Food authentication: Techniques, trends & emerging approaches

Recent advances in halal food authentication: Challenges and strategies

Increasing public awareness of food quality and safety has prompted a rapid increase in food authentication of halal food, which covers the production method, technical processing, identification of undeclared components, and species substitution in halal food products. This urges for extensive research into analytical methods to obtain accurate and reliable results for monitoring and controlling the authenticity of halal food. Nonetheless, authentication of halal food is often challenging because of the complex nature of food and the increasing number of food adulterants that cause detection difficulties. This review provides a comprehensive and impartial overview of recent studies on the analytical techniques used in the analysis of halal food authenticity (from 1980 to the present, but there has been no significant trend in the choice of techniques for authentication of halal food during this period). Additionally, this review highlights the classification of different methodologies based on validity measures that provide valuable information for future developments in advanced technology. In addition, methodological developments, and novel emerging techniques as well as their implementations have been explored in the evaluation of halal food authentication. This includes food categories that require halal authentication, illustrating the advantages and disadvantages as well as shortcomings during the use of all approaches in the halal food industry.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Why the importance of geo-origin tracing of edible bird nests is arising?

Edible bird's nest (EBN) swiftlet existed naturally 48,000 years ago in caves as their natural dwellings. Nowadays, edible bird's nest has become a very important industry due to its high nutritional, medicinal and economic value. Additionally, edible bird's nest has a long quality guarantee period. Obviously, the nutritional components and medicinal functions vary depending on geographical origins. Recently, the global demand for edible bird's nest has markedly increased, accompanied by the increasing attention of all key players of the global food trade system, i.e., producers, consumers, traders and the authorities to obtain safe and high-quality edible bird's nest. Hence, this target can be accomplished via the enforcement of an efficient and universal geo-tracing technique. Current methods of the geo-tracking of edible bird's nest, i.e., automation, physical and analytical techniques have several limitations and all of them fail to discriminate different quality grades of edible bird's nest. Meanwhile, in many studies and applications, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) has proven to be a "cutting edge" technique for greatly enhance food traceability from field to fork through its ability in distinguishing the food products in terms of their quality and safety. This article provides an overview of (1) edible bird's nest as a multiuse strategic food product, (2) quality issues associated with edible bird's nest including implications that the site of acquisition of the edible bird's nest has food safety implications, (3) current regulations and geo-tracking approaches to ensure the safety and quality of edible bird's nest with the special focus on polymerase chain reaction-denaturing gradient gel electrophoresis technique as a vigorous and universal geo-tracing tool to be suggested for edible bird's nest geo-traceability.

Evaluating the usefulness of next-generation sequencing for herb authentication

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Food fraud and adulteration are rapidly spreading with the increased food demand.

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DNA-based food authenticity approaches can provide fast results at larger scale.

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We developed pipeline and customised barcode database that identify herbal plant taxa and ingredients.

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The pipeline performance was tested with publicly available datasets as well as newly sequenced herbal plants and products.

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Improvements to plant sequence databases remain needed to implement DNA testing more routinely.

Food authentication is a rapidly growing field driven by increasing public awareness of food quality and safety. Foods containing herbs are particularly prone to industrial fraud and adulteration. Several methodologies are currently used to evaluate food authenticity. DNA-based technologies have increased focus, with DNA barcoding the most widely used. DNA barcoding is based on the sequencing and comparison of orthologous DNA regions from all species in a sample, but the approach is limited by its low resolution to distinguish closely-related species. Here we developed a customised database and bioinformatics pipeline (Herbs Authenticity - GitHub) to identify herbal ingredients implemented as a metagenomics approach for plant-derived product authenticity testing. We evaluated the accuracy of the method by using publicly available plant genomes and databases to allow the construction of our customised database barcodes, which were also complemented with entries from publicly available resources (iBOL and ENA). The pipeline performance was then tested with new 47 de novo partly sequenced whole plant genomes or barcodes as query sequences. Our results show that using our mapping algorithm with the customised barcode database correctly identifies the main components of a wide range of plant-derived samples, albeit with variable additional noise across samples depending on the tested samples and barcodes. Our result also show that at the current stage the usefulness of metagenomics is limited by the availability of reference sequences and the needed sequencing depth. However, this method shows promise for evaluating the authenticity of different herbal products provided that the method is further refined to increase the qualitative and quantitative accuracy.

