Detection Of Food Adulteration Research Articles

Overview of Food Adulteration: Its Definition, Causes, Impacts, and Detection

A tampered food, Food is any substance made up of carbohydrates, water, fats, and proteins that is consumed by humans and other animals to survive. It holds great significance for us. Nonetheless, it is not uncommon for food products to be tampered with, and dangerous situations can arise from poorly managed supply chains that handle perishable goods. Economic Customers who purchase and serve food are impacted in numerous ways; they might not receive the nutrients they are supposed to, contaminated food may be harmful to their health, and they might also suffer financial losses. So, every responsible individual, organization, and government should complete their role to protect the act of food adulteration and to reveal the recognized acts. Furthermore, there are numerous gaps in the literature that need to be filled by academics and researchers working in various research institutes. These gaps include the assessment of the situation, the identification of foods that are vulnerable to adulteration in the national context, the development and validation of detection methods, and much more. adulteration has long plagued the food business, negatively impacting both food quality and public health. The process of altering food by adding undesirable or dangerous components lowers its safety and quality, a practice known as food adulteration. Inadequate handling of this issue has led to the production of contaminated and dangerous food products, which has raised concerns throughout history. Food adulteration can harm our health and often does so without our knowledge. By addressing these issues, we may proactively work to create a better society. Food adulteration can involve adding, taking away, or replacing expensive food ingredients with less expensive (cheaper) ones in order to get unfair financial advantage. The manufacturer may profit financially from this act of food fraud, but the eventual consumers of the goods suffer losses. A variety of approaches, such as physical, chemical, biological, and other methods, are used to detect food adulteration. However, detecting adulterants can be hard when the adulterants closely mirror the food’s physiochemical properties. The right of consumers to safe and high-quality food is undermined by adulteration, hence it is imperative that people, groups, and governments take action to stop these practices.

Assess the knowledge regarding food adulteration among house wife: A study on berhampur city of Ganjam district, Odisha

Adulteration of food has occurred if it contains any substance injurious to health; it contains any filthy, putrid or decomposed substance; it is prepared, handled or stored under unsanitary conditions; diseased animals have been used in preparation; the container is made of a poisonous substance which will render the contents harmful; contains colours and preservatives other than those permitted by law; it contains pesticide residues or additives not recognised as safe etc. foods may be adulterated either intentional or incidental at all stages from production to selling. Food is adulterated if its quality is lowered or affected by the addition of substances which are injurious to health or by the removal of substances which are nutritious. The present study focuses on the awareness of the housewives among the consumers on food adulterants and to find out the extent of adulteration of the food stuffs. This study was conducted to assess the knowledge about food adulteration among 100 housewives in selected areas, Berhampur. A survey was conducted to collect the information through interview method. They must be well acquainted with the physical food characteristics such as appearance, colour, shape, texture, touch, flavour, taste etc. The urban and rural women are using the home test method for detecting food adulteration. Some urban house wives are having knowledge about food law and the rest of the housewife’s are not aware about food law. The rural housewife’s are not having any knowledge regarding food law, food standard and consumer protection Act. It can be concluded that some women are not having any knowledge regarding different adulteration programme. So, the government and people should promote the different adulteration programme. The media should take a great role to aware women in an interesting and attractive manner. Hence, it is essential to create community awareness and take interventional measures for the health and wellbeing of the community

Quantitative Analysis of High-Price Rice Adulteration Based on Near-Infrared Spectroscopy Combined with Chemometrics.

Near-infrared spectroscopy (NIRS) holds significant promise in detecting food adulteration due to its non-destructive, simple, and user-friendly properties. This study employed NIRS in conjunction with chemometrics to estimate the content of low-price rice flours (Nanjing, Songjing, Jiangxi silk, Yunhui) blended with high-price rice (Wuchang and Thai fragrant). Partial least squares regression (PLSR), support vector regression (SVR), and back-propagation neural network (BPNN) models were deployed to analyze the spectral data of adulterated samples and assess the degree of contamination. Various preprocessing techniques, parameter optimization strategies, and wavelength selection methods were employed to enhance model accuracy. With correlation coefficients exceeding 87%, the BPNN models exhibited high accuracy in estimating adulteration levels in high-price rice. The SPXY-SG-BPNN, SPXY-MMN-BPNN, KS-SNV-BPNN, and SPXY-SG-BPNN models showcased exceptional performance in discerning mixed Wuchang japonica, Thai fragrant indica, and Thai fragrant Yunhui rice. As shown above, NIRS demonstrated its potential as a rapid, non-destructive method for detecting low-price rice in premium rice blends. Future studies should be performed to concentrate on enhancing the models' versatility and practical applicability.

