Analytical techniques combined with chemometrics for authentication and determination of contaminants in condiments: A review

Impact of source, packaging and presence of food safety management system on heavy metals levels in spices and herbs

Spices and herbs are susceptible to various contaminants, including heavy metals. Our study aimed to quantify the levels of lead (Pb), mercury (Hg), cadmium (As), and cadmium (Cd) in 96 composite samples of 13 herbs and spices frequently consumed in Lebanon. Twenty percent (19/96) and 4% (4/96) of the samples exceeded the permissible levels of Pb and Cd, respectively and all the samples met the permissible levels of As and Hg according to Codex Alimentarius standards. For Pb and Cd, unpackaged samples had the highest levels of unacceptable samples of 31% (8/26) and 8% (2/26), respectively. The samples locally packaged in companies with and without Food Safety Management System (FSMS) had the same levels of unacceptable samples of 12% (3/26) and 4% (1/26) for Pb and Cd, respectively. Imported packaged samples had unacceptable levels of Pb (28% (5/18)) and were acceptable for the three other heavy metals.

Illegal synthetic dyes in spices: a Singapore case study

Some synthetic dyes are fraudulently added into spices to appeal visually to consumers. Food regulations in several countries, including the United States, Australia, Japan and the European Union, strictly prohibit the use of unauthorised synthetic dyes in food. Nevertheless, illegal practices persist, where spices contaminated with potentially carcinogenic dyes have been documented, posing potential health risks to consumers. In the present study, 14 synthetic dyes were investigated through liquid chromatography/tandem mass spectrometry in 252 commercially available spices in the Singapore market. In 18 out of these (7.1%) at least 1 illegal dye was detected at concentrations ranging from 0.010 to 114 mg/kg. Besides potential health risks, presence of these adulterants also reflects the economic motivations behind their fraudulent use. Findings in the present study further emphasise the need for increased public awareness, stricter enforcement, and continuous monitoring of illegal synthetic dyes in spices to ensure Singapore's food safety.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

There is a necessity to protect the quality and authenticity of herbs and spices because of the increase in the fraud and adulteration incidence during the last 30 years. There are several aspects that make herbs and spices quite vulnerable to fraud and adulteration, including their positive and desirable sensorial and health-related properties, the form in which they are sold, which is mostly powdered, and their economic relevance around the world, even in developing countries. For these reasons, sensitive, rapid, and reliable techniques are needed to verify the authenticity of these agri-food products and implement effective adulteration prevention measures. This review highlights why spices and herbs are highly valued ingredients, their economic importance, and the official quality schemes to protect their quality and authenticity. In addition to this, the type of frauds that can take place with spices and herbs have been disclosed, and the fraud incidence and an overview of scientific articles related to fraud and adulteration based on the Rapid Alert System Feed and Food (RASFF) and the Web of Science databases, respectively, during the last 30 years, is carried out here. Next, the methods used to detect adulterants in spices and herbs are reviewed, with DNA-based techniques and mainly spectroscopy and image analysis methods being the most recommended. Finally, the available adulteration prevention measurements for spices and herbs are presented, and future perspectives are also discussed.

Pesticide Residues and Unauthorized Dyes as Adulteration Markers in Chilli Pepper and Tomato

To assess the contamination of processed chilli pepper and tomatoes, a report over the past four decades since the establishment of the Rapid Alert System for Food and Feed (RASFF) was retrieved and analysed. Out of the 887 notification reports assessed for eligibility, 446 were found regarding chilli pepper and tomato contamination. This study identified India as the country of origin with the highest number of reported cases relating to chilli pepper contamination. Italy and Türkiye were the countries with the highest number of reported cases regarding the exportation of adulterated tomatoes to other countries according to the RASFF report. Unauthorized dyes such as Sudan I, III, IV, orange II, rhodamine B, and para red were reported to have been detected in either chilli pepper or tomato in the supply chain. Almost all unauthorized dyes in this study were found to be more than the range (0.5 to 1 mg/kg) of the detection limit of Sudan dye and other related dyes using analytical methods set by the European Union. Unapproved pesticides by the European Union (EU) found in this study were acetamiprid, chlorothalonil, chlorpyrifos, dimethoate, methomyl, monocrotophos, omethoate, oxamyl, and thiophanate methyl. The present study indicates the persistence of chilli pepper and tomato contamination with harmful dyes and pesticide residues despite the ban on the use of certain chemicals in the food chain.

