Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk

Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky-Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques

The nutritional status of rubber trees (Hevea brasiliensis) is inseparable from the production of natural rubber. Nitrogen (N) and potassium (K) levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree. Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly. However, high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model. A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech. Therefore, a less intensive and streamlined method, remining information from hyperspectral image data, was assessed. From this new perspective, a semisupervised learning (SSL) method and resampling techniques were employed for generating pseudo-labeling data and class rebalancing. Subsequently, a 5-classification spectral model of the N and K statuses of rubber leaves was established. The SSL model based on random forest classifiers and mean sampling techniques yielded optimal classification results both on imbalance/balance dataset (weighted average precision 67.8/78.6%, macro averaged precision 61.2/74.4%, and weighted recall 65.7/78.5% for the N status). All data and code could be viewed on the:Github https://github.com/WeehowTang/SSL-rebalancingtest. Ultimately, we proposed an efficient way to rapidly and accurately monitor the N and K levels in rubber leaves, especially in the scenario of small annotation and imbalance categories ratios.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

Geographical Classification of Saffron (Crocus Sativus L.) Using Total and Synchronous Fluorescence Combined with Chemometric Approaches

There is an increasing interest in food science for high-quality natural products with a distinct geographical origin, such as saffron. In this work, the excitation-emission matrix (EEM) and synchronous fluorescence were used for the first time to geographically discriminate between Moroccan saffron from Taroudant, Ouarzazate, and Azilal. Moreover, to differentiate between Afghan, Iranian, and Moroccan saffron, a unique fingerprint was assigned to each sample by visualizing the EEM physiognomy. Moreover, principal component analysis (LDA) and linear discriminant analysis (LDA) were successfully applied to classify the synchronous spectra of samples. High fluorescence intensities were registered for Ouarzazate and Taroudant saffron. Yet, the Azilal saffron was distinguished by its low intensities. Furthermore, Moroccan, Afghan, and Iranian saffron were correctly assigned to their origins using PCA and LDA for different offsets (Δλ) (20-250 nm) such that the difference in the fluorescence composition of the three countries' saffron was registered in the following excitation/emission ranges: 250-325 nm/300-480 nm and 360-425 nm/500-550 nm. These regions are characterized by the high polyphenolic content of Moroccan saffron and the important composition of Afghan saffron, including vitamins and terpenoids. However, weak intensities of these compounds were found in Iranian saffron. Furthermore, a substantial explained variance (97-100% for PC₁ and PC₂) and an important classification rate (70-90%) were achieved. Thus, the non-destructive applied methodology of discrimination was rapid, straightforward, reliable, and accurate.

Visible-Near-Infrared Spectroscopy and Chemometrics for Authentication Detection of Organic Soybean Flour

Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and non-organic soybean flours were captured using a Vis-NIR spectrometer at 350–1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA–linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability.

Enhanced characterization of breast cancer phenotypes using Raman micro-spectroscopy on stainless steel substrate

Biochemical insights into varying breast cancer (BC) phenotypes can provide a fundamental understanding of BC pathogenesis, while identifying novel therapeutic targets. Raman spectroscopy (RS) can gauge these biochemical differences with high specificity. For routine RS, cells are traditionally seeded onto calcium fluoride (CaF₂) substrates that are costly and fragile, limiting its widespread adoption. Stainless steel has been interrogated previously as a less expensive alternative to CaF₂ substrates, while reporting increased Raman signal intensity than the latter. We sought to further investigate and compare the Raman signal quality measured from stainless steel versus CaF₂ substrates by characterizing different BC phenotypes with altered human epidermal growth factor receptor 2 (HER2) expression. Raman spectra were obtained on stainless steel and CaF₂ substrates for HER2 negative cells - MDA-MB-231, MDA-MB-468 and HER2 overexpressing cells - AU565, SKBr3. Upon analyzing signal-to-noise ratios (SNR), stainless steel provided a stronger Raman signal, improving SNR by 119% at 1450 cm^-1 and 122% at 2925 cm^-1 on average compared to the CaF₂ substrate. Utilizing only 22% of laser power on sample relative to the CaF₂ substrate, stainless steel still yielded improved spectral characterization over CaF₂, achieving 96.0% versus 89.8% accuracy in BC phenotype discrimination and equivalent 100.0% accuracy in HER2 status classification. Spectral analysis further highlighted increased lipogenesis and altered metabolism in HER2 overexpressing cells, which was subsequently visualized with coherent anti-Stokes Raman scattering microscopy. Our findings demonstrate that stainless steel substrates deliver improved Raman signal and enhanced spectral characterization, underscoring its potential as a cost-effective alternative to CaF₂ for non-invasively monitoring cellular biochemical dynamics in translational cancer research.