Review: Quality of animal-source foods

This article critically reviews the current state of knowledge on the quality of animal-source foods according to animal production and food processing conditions, including consumer expectations-behaviours and the effects of consumption of animal-source foods on human health. Quality has been defined through seven core attributes: safety, commercial, sensory, nutritional, technological, convenience, and image. Image covers ethical, cultural and environmental dimensions associated with the origin of the food and the way it is produced and processed. This framework enabled to highlight the priorities given to the different quality attributes. It also helped to identify potential antagonisms and synergies among quality attributes, between production and processing stages, and among stakeholders. Primacy is essentially given to commercial quality attributes, especially for standard commodity animal-source foods. This primacy has strongly influenced genetic selection and farming practices in all livestock commodity chains and enabled substantial quantitative gains, although at the expense of other quality traits. Focal issues are the destructuration of chicken muscle that compromises sensory, nutritional and image quality attributes, and the fate of males in the egg and dairy sectors, which have heavily specialised their animals. Quality can be gained but can also be lost throughout the farm-to-fork continuum. Our review highlights critical factors and periods throughout animal production and food processing routes, such as on-farm practices, notably animal feeding, preslaughter and slaughter phases, food processing techniques, and food formulation. It also reveals on-farm and processing factors that create antagonisms among quality attributes, such as the castration of male pigs, the substitution of marine-source feed by plant-based feed in fish, and the use of sodium nitrite in meat processing. These antagonisms require scientific data to identify trade-offs among quality attributes and/or solutions to help overcome these tensions. However, there are also food products that value synergies between quality attributes and between production and processing phases, particularly Geographical Indications, such as for cheese and dry-cured ham. Human epidemiological studies have found associations between consumption of animal-source foods and increased or decreased risk for chronic non-communicable diseases. These associations have informed public health recommendations. However, they have not yet considered animal production and food processing conditions. A concerted and collaborative effort is needed from scientists working in animal science, food process engineering, consumer science, human nutrition and epidemiology in order to address this research gap. Avenues for research and main options for policy action are discussed.

Food Informatics-Review of the Current State-of-the-Art, Revised Definition, and Classification into the Research Landscape

Background: The increasing population of humans, changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the Internet of Things (IoT) and Artificial Intelligence (AI) technology, including Machine Learning and data analytics, might help to account for these challenges. Scope and Approach: Several research perspectives, among them Precision Agriculture, Industrial IoT, Internet of Food, or Smart Health, already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept is Food Informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of Food Informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangent to the food production process and delineate it from other, existing research streams in the domain. Key Findings and Conclusions: Many different concepts related to the digitalization in food science overlap. Further, Food Informatics is vaguely defined. In this paper, we provide a clear definition of Food Informatics and delineate it from related concepts. We corroborate our new perspective on Food Informatics by presenting several case studies about how it can support the food production as well as the intermediate steps until its consumption, and further describe its integration with related concepts.

Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as "Pure (80 samples)" and "Adulterated (90 samples)". Lard was mixed with the ratio of 10-50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700-1800 cm^-1 and 2800-3000 cm^-1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

Chemometric Classification of Cocoa Bean Shells Based on Their Polyphenolic Profile Determined by RP-HPLC-PDA Analysis and Spectrophotometric Assays

The cocoa bean shell (CBS), a byproduct from the cocoa industry, was recently proposed as a functional and low-cost ingredient, mainly because of its content in polyphenols. However, vegetal food products could significantly differ in their chemical composition depending on different factors such as their geographical provenience. This work is aimed to determine the polyphenolic and methylxanthine profile of different CBS samples and utilize it for achieving their differentiation according to their geographical origin and variety. RP-HPLC-PDA was used to determine the CBS polyphenolic profile. Spectrophotometric assays were used to obtain the total phenolic, flavonoid, and tannin contents, as well as to evaluate their radical scavenging activity. The results obtained from both methods were then compared and used for the CBS differentiation according to their origin and varieties through chemometric analysis. RP-HPLC-PDA allowed to determine 25 polyphenolic compounds, as well as the methylxanthines theobromine and caffeine. Polyphenolic profile results highlighted significant differences among the analyzed samples, allowing for their differentiation based on their geographical provenience. Similar results were achieved with the results of the spectrophotometric assays, considered as screening methods. Differentiation based on CBS variety was instead obtained based on the HPLC-determined methylxanthine profile.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Multi-chemical analysis combined with chemometrics to characterize PDO and PGI Italian apples

BACKGROUND

The use of PDO (protected designation of origin) and PGI (protected geographical indication) labels allows to protect and promote agricultural products characterized by unique features related to the place of origin and traditional know-how. However, the presence of non-authentic products in the market represents a fraud that can be tackled applying analytical techniques combined with chemometric analysis. In this study, we applied multi-element and multi-isotope analysis to characterize PDO and PGI apples cultivated in northern Italy, comparing them with Italian apples without labels of geographical indications.

