Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies

Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities

Food adulteration is the deceitful practice of misleading consumers about food to profit from it. The threat to public health and food quality or nutritional valuable make it a major issue. Food origin and adulteration should be considered to safeguard customers against fraud. It has been established that artificial intelligence is a cutting-edge technology in food science and engineering. In this study, it has been explained how AI detects food tampering. Applications of AI such as machine learning tools in food quality have been studied. This review covered several food quality detection web-based information sources. The methods used to detect food adulteration and food quality standards have been highlighted. Various comparisons between state-of-the-art techniques, datasets, and outcomes have been conducted. The outcomes of this investigation will assist researchers choose the best food quality method. It will help them identify of foods that have been explored by researchers and potential research avenues.

Quality analysis and geographical origin identification of Rosa roxburghii Tratt from three regions based on Fourier transform infrared spectroscopy

The study aimed to provide new information of Rosa roxburghii Tratt (RRT) for the production of functional foods and distinguish the geographical origins of RRT. The nutritional components of RRT from three regions in China, such as vitamin C, polysaccharides, total flavonoids, and total phenolics, and their antioxidant activities were analyzed by one-way ANOVA. The results of Fourier transform infrared spectroscopy (FT-IR) combined with principal component analysis (PCA), stepwise linear discriminant analysis (SLDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were used to establish discriminant models to identify the geographical origin of RRT. The results of one-way ANOVA showed that the contents of some nutrients and antioxidant activity were significantly different among RRT from different regions and their FT-IR spectra also showed significant differences. The characteristic fingerprint bands of FT-IR (1679-1618 cm^-1and 1520-900 cm^-1) closely related to the geographical origins of RRT were screened out. Based on SLDA, a discriminant model was established to realize the classification and identification of RRT from different regions and the correct discrimination rate of the testing sample set obtained with the established model reached 100 %. Geographical factors caused the obvious differences in nutritional components and antioxidant activity in RRT. The characteristic fingerprint bands of RRT obtained with FT-IR could be used to identify the geographical origins of RRT more quickly and accurately.

Personal Glucose Meters Coupled with Signal Amplification Technologies for Quantitative Detection of Non-Glucose Targets: Recent Progress and Challenges in Food Safety Hazards Analysis

Ensuring food safety is paramount worldwide. Developing effective detection methods to ensure food safety can be challenging owing to trace hazards, long detection time, and resource-poor sites, in addition to the matrix effects of food. Personal glucose meter (PGM), a classic point-of-care testing device, possesses unique application advantages, demonstrating promise in food safety. Currently, many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards. Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors, which is crucial for solving the challenges associated with the use of PGMs for food safety analysis. This review introduces the basic detection principle of a PGM-based sensing strategy, which consists of three key factors: target recognition, signal transduction, and signal output. Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies (nanomaterial-loaded multienzyme labeling, nucleic acid reaction, DNAzyme catalysis, responsive nanomaterial encapsulation, and others) in the field of food safety detection are reviewed. Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed. Despite the need for complex sample preparation and the lack of standardization in the field, using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.

Discrimination of whole grape juice using fluorescence spectroscopy data with linear discriminant analysis coupled to genetic and ant colony optimisation algorithms

In this study, a new approach was developed for classifying grape juices produced in Brazil using unfolded excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics, with respect to the agricultural production system, namely the conventional or organic agricultural one. Linear discriminant analysis (LDA) coupled to ant colony optimisation (ACO) and the genetic algorithm (GA) were used to select a more effective subset of variables to discriminate grape juice samples. The best results demonstrated highly efficient classification of grape juice samples according to a conventional or organic production process with an accuracy rate of up to 97% for the models and 94% in the prediction of these classes for samples external to the model. The models showed high selectivity and sensitivity with a rate of up to 100% for the training and test datasets, in addition to determining the most significant variables that explain the separation of classes. The proposed method proves to be viable, as it is fast and requires minimal sample preparation, allowing quality control in the food industry.

Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology

Wolfiporia cocos is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of W. cocos from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify Poria and Poriae cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the “component-target-pathway” network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in Poria was significantly higher than that in Poriae cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for Poria and Poriae cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in Poria and Poriae cutis.

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

Rapid detection of apple juice concentrate adulteration with date concentrate, fructose and glucose syrup using HPLC-RID incorporated with chemometric tools

The present study investigates the substitute of apple juice concentrate with some cheap sweeteners including glucose syrup, fructose syrup, and date concentrate, as the most common adulterants. For this purpose, pure and authenticated apple juice concentrate was individually adulterated with 10% to 50% of glucose syrup, fructose syrup, and date concentrate. High-performance liquid chromatography coupled with a refractive index detector (HPLC-RID) was applied to determine the carbohydrates profile of samples. The results of HPLC-RID were subjected to multivariate statistical analysis, namely principal component analysis (PCA) and linear discriminant analysis (LDA). The results showed that the glucose/fructose ratio and maltose content were the best indicators to detect adulteration of apple juice concentrate. A set of glucose, sorbitol, sucrose, maltose, and glucose/fructose ratio was used as a discriminating factor. Using this approach, adulteration of apple juice concentrate with cheaper sweeteners was detected at a limit of 10%, depending on the adulterant.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods

A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger) and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared spectroscopy (FT-NIR) and chemometrics. FT-NIR provided a reliable method to qualitatively assess ginger samples and batches of S-ginger (41) and NS-ginger (39) were discriminated using principal component analysis and orthogonal partial least squares discriminant analysis of FT-NIR data. To generate quantitative methods based on partial least squares (PLS) and counter propagation artificial neural network (CP-ANN) from the FT-NIR, major gingerols were quantified using high performance liquid chromatography (HPLC) and the data used as a reference. Finally, PLS and CP-ANN were deployed to predict concentrations of target compounds in S- and NS-ginger. The results indicated that FT-NIR can provide an alternative to HPLC for prediction of active components in ginger samples and was able to work directly on solid samples.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

Deoxynivalenol (DON) is one of the mycotoxins produced mainly by the Fusarium graminearum species complex in small grain cereals, including barley. This toxin can cause alimentary disorders, immune function depression and gastroenteritis. The negative health effects associated with DON coupled to the increasing concern about green and rapid methods of analysis motivated this study. In this context, near infrared (NIR) spectroscopy data were applied for exploratory analysis to distinguish barley with high and low levels of DON contamination (> or <1250 µg/kg according to the European Union threshold), by Partial Least Squares-Discriminant Analysis (PLS-DA), and to verify the performance of Partial Least Squares-Regression (PLS-R) to predict DON concentration in barley samples. Maximum values of specificity and sensitivity were achieved in the calibration set; 90.9% and 81.9% were observed in the cross-validation set for the PLS-DA classification model. PLS-R quantification of DON in barley presented low values of error (RMSEC = 101.94 µg/kg and RMSEP = 160.76 µg/kg). Thus, we found that NIR in combination with adequate chemometric tools could be applied as a green technique to monitor DON contamination in barley.

Detection of adulteration in food based on nondestructive analysis techniques: a review

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Fruit-based juices: Focus on antioxidant properties-Study approach and update

This paper proposes a perspective literature review of the antioxidant properties in fruit-based juices. The total antioxidant properties due to compounds such as carotenoids, polyphenolic compounds, flavonoids, and tannins as well as the assessment of interactions between natural active compounds and other food matrix components can be seen as the first step in the study of potential health benefits of fruit-based juices. A brief summary is given on the significance of antioxidant properties of fruit juices, the conventional methods for antioxidant activity evaluation, and on the newly emerged sample analysis and data interpretation strategies, that is, chemometric analysis based on spectroscopic data. The effect of fruit processing techniques and the addition of ingredients on the antioxidant properties of fruit-based juices are also discussed.

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.