Mid-infrared spectroscopy for the rapid quantification of eucalyptus oil adulteration in Australian tea tree oil (Melaleuca alternifolia)

Essential oil distilled from Melaleuca alternifolialeaves, commonly known as tea tree oil, is well known for its biological activity, principally its antimicrobial properties. However, many samples are adulterated with other, cheaper essential oils such as eucalyptus oil. Current methods of detecting such adulteration are costly and time-consuming, making them unsuitable for rapid authentication screening. This study investigated the use of mid-infrared (MIR) spectroscopy for detecting and quantifying the level of eucalyptus oil adulteration in spiked samples of pure Australian tea tree oil. To confirm the authenticity of the tea tree oil samples, GC-MS analysis was used to profile 37 of the main volatile constituents present, demonstrating that the samples conformed to ISO specifications. Three chemometric regression techniques (PLSR, PCR and SVR) were trialled on the MIR spectra, along with a variety of pre-processing techniques. The best-performing full-wavelength PLSR model showed excellent prediction of eucalyptus oil content, with an R²_CV of 0.999 and RMSECV of 1.08 % v/v. The RMSECV could be further improved to 0.82 % v/v through a moving window wavenumber optimisation process. The results suggest that MIR spectroscopy combined with PLSR can be used to predict eucalyptus oil adulteration in Australian tea tree oil samples with a high level of accuracy.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

Facile hydrothermal synthesis and purification of fluorescent carbon dots for food colorant tartrazine detection based on a dual-mode nanosensor

Colorant tartrazine is widely used in the food industry, but its long-term and excessive consumption is harmful to human health. Therefore, it is necessary to establish a sensitive detection method for tartrazine. Blue fluorescent carbon dots with L-arginine and o-phenylenediamine as precursors, namely L-Arg/oPD-CDs, were prepared via the hydrothermal method. Then, L-Arg/oPD-CDs were further purified by dialysis, thin layer chromatography and column chromatography. A dual-mode nanosensor based on fluorescent and UV absorption was successfully developed. Excellent linear ranges of 0-5 μM and 10-50 μM were obtained with a low detection limit of 42.3 nM based on fluorescence. A good linear range of 0-50 μM was obtained with a low detection limit of 130.15 nM based on UV absorption. The quenching mechanism of tartrazine towards L-Arg/oPD-CDs fluorescence was the inner filter effect. In addition, a dual-mode nanosensor was used for tartrazine determination in millet, maize flour, carbonated drink, and sugar samples. This study provides new insight into the detection of tartrazine by applying a dual-mode nanosensor.

Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods

Size and concentration are two important parameters for the analysis of microplastics (MPs) in water. The analytical tools reported so far extract this information in a single-particle analysis mode, dramatically increasing the analysis time. Here, we present a combination of multi-angle static light scattering technique, called "Goniophotometry", with chemometric multivariate data processing for the batch analysis of size and concentration of MPs in water. Nine different sizes of polystyrene (PS) MPs with diameters between 500 nm and 20 μm are investigated in two different scenarios with uniform (monodisperse) and non-uniform (polydisperse) size distribution of MPs, respectively. It is shown that Principal Component Analysis (PCA) can reveal the existing relationship between the scattering data of mono- and polydisperse samples according to the size distribution of MPs in mixtures. Therefore, a Linear Discriminant Analysis (LDA) model is constructed based on the PCA of scattering data of PS monodisperse samples and is subsequently employed to classify the size of MPs not only in unknown mono- and polydisperse PS samples, but also for other types of MPs such as Polyethylene (PE) and Polymethylmethacrylate (PMMA). When the size of MPs is classified, their concentration is measured using a simple linear fit. Finally, a Linear Least Square (LLS) model is used to evaluate the reproducibility of the measurements.