A novel recombinase polymerase amplification approach with propidium iodide reporter for rapid detection of porcine adulteration in food

The adulteration of food products with porcine components is among the most widespread concerns in the global food industry. Apart from health and religious reasons, being able to detect porcine adulteration is crucial to warrant fair trading and legal compliance. Current detection methods are often constrained by their low sensitivity and expensive equipment. In this work, a recombinase polymerase amplification (RPA) assay for the rapid and sensitive detection of porcine is constructed, with propidium iodide (PI) dye as the reporter molecule. Under the optimized conditions, the RPA-PI assay achieved a wide linear range of 0.005–100 ng/μL and a detection limit of 0.005 ng/μL for porcine DNA with no cross-reactivity. Furthermore, the application of the assay to actual food samples demonstrated a 100 % consistency with conventional PCR results. This developed assay not only provides a novel porcine adulteration detection platform with simpler and faster protocols, but it also holds the potential to be developed further for point-of-care authenticity testing.

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC‑HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Multiple Fano Resonance Modes based Plasmonic Refractive Index Sensor for Edible Oil Adulteration Detection

Surface plasmon polariton (SPP)-based plasmonic sensors are increasingly replacing traditional bulky sensors in refractive index sensing, biosensing, food adulteration detection, and other applications due to their unique optical features and different Metal-Insulator-Metal (MIM) topologies. The present research introduces a new design for a highly sensitive MIM waveguide-based refractive index sensor. This design incorporates an innovative combination of an inverted U-shaped rectangular split-ring resonator (RSRR), a semi-circle-split-ring resonator (SCSRR), and a stub. The sensor’s wave-guiding capabilities, such as electromagnetic wave transmission, field profile distribution, and sensitivity, are meticulously analyzed using a two-dimensional finite-element method. The key objective of this device is to identify slight differences in the refractive index by observing shifts in resonant wavelength through the light’s interaction with the plasmonic structure. Moreover, optimizing the geometric parameters significantly enhances the sensor’s performance. The optimized sensor achieves a maximum refractive index sensitivity of 2400 nm/RIU, a figure of merit (FOM) of 45.28, and a Q-factor of 39.86, as demonstrated by simulation results. Further investigations reveal the MIM structure’s capacity to detect adulteration in edible oils, underscoring the sensor’s adaptability and its potential for widespread biosensing applications.

Prospects of nanotechnology advances in food adulterant detection, spoilage detection, packaging and preservation applications

Abstract Food is essential for the development and essential functions of our lives. Our typical dietary intake consists of vegetables, fruits, legumes, pulses, grains, and other similar foods. These ingredients can be consumed in their raw form or transformed into delicious recipes. Food adulteration refers to the deliberate introduction of potentially hazardous substances into food or food products to contaminate or adulterate them. Prolonged storage of food without sufficient preservation leads to spoilage, which poses a health risk. Food undergoes degradation over an extended duration of storage, leading to the proliferation of germs on its surface. Nanotechnology revolutionizes food safety by employing precise detection and preservation methods. Nanoparticles play a crucial role in expediting the detection of adulterants, enabling the identification of even minute amounts of contaminants like pesticides or hazardous compounds. This, in turn, ensures the preservation of the quality and safety of food items. Nanosensors are used to identify spoilage by monitoring microbial activity or changes in pH levels, to prevent the intake of spoiled food. Nano-emulsions and coatings can extend the shelf life of products by establishing protective barriers that hinder the ingress of oxygen, moisture, and microbial growth. This process effectively preserves the freshness and nutritional value of the products. In this review, we have discussed the role of nanotechnology in detecting food adulteration, food spoilage, food preservation and food packaging techniques.