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

Fingerprinting based on gas chromatography-Orbitrap high-resolution mass spectrometry and chemometrics to reveal geographical origin, processing, and volatile markers for thyme authentication

Thyme is an aromatic herb traditionally used for food purposes due to its organoleptic characteristics and medicinal properties, which is highly susceptible to food fraud. In this study, GC-HRMS-based fingerprinting was applied for the first time to determine the geographical traceability of thyme based on different origins (Spain, Poland, and Morocco), as well as to assess its processing by comparing sterilized vs. non-sterilized thyme. Unsupervised chemometric methods (PCA and HCA) revealed a predominant influence of the geographical origin on thyme fingerprints rather than processing effects. Supervised PLS-DA and OPLS-DA were used for discrimination purposes, revealing high predictive ability for further samples (100%), and allowing the identification of differential compounds (markers). A total of 24 markers were putatively identified (13 metabolites were confirmed) belonging to different classes: monoterpenoids, diterpenoids, sesquiterpenoids, alkenylbenzenes, and other miscellaneous compounds. This study outlines the potential of combining untargeted analysis by GC-HRMS with chemometrics for thyme authenticity and traceability.

Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning

Food samples are routinely screened for food-contaminating beetles (i.e., pantry beetles) due to their adverse impact on the economy, environment, public health and safety. If found, their remains are subsequently analyzed to identify the species responsible for the contamination; each species poses different levels of risk, requiring different regulatory and management steps. At present, this identification is done through manual microscopic examination since each species of beetle has a unique pattern on its elytra (hardened forewing). Our study sought to automate the pattern recognition process through machine learning. Such automation will enable more efficient identification of pantry beetle species and could potentially be scaled up and implemented across various analysis centers in a consistent manner. In our earlier studies, we demonstrated that automated species identification of pantry beetles is feasible through elytral pattern recognition. Due to poor image quality, however, we failed to achieve prediction accuracies of more than 80%. Subsequently, we modified the traditional imaging technique, allowing us to acquire high-quality elytral images. In this study, we explored whether high-quality elytral images can truly achieve near-perfect prediction accuracies for 27 different species of pantry beetles. To test this hypothesis, we developed a convolutional neural network (CNN) model and compared performance between two different image sets for various pantry beetles. Our study indicates improved image quality indeed leads to better prediction accuracy; however, it was not the only requirement for achieving good accuracy. Also required are many high-quality images, especially for species with a high number of variations in their elytral patterns. The current study provided a direction toward achieving our ultimate goal of automated species identification through elytral pattern recognition.

Notifications on Pesticide Residues in the Rapid Alert System for Food and Feed (RASFF)

Pesticides are commonly used to protect plants against various pests and to preserve crops, but their residues can be harmful for human health. They are the third most widely reported hazard category in the European Rapid Alert System for Food and Feed (RASFF). The purpose of the study was to identify the most frequently notified pesticides in the RASFF in 1981–2020, considering: year, notification type, product category, origin country, notifying country, notification basis, distribution status and action taken. The data from the RASFF database was processed using: filtering, transposition, pivot tables and then subjected to cluster analysis: joining (tree clustering) and two-way joining methods. Pesticides were most commonly reported in fruits and vegetables and herbs and spices following border controls and rejections. The products usually came from India or Turkey and were not placed on the market or were not distributed and then destroyed. The effectiveness of the European Union border posts in terms of hazards detection and mutual information is important from the point of view of protecting the internal market and ensuring public health. It is also necessary to increase the awareness of pesticide users through training and the activity of control authorities in the use of pesticides.