RESULTS

The multi-element and multi-isotope approach allowed to characterize the apples cultivated in northern Italy. Despite a significant effect of the sampling sites on the apple composition, the comparison of the multi-chemical fingerprint of the apples significantly varied among cultivation areas. Results of this characterization were used to classify samples according to their cultivation area applying a linear discriminant analysis (LDA). Outputs of the LDA showed that correct sample classification can be successfully achieved (balanced accuracy > 96%). Moreover, using a selection of variables, it was possible to correctly classify samples also at regional level.

CONCLUSION

The presented evidences indicate that the multi-element and multi-isotope fingerprint can be successfully applied to traceability studies. The combination of this characterization with chemometric tools allows the classification of Italian apples based on their origin both on a national and regional scale. This approach represents an interesting tool to enhance and protect PDO and PGI Italian products. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using plant physiological stable oxygen isotope models to counter food fraud

Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30-$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable oxygen isotope models simulate how precipitation δ18O values and climatic variables shape the δ18O values of water and organic compounds in plants. These models have the potential to simplify, speed up, and improve conventional stable isotope applications and produce temporally resolved, accurate, and precise region-of-origin assignments for agricultural food products. However, the validation of these models and thus the best choice of model parameters and input variables have limited the application of the models for the origin identification of food. In our study we test model predictions against a unique 11-year European strawberry δ18O reference dataset to evaluate how choices of input variable sources and model parameterization impact the prediction skill of the model. Our results show that modifying leaf-based model parameters specifically for fruit and with product-independent, but growth time specific environmental input data, plant physiological isotope models offer a new and dynamic method that can accurately predict the geographic origin of a plant product and can advance the field of stable isotope analysis to counter food fraud.

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Table olives, the number one consumed fermented food in Europe, are widely consumed as they contain many valuable ingredients for health. It is also a food which may be the subject of adulteration, as many different olive varieties with different geographical origin, exist all over the word. In the present study, the image analysis of stones of six main Greek protected designation of origin (PDO) table olive varieties was performed for the control of their authentication and discrimination, with cv. Prasines Chalkidikis, cv. Kalamata Olive, cv. Konservolia Stylidas, cv. Konservolia Amfissis, cv. Throuba Thassos and cv. Throuba Chios being the studied olive varieties. Orthogonal partial least square discriminant analysis (OPLS-DA) was used for discrimination and classification of the six Greek table olive varieties. With a 98.33% of varietal discrimination, the OPLS-DA model proved to be an efficient tool to authentify table olive varieties from their morphological characteristics.

ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder

In a preliminary study, commercial insect powders were successfully identified using infrared spectroscopy combined with multivariate analysis. Nonetheless, it is necessary to check if this technology is capable of discriminating, predicting, and quantifying insect species once they are used as an ingredient in food products. The objective of this research was to study the potential of using attenuated total reflection Fourier transform mid-infrared spectroscopy (ATR-FTMIR) combined with multivariate analysis to discriminate doughs and 3D-printed baked snacks, enriched with Alphitobius diaperinus and Locusta migratoria powders. Several doughs were made with a variable amount of insect powder (0–13.9%) replacing the same amount of chickpea flour (46–32%). The spectral data were analyzed using soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) algorithms. SIMCA models successfully discriminated the insect species used to prepare the doughs and snacks. Discrimination was mainly associated with lipids, proteins, and chitin. PLSR models predicted the percentage of insect powder added to the dough and the snacks, with determination coefficients of 0.972, 0.979, and 0.994 and a standard error of prediction of 1.24, 1.08, and 1.90%, respectively. ATR-FTMIR combined with multivariate analysis has a high potential as a new tool in insect product authentication.