Magnetic-gold nanoparticle-mediated paper-based biosensor for highly sensitive colorimetric detection of food adulteration

Food adulteration presents a significant challenge due to the evasion of legal oversight and the difficulty of identification. Addressing this issue, there is an urgent need for on-site, rapid, visually based small-scale equipment, along with large-scale screening technology, to enable prompt results without providing opportunities for dishonest traders to react. Colorimetric reactions offer advantages in terms of speed, visualization, and miniaturization. However, there is a scarcity of suitable colorimetric reactions for food adulteration detection, and interference from colored food impurities and easily comparable color results affects accuracy. To overcome limitations, this study introduces a novel approach utilizing polydopamine magnetic nanoparticles to enrich DNA in food samples, effectively eliminating interfering components. By employing gold nanoparticles to generate magnetic-gold nanoparticles, a single magnetic bead achieves simultaneous enrichment, impurity removal, and detection. The use of paper-based biosensors and visualization equipment allows for the visualization and digital analysis of results, achieving a low detection limit of 4.59 nmol mL−1. The method also exhibits high accuracy and repeatability, with a RSD ranging from 1.6 % to 4.0 %. This innovative colorimetric method addresses the need for rapid, miniaturized, and large-scale detection, thus providing a solution for food adulteration challenges.

Application of high-performance thin layer chromatography mass spectrometry (HPTLC MS) in foodomics authenticity

BackgroundChemometric analysis is also a powerful tool that can be used in the preliminary stages of analytical methods optimization, but is also efficient in data processing. These new approaches may be the key to the analysis of proteins, carbohydrate, sphignolipids, polyphenols in different food components. Although there are challenges in identifying and annotating those compounds due to the limited availability of analytical standards and structural diversity. Scope and approachChromatograms are a useful tool for learning more about the chemical make-up of the food being tested. This information can occasionally be implicit or presented in a subtle way. Therefore, to extract it, chemometric tools and data mining techniques are needed. A chromatogram's fingerprint allows for the possibility of performing both identity and quality testing on food. This viewpoint aims to offer a current assessment of chromatographic fingerprinting technique in the area of food authentication. Additionally, this methodology's limitations, absence from official analytical methods, and future directions are discussed. Novel techniques have been employed in the past few decades, ranging from high-pressure thin liquid chromatography (HPTLC) to mass spectrometry (MS) and other spectroscopic methods. In the last decade research developments, and the application of analytical methods in qualitative and quantitative studies of food components and their adulterants is reviewed in the present work. High-pressure Thin liquid chromatography (HPTLC) is hyphenated with high resolution mass spectrometry is particularly addressed due to its applicability in the targeted/untargeted metabolomic analysis of all food components. Key findings and conclusionsIn the current study, the HPTLC-MS technique was used to examine food components and food adulteration. The HPTLC-MS hyphenated techniques were found to be an extremely helpful method for the detection of food adulterants as well as the various food components, such as protein, carbohydrates, lipids, essential oils, and flavonoids, which were found to be effective as medicinally.

Rapid identification of adulterated camel meat by MIL-101(Cr)-based fluorescent sensing platform: Surface potential adjustment to optimize detection performance

To identify adulterated camel meat in emerging markets, there is an urgent need for sensitive, rapid, and convenient detection methods. In this study, a novel fluorescent sensing platform was developed to effectively identify pork components adulterated in camel meat by regulating the surface charge properties of MIL-101(Cr). Our data demonstrated that the modification of polyethylenimine (PEI) had minimal impact on the crystal structure of MIL-101(Cr). However, it significantly affected the adsorption behavior of MIL-101(Cr) towards 6-carboxyfluorescein (FAM) labelled DNA, thereby influencing the response of fluorescence quenching-recovery. Under optimal conditions, the proposed turn-on fluorescent biosensor, based on PEI-MIL-101(Cr) exhibited high sensitivity with a detection limit of 42 pM. It also demonstrated excellent selectivity for specific target DNA, successfully detecting a pork fraction of 0.45 % (w/w) in camel meat. This study was aimed to provide a fresh perspective on the potential application of functional materials derived from metal-organic frameworks in detecting food adulteration.

Fish DNA Sensors for Authenticity Assessment-Application to Sardine Species Identification.