Determination of Peak Purity in HPLC by Coupling Coulometric Array Detection and Two-Dimensional Correlation Analysis

This work aims to evaluate the purity of chromatographic peaks by a two-dimensional correlation (2D-corr) analysis. Such an analysis leads to two contour plots: synchronous and asynchronous. The synchronous contour plot provides information on the number of peaks present in the chromatogram. The asynchronous contour plot reveals the presence of overlapping species on each peak. The utility of 2D-corr analysis was demonstrated by the chromatographic analysis of Capsicum chili extracts obtained by HPLC coupled with a coulometric array of sixteen detectors. Thanks to 16 electrochemical sensors, each poised at increasing potentials, the resulting 2D-corr analysis revealed the presence of at least three species on the peak located at a retention time of 0.93 min. Mass spectrometry (MS) analysis was used to analyze the coeluting species, which were identified as: quinic acid (3.593 min), ascorbic acid (3.943 min), and phenylalanine (4.229 min). Overall, this work supports the use of 2D-corr analysis to reveal the presence of overlapping compounds and, thus, verify the signal purity of chromatographic peaks.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

Fluorescence quenching by competitive absorption between solid foods: Rapid and non-destructive determination of maize flour adulterated in turmeric powder

Fluorescence quenching induced by competitive absorption between different components of solid foods was observed for the first time. By using front-face synchronous fluorescence spectroscopy (FFSFS) and fluorescence titration, competitive absorption between maize flour and turmeric powder was proven to occur between phenolic acids in maize flour and curcumin in turmeric powder. FFSFS was applied for the rapid and non-destructive determination of maize flour adulterated in turmeric powder. Prediction models were constructed by partial least square (PLS) regression based on unfolded total synchronous fluorescence spectra, and were validated by five-fold cross-validation and external validation, with the determination coefficient of prediction (R_p²) greater than 0.95, root mean square error of prediction (RMSEP) < 6%, relative error of prediction (REP) < 15% and residual predictive deviation (RPD) greater than 5. The limit of detection (LOD) of maize flour was approximately 9%. In addition, most relative errors for test samples were from -20% to 20%.

ICP-MS Assessment of Essential and Toxic Trace Elements in Foodstuffs with Different Geographic Origins Available in Romanian Supermarkets

The present study was conducted to quantify the daily intake and target hazard quotient of four essential elements, namely, chromium, cobalt, nickel, and copper, and four toxic trace elements, mercury, cadmium, lead, and arsenic. Thirty food items were assigned to five food categories (seeds, leaves, powders, beans, and fruits) and analyzed using inductively coupled plasma-mass spectrometry. Factor analysis after principal component extraction revealed common metal patterns in all foodstuffs, and using hierarchical cluster analysis, an association map was created to illustrate their similarity. The results indicate that the internationally recommended dietary allowance was exceeded for Cu and Cr in 27 and 29 foodstuffs, respectively. According to the tolerable upper level for Ni and Cu, everyday consumption of these elements through repeated consumption of seeds (fennel, opium poppy, and cannabis) and fruits (almond) can have adverse health effects. Moreover, a robust correlation between Cu and As (p < 0.001) was established when all samples were analyzed. Principal component analysis (PCA) demonstrated an association between Pb, As, Co, and Ni in one group and Cr, Cu, Hg, and Cd in a second group, comprising 56.85% of the total variance. For all elements investigated, the cancer risk index was within safe limits, highlighting that lifetime consumption does not increase the risk of carcinogens.

Application of spectral features for separating homochromatic foreign matter from mixed congee

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A method that can separate homochromatic FM in mixed congee was proposed.

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Spectral features of FM and mixed congee were extracted to build recognition model.

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The SVM model achieved high identification rates (99.17%) for homochromatic FM.

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The proposed method is better than computer vision in separating homochromatic FM.

Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.