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

Authentication of berries and berry-based food products

Berries represent one of the most important and high-valued group of modern-day health-beneficial "superfoods" whose dietary consumption has been recognized to be beneficial for human health for a long time. In addition to being delicious, berries are rich in nutrients, vitamins, and several bioactive compounds, including carotenoids, flavonoids, phenolic acids, and hydrolysable tannins. However, due to their high value, berries and berry-based products are often subject to fraudulent adulteration, commonly for economical gain, but also unintentionally due to misidentification of species. Deliberate adulteration often comprises the substitution of high-value berries with lower value counterparts and mislabeling of product contents. As adulteration is deceptive toward customers and presents a risk for public health, food authentication through different methods is applied as a countermeasure. Although many authentication methods have been developed in terms of fast, sensitive, reliable, and low-cost analysis and have been applied in the authentication of a myriad of food products and species, their application on berries and berry-based products is still limited. The present review provides an overview of the development and application of analytical chemistry methods, such as isotope ratio analysis, liquid and gas chromatography, spectroscopy, as well as DNA-based methods and electronic sensors, for the authentication of berries and berry-based food products. We provide an overview of the earlier use and recent advances of these methods, as well as discuss the advances and drawbacks related to their application.

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.

Front-face synchronous fluorescence spectroscopy for rapid and non-destructive determination of free capsanthin, the predominant carotenoid in chili (Capsicum annuum L.) powders based on aggregation-induced emission

Capsanthin is the major natural carotenoid pigment in red chili pepper possessing important bioactivity. Its conventional determination method is high performance liquid chromatography (HPLC) with complex and tedious sample pretreatment. In this study, synchronous front-face fluorescence spectroscopy (FFFS) was applied for the fast and non-invasive detection of free capsanthin in chili powders. Although capsanthin was only weak fluorescent in solution state, it showed strong fluorescence in two separated regions in front-face geometry which could also be clearly observed in chili powders. The mechanisms of these emissions are revealed to be aggregation-induced emission (AIE) and J-aggregate formation (JAF). The free capsanthin in 85 chili powder samples were determined by HPLC as in the range of 0.6-3.0 mg/g. The total synchronous FFFS spectra of these samples were scanned. Simple first-order models were built by partial least square regression (PLSR), and were validated by 5-fold cross-validation and external validation. The coefficients of determination (R²) were higher than 0.9, and the root mean square errors (RMSE) were less than 0.2 mg/g. The relative error of prediction (REP) was 9.9%, and the residual predictive deviation (RPD) was 3.7. The method was applied for the estimation of free capsanthin in several real-world samples with satisfactory analytical results. The average relative error to HPLC reference values was -11.8%.

Wheat authentication：An overview on different techniques and chemometric methods

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers

Standard methods for chemical food safety testing in official laboratories rely largely on liquid or gas chromatography coupled with mass spectrometry. Although these methods are considered the gold standard for quantitative confirmatory analysis, they require sampling, transferring the samples to a central laboratory to be tested by highly trained personnel, and the use of expensive equipment. Therefore, there is an increasing demand for portable and handheld devices to provide rapid, efficient, and on-site screening of food contaminants. Recent technological advancements in the field include smartphone-based, microfluidic chip-based, and paper-based devices integrated with electrochemical and optical biosensing platforms. Furthermore, the potential application of portable mass spectrometers in food testing might bring the confirmatory analysis from the laboratory to the field in the future. Although such systems open new promising possibilities for portable food testing, few of these devices are commercially available. To understand why barriers remain, portable food analyzers reported in the literature over the last ten years were reviewed. To this end, the analytical performance of these devices and the extent they match the World Health Organization benchmark for diagnostic tests, i.e., the Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users (ASSURED) criteria, was evaluated critically. A five-star scoring system was used to assess their potential to be implemented as food safety testing systems. The main findings highlight the need for concentrated efforts towards combining the best features of different technologies, to bridge technological gaps and meet commercialization requirements.

Amino Acid Profiling with Chemometric Analysis as a Feasible Tool for the Discrimination of Marine-Derived Peptide Powders

Marine-derived peptide powders have suffered from adulteration via the substitution of lower-price peptides or the addition of adulterants in the market. This study aims to establish an effective approach for the discrimination and detection of adulterants for four representative categories of marine-derived peptide powders, namely, oyster peptides, sea cucumber peptides, Antarctic krill peptides, and fish skin peptides, based on amino acid profiling alongside chemometric analysis. The principal component analysis and orthogonal partial least squares discriminant analysis results indicate that four categories of marine-derived peptides could be distinctly classified into four clusters and aggregated with the respective raw materials. Taurine, glycine, lysine, and protein contents were the major discriminants. A reliable classification model was constructed and validated by the prediction dataset, mixture sample dataset, and unclassified sample dataset with accuracy values of 100%, 100%, and 100%, respectively.