Spectroscopic techniques for authentication of animal origin foods

Milk and milk products, meat, fish and poultry as well as other animal derived foods occupy a pronounced position in human nutrition. Unfortunately, fraud in the food industry is common, resulting in negative economic consequences for customers as well as significant threats to human health and the external environment. As a result, it is critical to develop analytical tools that can quickly detect fraud and validate the authenticity of such products. Authentication of a food product is the process of ensuring that the product matches the assertions on the label and complies with rules. Conventionally, various comprehensive and targeted approaches like molecular, chemical, protein based, and chromatographic techniques are being utilized for identifying the species, origin, peculiar ingredients and the kind of processing method used to produce the particular product. Despite being very accurate and unimpeachable, these techniques ruin the structure of food, are labor intensive, complicated, and can be employed on laboratory scale. Hence the need of hour is to identify alternative, modern instrumentation techniques which can help in overcoming the majority of the limitations offered by traditional methods. Spectroscopy is a quick, low cost, rapid, non-destructive, and emerging approach for verifying authenticity of animal origin foods. In this review authors will envisage the latest spectroscopic techniques being used for detection of fraud or adulteration in meat, fish, poultry, egg, and dairy products. Latest literature pertaining to emerging techniques including their advantages and limitations in comparison to different other commonly used analytical tools will be comprehensively reviewed. Challenges and future prospects of evolving advanced spectroscopic techniques will also be descanted.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet–visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks

The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013-2020)

The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.

Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review

In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products.

Recent advances in NMR-based metabolomics of alcoholic beverages

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NMR-based techniques can be used for establishing metabolic “fingerprint” .

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Biomarkers for discrimination of wine varietals were identified.

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COSY and DOSY techniques may aid in assigning phenolic compounds and disaccharides.

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NMR-based metabolomic studies of alcoholic beverages remain limited in Asia.

Alcoholic beverages have a complex chemistry that can be influenced by their alcoholic content, origin, fermentation process, additives, and contaminants. The complex composition of these beverages leave them susceptible to fraud, potentially compromising their authenticity, quality, and market value, thus increasing risks to consumers’ health. In recent years, intensive studies have been carried out on alcoholic beverages using different analytical techniques to evaluate the authenticity, variety, age, and fermentation processes that were used. Among these techniques, NMR-based metabolomics holds promise in profiling the chemistry of alcoholic beverages, especially in Asia where metabolomics studies on alcoholic beverages remain limited.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.

A Multi-Elements Isotope Approach to Assess the Geographic Provenance of Manila Clams (Ruditapes philippinarum) via Recombining Appropriate Elements

The increasing global consumption of seafood has led to increased trade among nations, accompanied by mislabeling and fraudulent practices that have rendered authentication crucial. The multi-isotope ratio analysis is considered as applicable tool for evaluating geographical authentications but requires information and experience to select target elements such as isotopes, through a distinction method based on differences in habitat and physiology due to origin. The present study examined recombination conditions of multi-elements that facilitated geographically distinct classifications of the clams to sort out appropriate elements. Briefly, linear discriminant analysis (LDA) analysis was performed according to several combinations of five stable isotopes (carbon (δ¹³C), nitrogen (δ¹⁵N), oxygen (δ¹⁸O), hydrogen (δD), and sulfur (δ³⁴S)) and two radiogenic elements (strontium (⁸⁷Sr/⁸⁶Sr) and neodymium (¹⁴³Nd/¹⁴⁴Nd)), and the geographical classification results of the Manila clam Ruditapes philippinarum from Democratic People’s Republic of Korea (DPR Korea), Korea and China were compared. In conclusion, linear discriminant analysis (LDA) with at least four elements (C, N, O, and S) including S revealed a remarkable cluster distribution of the clams. These findings expanded the application of systematic multi-elements analyses, including stable and radiogenic isotopes, to trace the origins of R. philippinarum collected from the Korea, China, and DPR Korea.