Spices and Seasoning Mixes in European Union-Innovations and Ensuring Safety

Spices are an important group of food products of great importance in nutrition and food technology. They are mainly used to shape the sensory properties of food in gastronomy, in home cooking, and in industry. Ensuring quality and safety is one of the basic tasks of spice producers. The aim of this review is to present the threats to the consumer related to the presence of spices and seasoning mixes in the diet. Therefore, special attention was paid to such risks as excess sodium chloride (and sodium) in spice mixtures, the use of additives influencing the sensory experience, and irregularities in the labeling of spices and seasoning mixes for the presence of additives and allergens. The threats regarding microbiological safety and the presence of heavy metals, pesticides, plant protection products, as well as synthetic fertilizers and undeclared additives are also presented and the issue of adulteration and lack of authenticity of spices and spice mixtures is discussed. Using data from IJHARS planned inspections and notifications registered in the EU Rapid Alert System for Food and Feed (RASFF) for 2015-2019, as well as the results of own research, an analysis of the risks caused by herbs and spices was carried out. Strategic activities of companies producing spices focus, among others, on improving production and expanding the commercial offer with new, attractive products. The article reviews product and process innovations in spice mixes and the methods of ensuring safety in this group of food products.

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Feasibility of Applying Untargeted Metabolomics with GC-Orbitrap-HRMS and Chemometrics for Authentication of Black Pepper (Piper nigrum L.) and Identification of Geographical and Processing Markers

Black pepper is one of the most consumed spices all over the world. Due to its high demand and nutritional value, a metabolomics approach based on GC-Orbitrap-HRMS fingerprinting and chemometrics was applied to assess its geographical traceability and processing authenticity. GC-HRMS-based fingerprints were obtained using a simple ultrasound-assisted extraction method, which may be easily implemented in routine activities of quality control. Unsupervised methods, such as principal component analysis (PCA), were performed for sample overview according to the investigated origins (Brazil, Vietnam, and Sri Lanka) and processing (sterilized vs nonsterilized samples). Further orthogonal partial least squares discriminant analysis (OPLS-DA) models were validated by cross- and external validation, providing satisfactory performance for geographical and processing authentication, as well as excellent predictive ability for further samples. Furthermore, reliable putative identification of 12 key metabolites (markers) was performed, highlighting the feasibility of combining untargeted GC-HRMS analysis with chemometrics for quality control of black pepper.

Stable Isotope Ratios of Herbs and Spices Commonly Used as Herbal Infusions in the Italian Market

Stable isotope ratio analysis has been widely used for traceability and authenticity purposes in relation to various food commodities, but only in a limited number of herb and spice species. This study explored the stable isotope ratios of carbon, nitrogen, sulfur, oxygen, and hydrogen (δ¹³C, δ¹⁵N, δ³⁴S, δ¹⁸O, and δ²H) of 119 herbs and spices belonging to 116 plant species and 57 plant families collected from the Italian market for the first time. The characteristic value ranges of δ¹³C, δ¹⁵N, δ³⁴S, δ¹⁸O, and δ²H of the herbs and spices went from -31.0 to -11.6, -4.7 to 12.0, -5.0 to 22.0, 14.7 to 46.0, and -158 to -12‰, respectively. The isotopic profiles within and between common botanical families and their similarity/dissimilarity between herbs and spices belonging to the common botanical families are also discussed here. The results of this exploratory work highlight the possibility of characterizing herbs and spices and suggest widening the scope of the survey through more extensive sampling and focusing on specific plant species.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Persistent organic pollutants (PCBs and PCDD/Fs), PAHs, and plasticizers in spices, herbs, and tea - A review of chromatographic methods from the last decade (2010-2020)

Edible and highly demanded plant-derived products such as herbs, spices, and tea may be subjected to exogenous contamination of well-known chemical hazards such as persistent organic pollutants (POPs), and emerging ones such as plasticizers, affecting negatively the safety of these food commodities. This fact has led to the increasing analysis of exogenous compounds including priority POPs such as polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs), as well as highly persistent polycyclic aromatic hydrocarbons (PAHs). Currently, plasticizer residues are also considered an emerging issue because of the extensive use in food packaging and potential migration into foodstuffs. In this review, the studies published from 2010 to 2020 were discussed, including the main extraction methods applied for these contaminants from herbs, spices, and tea, and it was revealed the trend toward the use of less solvent-consuming and time-effective methods. Chromatographic methods were also described, which were mainly combined with detection techniques such as classical or mass spectrometry (MS) detection. Finally, a comprehensive overview of the occurrence of these selected exogenous compounds was presented in the studied matrices, showing that their monitoring should be further investigated to ensure food safety of highly consumed condiments and tea.

Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy

The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well.

Practical application

Wholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis.

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Investigated if elemental content (XRF) can indicate origin of commercial teas.

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Only 18 elements were selected for analysis based on their repeatability performance.

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Multivariate classification was used to classify in five or two global regions.

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Asian vs non-Asian classification reached 85% correct prediction rate.

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Classification linked not only with origin but with type of tea as well.

Advances in Troubleshooting Fish and Seafood Authentication by Inorganic Elemental Composition

The demand for fish and seafood is growing worldwide. Meanwhile, problems related to the integrity and safety of the fishery sector are increasing, leading legislators, producers, and consumers to search for ways to effectively protect themselves from fraud and health hazards related to fish consumption. What is urgently required now is the availability of reliable, truthful, and reproducible methods assuring the correspondence between the real nature of the product and label declarations accompanying the same product during its market life. The evaluation of the inorganic composition of fish and seafood appears to be one of the most promising strategies to be exploited in the near future to assist routine and official monitoring operations along the supply chain. The present review article focuses on exploring the latest scientific achievements of using the multi-elemental composition of fish and seafood as an imprint of their authenticity and traceability, especially with regards to the geographical origin. The scientific literature of the last 10 years focusing on the analytical determination and statistical elaboration of elemental data (alone or in combination with methodologies targeting other compounds) to verify the identity of fishery products is summarized and discussed.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Strategies for data reduction in non-targeted screening analysis: The impact of sample variability for food safety applications

While analytical methods targeting specific compounds are critical for food safety, analytes excluded from the targeted list will not be identified. Non-targeted analyses (NTA) using LC/HR-MS complement these approaches by producing information-rich data sets where molecular formula can be generated for each detected compound; however, data mining can be labor intensive. Thus, we examined different NTA approaches to reduce the number of compounds needing further investigation, without relying on a suspect list or MS/MS database, both in single ingredient foods (i.e., oats) and more complex, oat-containing samples. We investigated inherent sample variability and utilized this information to build in-house databases for removing food compounds from sample data. While food databases were useful for data reduction, differential analysis was the most promising approach for single ingredient foods because it substantially reduced the number of features while retaining spiked QC compounds; however, a combination of approaches was necessary with greater sample complexity.

A reusable AuNPS with increased stability applied for fast screening of trace heavy metals in edible and medicinal marine products

Heavy metal pollution in marine environment poses a severe threat to the safety of marine products and is thus causing increasingly concerns in terms of their toxicity and potential health risks pose to human. Due to the complex matrix of marine products, a fast screening method for heavy metals at trace level with low price, reusability, high accuracy and long lifetime is of urgency and necessity for consumers and processing factories. This work described a simplified screening system through the preparation, characterization and particular application of Au nano particle sensor (AuNPS) in the complex marine matrix, the main aim is to significantly increase the stability, sensitivity and lifetime of detection system dedicated to Cu and Hg trace analysis in marine products. It is worth mentioning that, the proposed screening system was characterized through electrochemical experiments and theoretical calculations, which could be a new evidence for selecting the detection system in commercially complex samples. Importantly, the discipline of deposition and oxidative stripping process on AuNPS was explained based on the mechanism of Metal Ion Deficient Layer (MIDL), and illustrated with SEM changes during stripping process, as well as the dissolving-out rate of metals on AuNPS material. Moreover, to further improve the reusability and stability of AuNPS sensor, the complex marine matrix was purified by pre-plating interferences on indium tin oxide glass electrode. The screening system exhibited a liner response in the range of 0.02-0.10 μg mL^-1 for Hg, 0.01-0.10 μg mL^-1 and 0.001-0.01 μg mL^-1 for Cu with the detection limits of 0.138 mg kg^-1 and 1.51 mg kg^-1 in marine matrix, respectively. The sensitivity and lifetime was at least two times better as compared to similar works even after 20-times use. Finally, this proposed analysis system combined with purification procedure was successfully applied for the edible and medicinal marine products analysis, meanwhile, the accuracy and stability were confirmed with standard analytical methods.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Active Fractions of Methanol Crude Obtained from Acacia seyal gum: Antioxidant Capacity, using FTIR Analysis