Food and fish adulteration is a major public concern worldwide. Apart from economic fraud, health issues are in the forefront mainly due to severe allergies. Sardines are one of the most vulnerable-to-adulteration fish species due to their high nutritional value. Adulteration comprises the substitution of one fish species with similar species of lower nutritional value and lower cost. The detection of adulteration, especially in processed fish products, is very challenging because the morphological characteristics of the tissues change, making identification by the naked eye very difficult. Therefore, new analytical methods and (bio)sensors that provide fast analysis with high specificity, especially between closely related fish species, are in high demand. DNA-based methods are considered as important analytical tools for food adulteration detection. In this context, we report the first DNA sensors for sardine species identification. The sensing principle involves species recognition, via short hybridization of PCR-amplified sequences with specific probes, capture in the test zone of the sensor, and detection by the naked eye using gold nanoparticles as reporters; thus, avoiding the need for expensive instruments. As low as 5% adulteration of Sardina pilchardus with Sardinella aurita was detected with high reproducibility in the processed mixtures simulating canned fish products.

Food adulteration: Causes, risks, and detection techniques-review.

Food adulteration is the intentional addition of foreign or inferior substances to original food products for a variety of reasons. It takes place in a variety of forms, like mixing, substitution, hiding poor quality in packaging material, putting decomposed food for sale, misbranding or giving false labels, and adding toxicants. Several analytical methods (such as chromatography, spectroscopy, electronic sensors) are used to detect the quality of foodstuffs. This review provides concise but detailed information to understand the scope and scale of food adulteration as a way to further detect, combat, and prevent future adulterations. The objective of this review was to provide a comprehensive overview of the causes, risks, and detection techniques associated with food adulteration. It also aimed to highlight the potential health risks posed by consuming adulterated food products and the importance of detecting and preventing such practices. During the review, books, regulatory guidelines, articles, and reports on food adulteration were analyzed critically. Furthermore, the review assessed key findings to present a well-rounded analysis of the challenges and opportunities associated with combating food adulteration. This review included different causes and health impacts of food adulteration. The analytical techniques for food adulteration detection have also been documented in brief. In addition, the review emphasized the urgency of addressing food adulteration through a combination of regulatory measures, technological advancements, and consumer awareness. In conclusion, food adulteration causes many diseases such as cancer, liver disease, cardiovascular disease, kidney disease, and nervous system-related diseases. So, ensuring food safety is the backbone of health and customer satisfaction. Strengthening regulations, taking legal enforcement action, enhancing testing, and quality control can prevent and mitigate the adulteration of food products. Moreover, proper law enforcement and regular inspection of food quality can bring about drastic changes.

Selected food items adulteration, their impacts on public health, and detection methods: A review.

Every living thing requires food to survive. Clean, fresh, and healthy foods are important to human health. Today, food is affected by various counterfeits. Adulteration of food is the intentional deterioration of the quality of food offered for sale by either the addition or substitution of an inferior substance or by the omission of a valuable ingredient. Economically motivated adulteration is the intentional adulteration of food for financial gain, and has enormous public health implications, making it an important issue in food science. Almost every food, including milk and dairy products, fats and oils, fruits and vegetables, grain foods, coffee, tea, honey, etc., is susceptible to adulteration. It is difficult to find food that is free from adulteration. Consumption of adulterated food contributes to numerous diseases in society, ranging from mild to life threatening. Therefore, detection of adulteration in food is essential to ensure the safety of the food we consume. To provide consumers with food that is free of adulterants, various detection methods such as physical, chemical, biochemical, and molecular techniques are used to identify adulterants in food. This review aims to provide up-to-date information on food adulteration, its impact on health, and the analytical techniques used to detect adulteration in food.

Real-Time Detection of Dynamic Affinity between Biomolecules Using Surface Plasmon Resonance (SPR) Technology.

Surface plasmon resonance (SPR) technology is a sensitive precise method for detecting viruses, pathogenic molecular proteins, and receptors, determining blood types, and detecting food adulteration, among other biomolecular detections. This technology allows for the rapid identification of potential binding between biomolecules, facilitating fast and user-friendly, non-invasive screening of various indicators without the need for labeling. Additionally, SPR technology facilitates real-time detection for high-throughput drug screening. In this program, the application field and basic principles of SPR technology are briefly introduced. The operation process is outlined in detail, starting with instrument calibration and basic system operation, followed by ligand capture and multi-cycle analysis of the analyte. The real-time curve and experimental results of binding quercetin and calycosin to KCNJ2 protein were elaborated upon. Overall, SPR technology provides a highly specific, simple, sensitive, and rapid method for drug screening, real-time detection of related pharmacokinetics, virus detection, and environmental and food safety identification.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods.