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives

With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.

Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.

NIR and 1H qNMR methods coupled to chemometrics discriminate the chemotypes of the gastroprotective herb Egletes viscosa

Egletes viscosa is a Brazilian medicinal herb consumed as flower bud tea due to its gastroprotective properties. This plant possesses two essential oil-based chemical varieties: trans-pinocarveyl acetate-rich chemotype A and cis-isopinocarveyl acetate- rich chemotype B. Therefore, we developed two simple, fast and reliable methods for discrimination of E. viscosa chemotypes using NIR and ¹H qNMR spectroscopies combined with the chemometrics tools (iPLS and PLS-DA). Both methods showed high sensitivity, precision and specificity in the cross-validation tests. The NIR method has the advantages of being non-destructive and analyzable by portable devices, enabling its application for field and industrial evaluations. Meanwhile, the ¹H qNMR method allows the quantification of the bioactive components ternatin, tanabalin, and centipedic acid. These aforementioned compounds were found higher in the chemotype A. Accordingly, our methods showed to be complimentary approaches for authenticity and/or quality control of E. viscosa-derived raw materials and herbal products.

Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

Wine traceability and authenticity: approaches for geographical origin, variety and vintage assessment

The aim of this work is to identify and discuss physicochemical wine characteristics, to provide to some extent a link to the vintage, variety, and/or geographical origin. Bibliographic datasets were attempted to provide the main information for topic comprehension, identifying the sources of wine compositional variability and how these can be expressed in terms of the belonging categories. Since all the environmental and technological conditions which vineyard and wine are subjected are rarely known, different sources were inspected. Great importance was given to the study of isotopic composition because of its importance in food frauds detection history. The interaction of the plant genotype with the environmental conditions of the vintage is the main responsible for the wines organic and inorganic fraction variability in terms of both total and relative content. This phenotypical expression, together with human and abiotic variability sources, has been examined since it contains to some extent the information for the discrimination of wines according to their category. Recently, new proton nuclear magnetic resonance (1H NMR) spectroscopy techniques have been under study and, used concurrently to chemometric data management procedures, showed to be an interesting and promising tool for wine characterization according to both vintage and variety.

NGS-based barcoding with mini-COI gene target is useful for pet food market surveys aimed at mislabelling detection

Pet food industry has grown considerably in the last few years and it is expected to continue with this rate. Despite the economic impact of this sector and the consumer concerns for the increasing number of food and feed adulteration cases, few studies have been published on mislabelling in pet foods. We therefore investigated the capability of a next generation sequencing-based mini-barcoding approach to identify animal species in pet food products. In a preliminary analysis, a 127 bp fragment of the COI gene was tested on both individual specimens and ad hoc mixed fresh samples used as testers, to evaluate its discrimination power and primers effectiveness. Eighteen pet food products of different price categories and forms available on the market (i.e. kibbles, bites, pâté and strips) were analysed through an NGS approach in biological replicates. At least one of the species listed in the ingredients was not detected in half of the products, while seven products showed supplementary species in addition to those stated on the label. Due to the accuracy, sensitivity and specificity demonstrated, this method can be proposed as food genetic traceability system to evaluate both the feed and food quality timely along the supply chain.

FT-NIR and linear discriminant analysis to classify chickpea seeds produced with harvest aid chemicals

Spectroscopy and machine learning (ML) algorithms have provided significant advances to the modern food industry. Instruments focusing on near-infrared spectroscopy allow obtaining information about seed and grain chemical composition, which can be related to changes caused by field pesticides. We investigated the potential of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea seeds produced using different desiccant herbicides at harvest anticipation. Five herbicides applied at three moments of the plant reproductive stage were utilized. The NIR spectra obtained from individual seeds were used to build ML models based on LDA algorithm. The models developed to identify the herbicide and the plant phenological stage at which it was applied reached 94% in the independent validation set. Thus, the LDA models developed using near-infrared spectral data provided to be efficient, quick, non-destructive, and accurate to identify differences between seeds due to pre-harvest herbicides application.