The present study is on Acacia seyal gum (ASG), which is an exudate from Talha tree. It provides a rich source of polyphenolics compounds that are used traditionally in folk medicine. The study aims to determine the antioxidant capacity (AC) and functional groups of ASG and Prebio-T-commercial (PTC) samples. The methanol crude extracts of both ASG and PTC have fractioned into chloroform (CHF), hexane (HF), acetone (AF) and methanol (MF) using solvent-solvent portion. Both ferric reducing antioxidant power (FRAP), and cupric reducing antioxidant capacity (CUPRAC) assays for each fraction examined. Crude methanol extracts (CME) and its active compositions also analysed carefully using Fourier Transform Infrared Spectroscopy (FTIR) technique. The findings presented a wide variety of functional groups provided by the FTIR spectra (eights bands approximately. Regarding cupric reducing antioxidant capacity (CUPRAC), the methanol crude extracts values are 888.6�4.57 mg TE/100g extract, for PTC as compared to 474.3� 2.23 mg TE/100g of extract for ASG. However, both methanol and acetone fractions revealed significantly (p = 0.05) high FRAP values ranged between 599.8�7.5 and 741.8�5.8 mg TE/100g fraction; for PTC and ASG, respectively. While CUPRAC showed insignificant (p = 0.05) same values 356.1�2.62 mg TE/100g of fraction; for MF of both PTC and ASG respectively. Therefore, in this study, methanolic fractions (MFs) are found to be more effective than acetone fractions (AFs), except for CHF and HF. Finally, the antioxidant activity of the active fraction has provided some evidence regarding its functional groups which may have used in traditional medicine.

Fe3O4@Au nanoparticles-based magnetoplatform for the HMGA maize endogenous gene electrochemical genosensing

This work addresses a technological advance applied to the construction of a magnetogenoassay with electrochemical transduction for the maize taxon-specific (HMGA gene) detection using gold-coated magnetic nanoparticles as nanosized platform. Superparamagnetic core-shell Fe₃O₄@Au nanoparticles (10.4 ± 1.7 nm) were used to assemble the genoassay through the covalent immobilization of HMGA DNA probes onto carboxylated self-assembled monolayers at the nanoparticles surface. A hybridization reaction using sandwich format was selected to prevent inefficient hybridization connected with stable secondary DNA structures using also fluorescein isothiocyanate as DNA signaling tag. The labelling of the hybridization reaction with enzymes allowed the chronoamperometric measurement of the peroxidase activity linked to the nanoplatform located on gold surface. Using this electrochemical magnetogenoassay a linear concentration range from 0.5 to 5 nM and a LOD of 90 pM with a RSD <1.2% was calculated. Certified maize was evaluated without further purification after PCR amplification. This work highlights the efficacy of the electrochemical magnetogenoassay for the HMGA detection, showing its potential as alternative procedure for the verification of the compliance of the legislation.