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Improving fraud detection in processed meats: A histology–PCR approach

Food fraud is a growing global concern that involves intentional actions aimed at deceiving consumers for financial gain by misrepresenting food products. Adulteration, substitution, mislabeling, and tampering are among the deceptive practices that can alter food products' nutritional value and composition. While various techniques, including mass spectrometry, enzyme immunoassay, histology, and DNA-based analysis, have been employed to detect food adulteration, each method has its limitations. This study presents a novel approach that combines PCR-based techniques with histology analysis to improve the detection of food adulteration in processed meats. The investigation involved the analysis of 105 randomly selected samples of sausages and beef hams in Iran using histological and PCR techniques. The results showed that 55 % of the beef ham samples and 66.15 % of the sausage samples contained unauthorized tissues. The multiplex PCR tests confirmed that 20 % of the beef ham samples and 0 % of the sausage samples had undeclared meat. Therefore, including histology as an additional method was necessary to accurately identify undeclared tissues from the same species. The findings suggest that the combined application of PCR-based and histology-based methods can effectively identify fraudulent practices within the food industry. This approach can enhance the detection of food adulteration and provide strong evidence for regulatory authorities to take necessary actions to protect consumer rights and promote food safety.

Wafer-scale fibrous SERS substrates allow label-free, portable detection of food adulteration and diagnosis of pesticide poisoning

Herein, a method for constructing three-dimensional (3D) fibrous surface-enhanced Raman scattering (SERS) substrates was developed. This strategy enables the wafer-scale, direct formation of monolayer gold nanoparticles (Au NPs) on filter paper. The process begins with coating of the fiber surface with perfluorooctyltrichlorosilane (FOS) to manipulate the surface energy. During subsequent sputter deposition, the large difference in surface energy between the Au atoms and the FOS-modified fibers leads to the thermodynamically driven formation of discrete Au NPs, thereby generating an abundance of hotspots. Moreover, the Au NP–decorated paper maintained its great hydrophobicity, condensing the analyte molecules nearby hot spots after solvent evaporation. In addition, the 3D fibrous structure provided a large detection space to efficiently utilize the large depth of field of a portable Raman spectrometer. These features resulted in the as-designed SERS paper having high sensitivity, with the enhancement factor reaching 1.63 × 108 and the detection limit reaching the nanomolar level when using a portable 785-nm Raman spectrometer. We used this approach for the trace-level, label-free detection of a diverse range of substances as well as their practical on-site detection in multiple-component systems, including illegal rhodamine B in commercial beverages and lethal paraquat in poisoning patients.

Application of Electronic Nose and Eye Systems for Detection of Adulteration in Olive Oil based on Chemometrics and Optimization Approaches

In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses. 

Improving the performance of the model developed from the classification of adulterated honey with different botanical origins based on near-Infrared hyperspectral imaging system and supervised classification algorithms

Honey is a high-value product that proscribes adulteration. Authentication of pure honey is an imperative act that can disregard the adverse impacts brought by fraudsters on honey producers and consumers. A reliable method that can detect food adulteration that is nondestructive, rapid, and efficient can help maintain product authenticity. This study investigated the potential of a near-infrared hyperspectral imaging (NIR-HSI) system combined with supervised classification techniques for the classification of honey samples from different botanical origins that were adulterated with sugar cane syrup at 20 % and 40 % w/w. Reflectance and transflectance modes of measurement were used to obtain honey spectral images from 864.53 nm to 1700 nm. Supervised classification algorithms, namely, stepwise discriminant analysis (SDA), support vector machine (SVM), and k-nearest neighbors (kNN) were used for the classification based on whole spectral data and feature selected spectral data. The best classification model from reflectance mode was observed from SDA based on a leave-one-out cross-validation (LOOCV) method with a test accuracy of 95 %. For transflectance mode, the classification model developed from SDA based on a LOOCV method provided better test set accuracy of 100 %. The classification performance levels of the models developed from SVM and kNN were lower with the use of the whole wavelength region. Moreover, the classification performance levels of SVM and kNN using the selected features by the SDA greatly increased, however, they were not as good as from using SDA alone. The classifier maps created using SDA and predicting the averaged spectra of all pixels in the image with 10-discriminant functions for each mode of measurement displayed a two-dimensional image that had a distinct colour pertaining to a particular class that could easily discriminate one class from another.