electrochemical supercapacitor applications
Langmuir
2008
, vol. 
24
 
3
(pg. 
1064
-
1069
)
104.
Wilson
 
A.
Baietto
 
M.
Applications and advances in electronic-nose technologies
Sensors
2009
, vol. 
9
 
7
(pg. 
5099
-
5148
)
105.
Wilson
 
D. M.
Hoyt
 
S.
Janata
 
J.
Booksh
 
K.
Obando
 
L.
Chemical sensors for portable, handheld field instruments
IEEE Sens. J.
2001
, vol. 
1
 
4
(pg. 
256
-
274
)
106.
Escuder-Gilabert
 
L.
Peris
 
M.
Review: Highlights in recent applications of electronic tongues in food analysis
Anal. Chim. Acta
2010
, vol. 
665
 
1
(pg. 
15
-
25
)
107.
Rita
 
A.
Rosa
 
D.
Leone
 
F.
Cheli
 
F.
Chiofalo
 
V.
Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment e A review
J. Food Eng.
2017
, vol. 
210
 (pg. 
62
-
75
)
108.
Winquist
 
F.
Voltammetric electronic tongues – Basic principles and applications
Microchim. Acta
2008
, vol. 
163
 (pg. 
3
-
10
)
109.
Valle
 
M.
Mimendia
 
A.
Gutie
 
J. M.
A review of the use of the potentiometric electronic tongue in the monitoring of environmental systems
Environ. Modell. Software
2010
, vol. 
25
 (pg. 
1023
-
1030
)
110.
Ciosek
 
P.
Wroblewski
 
W.
Potentiometric electronic tongues for foodstuff and biosample recognition—An overview
Sensors
2011
, vol. 
11
 
5
(pg. 
4688
-
4701
)
111.
Cozzolino
 
D.
The sample, the spectra and the maths – the critical pillars in the development of robust and sound vibrational spectroscopy applications
Molecules
2020
, vol. 
25
 pg. 
3674
 
112.
Bureau
 
S.
Cozzolino
 
D.
Clark
 
C. J.
Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review
Postharvest Biol. Technol.
2019
, vol. 
148
 (pg. 
1
-
14
)
113.
Agelet
 
L.
Hurburgh
 
C. H.
A Tutorial on Near Infrared Spectroscopy and its’ Calibration
Crit. Rev. Anal. Chem.
2010
, vol. 
40
 (pg. 
246
-
260
)
114.
Bro
 
R.
Smilde
 
A. K.
Principal component analysis
Anal. Methods
2014
, vol. 
6
 (pg. 
2812
-
2831
)
115.
Szymańska
 
E.
Gerretzen
 
J.
Engel
 
J.
Geurts
 
B.
Blanchet
 
L.
Buydens
 
L. M.
Chemometrics and qualitative analysis have a vibrant relationship
TrAC, Trends Anal. Chem.
2015
, vol. 
69
 (pg. 
34
-
51
)
116.
Szymanska
 
E.
Modern data science for analytical chemical data: a comprehensive review
Anal. Chim. Acta
2018
, vol. 
1028
 (pg. 
1
-
10
)
117.
Callao
 
M. P.
Ruisánchez
 
I.
An overview of multivariate qualitative methods for food fraud detection
Food Control
2018
, vol. 
86
 (pg. 
283
-
293
)
118.
Chavas
 
J. P.
Nauges
 
C.
Uncertainty, learning and technology adoption in agriculture
Appl. Econ. Perspect. Policy
2020
, vol. 
42
 (pg. 
42
-
53
)
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