Quantification of particulate matter, tracking the origin and relationship between elements for the environmental monitoring of the Antarctic region

The present work reports on the analysis of atmospheric aerosols in the Antarctic region, Deception Island, collected during austral summer 2016-2017 by field measurements carried from Gabriel de Castilla Spanish Research Station. A low-volume sampler was used to capture the aerosols depositing them onto the air filters. A chemical analysis of the samples using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) provided the total carbon (TC), organic carbon (OC), elemental Carbon (EC) and elements such as Al, Ca, Fe, K, Mg, Na, P, S, Cu, Pb, Sr, Ti, Zn and Cr. The average mass concentration of particulate matter (PM₁₀) originated by natural and anthropogenic activities was calculated as 10 ± 4 μg/m³, although values such as 28.2 μg/m³ were also obtained which is very high even when compared to other places in the coast of the Antarctic region. In addition, high enrichment factors have been found for elements such as Pb, Cr, Cu and Zn showing a remote anthropogenic contribution to particulate matter in this region. Correlations were found between Na, Mg, Ca, Al, Ti and S, where Na/Mg displayed the influence of marine environments, S correspond to volcanic activities, Ca to penguin colonies and influence of sea whereas Al/Ti indicated the crustal origin. Polar contour graphical maps were obtained from meteorological data using chemometrics methods, which allowed reproducing wind maps revealing the distribution of the aerosols and possible emission sources of different elements in the area. Given that this island has not been previously studied for atmospheric contamination, this work provides an interesting insight about the site-specific characteristics of particulate matter.

Application of different chromatographic techniques and chemometric analysis in authenticity testing of plant protection products containing azoxystrobin as an active substance

Azoxystrobin (methyl(2E)-2-{2-[6-(2-cyanophenoxy)pyrimidin-4-yloxy] phenyl}-3-methoxyacrylate) is an active ingredient used to protect crops against fungal diseases. The experience of the Polish control laboratory indicates relatively frequent cases of counterfeit plant protection products (PPPs) containing this active substance. The present study aimed to use chemometric methods to model chemical fingerprints obtained by different chromatographic techniques to verify the original formulation of PPPs containing the active substance azoxystrobin. The pesticides used in the study came from different sources (including stores and warehouses), were manufactured at a different time and came from different production batches. The results obtained with the HPLC-DAD and HS-GC-MS techniques were then modeled using principal component analysis (PCA) and soft independent modeling by class analogy (SIMCA) classifier. The proposed approach has been confirmed as useful for verifying the authenticity of PPPs and can be used in the routine control testing of SC pesticides containing azoxystrobin.

A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey

Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people's health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box-Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10-50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.

Machine learning algorithms for the automated classification of contaminated maize at regulatory limits via infrared attenuated total reflection spectroscopy

Mould infested maize poses a severe problem for farmers, food producers and for consumers worldwide. Mycotoxins are secondary metabolites produced by certain mould species that contaminate food and feed. The consumption of these toxins may cause serious health problems for humans and animals. The trichothecene deoxynivalenol (DON) constitutes one of the most commonly occurring Fusarium toxins encountered in maize and requires improved methods to limit its entrance into the food and feed system. While a variety of chromatographic and mass spectrometry methods for the identification of such toxins have been established, these are considered time-consuming, cost-intensive and require highly qualified personnel. Alternatively, optical techniques, such as mid-infrared spectroscopy offer rapid detection of fungal infections in cereals and other commodities with minimised sample preparation and analysis time. The present study demonstrates a rapid fungal contamination detection strategy in maize taking advantage of IR-spectroscopy combined with advanced machine learning algorithms. The developed method represents an advancement for the analysis of differences in protein and carbohydrate content revealed in the associated IR-spectra related to the amount of toxin contamination at the European Union (EU) regulatory limits for DON in maize (i.e. 1,250 μg/kg). The employed maize varieties are naturally infected samples or have been infected with Fusarium verticillioides, Fusarium graminearum or Fusarium culmorum. Sieved maize samples at the EU regulatory limit were correctly classified using machine learning approaches, therefore enabling the differentiation between DON-contaminated and non-contaminated maize samples. Specifically, a variety of machine learning methods, including Adaptive Boosting (AdaBoost), Random Forests, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) demonstrated excellent classification and validation performance using the obtained IR-spectra. As a result, 183 maize samples of different varieties and infection levels were accurately classified. 94% of the non-contaminated samples and 91% of the contaminated samples were correctly classified using an MLP classification approach.