Assessment of multiple stable isotopes for tracking regional and organic authenticity of plant products in Hesse, Germany

As demand for regional and organically produced foodstuff has increased in Europe, the need has arisen to verify the products' origin and production method. For food authenticity tracking (production method and origin), we examined 286 samples of wheat (Triticum aestivum), potatoes (Solanum tuberosum), and apples (Malus domestica) from different regions in Germany for their stable isotope compositions of oxygen, hydrogen, carbon, nitrogen and sulphur. Single-variate authentication methods were used. Suitable isotope tracers to determine wheat's regional origin were δ¹⁸O and δ³⁴S. δ¹³C helped to distinguish between organic and conventional wheat samples. For the separation of the production regions of potatoes, several isotope tracers were suitable (e.g. δ¹⁸O, δ²H, δ¹⁵N, δ¹³C and δ³⁴S isotopes in potato protein), but only protein δ¹⁵N was suitable to differentiate between organic and conventional potato samples. For the apple samples, ²H and ¹⁸O isotopes helped to identify production regions, but no significant statistical differences could be found between organically and conventionally farmed apples. For food authenticity tracking, our study showed the need to take the various isotopes into account. There is an urgent need for a broad reference database if isotope measurements are to become a main tool for determining product's origin.

Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

Use of non-volatile compounds for the classification of specialty and traditional Brazilian coffees using principal component analysis

Coffee beans contain different volatile and non-volatile compounds that are responsible for their flavor and aroma. Herein, principal component analysis (PCA) was employed to correlate the non-volatile composition of specialty and traditional coffees with drink quality. The quantified non-volatile compounds included caffeine, chlorogenic acid, caffeic acid, and nicotinic acid in both types of coffee samples, while 5-hydroxymethylfurfural was only quantified in the specialty coffee samples. The most abundant compounds present in specialty coffees were associated with the aroma and flavor, affording a high drink quality. In traditional coffees, the most abundant compounds included nicotinic acid and caffeine, indicating a stronger roasting process, loss of sensory characteristics, and blended formulations. PCA showed a distinction between the traditional and specialty coffees such that a relationship between the contents of the compounds in each type of coffee, quality, and classification could be established.

Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics

Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, ¹H and ¹³C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using ¹H and ¹³C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Spatial Fingerprinting: Horizontal Fusion of Multi-Dimensional Bio-Tracers as Solution to Global Food Provenance Problems

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.

Multi-element signature of cuttlefish and its potential for the discrimination of different geographical provenances and traceability

The measurement and analysis of fifty-two elements by quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and direct mercury analysis were applied to origin discrimination of Italian traditional cuttlefish (Chioggia, Venice lagoon) from Mediterranean and Atlantic samples. A total 68 specimens were analyzed in triplicates to generate 204 mass spectra profiles which were statistically processed by different chemometric techniques. Loading weights from principal component analysis as input for linear discriminant analysis (LW-LDA), stepwise-LDA (S-LDA) and variable influence of projection-partial least square discriminant analysis (VIP-PLS-DA) were used to classify samples while retaining the lowest possible number of key variables. VIP-PLS-DA was found to be the best variable selection-discriminant tool combo since the selected Na-Co-B-K-Cd-V-U-Rb-Ni-Ba-Cu-As-Sr-Mn-Mo-Li-Ca-Mg-Se-Bi-Cs-P-Y elemental pattern allowed the samples to be classified with 100% sensitivity, specificity and accuracy.

The Untargeted Capability of NMR Helps Recognizing Nefarious Adulteration in Natural Products

Curcuma longa (turmeric) has an extensive history of ethnomedical use for common ailments, and "curcumin"-containing dietary supplements (CDS) are a highly visible portion of today's self-medication market. Owing to raw material cost pressure, CDS products are affected by economically motivated, nefarious adulteration with synthetic curcumin ("syncumin"), possibly leading to unexpected toxicological issues due to "residual" impurities. Using a combination of targeted and untargeted (phyto)chemical analysis, this study investigated the botanical integrity of two commercial "turmeric" CDS with vitamin and other additives that were associated with reported clinical cases of hepatotoxicity. Analyzing multisolvent extracts of the CDS by 100% quantitative ¹H NMR (qHNMR), alone and in combination with countercurrent separation (CCS), provided chemical fingerprints that allowed both the targeted identification and quantification of declared components and the untargeted recognition of adulteration. While confirming the presence of curcumin as a major constituent, the universal detection capability of NMR spectroscopy identification of significant residual impurities, including potentially toxic components. While the loss-free nature of CCS captured a wide polarity range of declared and unwanted chemical components, and also increased the dynamic range of the analysis, (q)HNMR determined their mass proportions and chemical constitutions. The results demonstrate that NMR spectroscopy can recognize undeclared constituents even if they represent only a fraction of the mass balance of a dietary supplement product. The chemical information associated with the missing 4.8% and 7.4% (m/m) in the two commercial samples, exhibiting an otherwise adequate curcumin content of 95.2% and 92.6%, respectively, pointed to a product integrity issue and adulteration with undeclared synthetic curcumin. Impurities from synthesis are most plausibly the cause of the observed adverse clinical effects. The study exemplifies how the simultaneously targeted and untargeted analytical principle of the 100% qHNMR method, performed with entry-level high-field instrumentation (400 MHz), can enhance the safety of dietary supplements by identifying adulterated, non-natural "natural" products.

Elemental and Isotopic Characterization of Tobacco from Umbria

Umbrian tobacco of the Virginia Bright variety is one of the most appreciated tobaccos in Europe, and one characterized by an excellent yield. In recent years, the Umbria region and local producers have invested in introducing novel practices (for production and processing) focused on environmental, social, and economic sustainability. Due to this, tobacco from Umbria is a leading commodity in the global tobacco industry, and it claims a high economic value. The aim of this study is then to assess if elemental and isotopic compositions can be used to protect the quality and geographical traceability of this particular tobacco. For the first time the characteristic value ranges of the stable isotope ratios of the bio-elements as a whole (δ²H, δ¹³C, δ¹⁵N, δ¹⁸O, and δ³⁴S) and of the concentration of 56 macro- and micro-elements are now available, determined in Virginia Bright tobacco produced in two different areas of Italy (Umbria and Veneto), and from other worldwide geographical regions. The ranges of variability of elements and stable isotope ratios had slightly different results, according to the three geographical origins considered. In particular, Umbria samples presented significantly lower content of metals potentially dangerous for human health. The results of this first exploratory work highlight the possibility of characterizing tobacco from Umbria, and suggest widening the scope of the survey throughout Italy and foreign regions, in order to be used to describe the geographical origin of tobacco in general and verify the origin of the products on the market.

The application of image processing for cultivar discrimination of apples based on texture features of the skin, longitudinal section and cross-section

The study was aimed at the evaluation of the usefulness of textures of the outer surface from the images of apple skin and flesh for discrimination of different cultivars. The texture parameters were calculated from color channels: R, G, B, L, a, b, U, V, H, S, I, X, Y, Z. In the case of cultivar discrimination performed for the apple skin, the highest accuracies were obtained for textures from channels R, a and X. In the case of channels R and a, the apples were classified with the total accuracy of up to 93%. For channel X, the highest total accuracy was 90%. For discrimination based on the textures selected from images of a longitudinal section of apples, the total accuracy reached 100% for channels G, b and U. In the case of the cross-section images, the total accuracies were also satisfactory and reached 93% for channel G, 97% for channels b and U. The obtained results proved that the use of image processing based on textures can allow the discrimination of apple cultivars with a high probability of up to 100% in the case of textures selected from images of a longitudinal section. The results can be applied in practice for cultivar discrimination and detection of the falsification of apple cultivars. The obtained results revealed that texture features can allow for cultivar identification of apples with a very high probability in an inexpensive, objective, and fast way.

Graphic abstract

Honey authenticity: analytical techniques, state of the art and challenges

Honey is a high-value, globally consumed, food product featuring a high market price strictly related to its origin. Moreover, honey origin has to be clearly stated on the label, and quality schemes are prescribed based on its geographical and botanical origin. Therefore, to enhance food quality, it is of utmost importance to develop analytical methods able to accurately and precisely discriminate honey origin. In this study, an all-time scientometric evaluation of the field is provided for the first time using a structured keyword on the Scopus database. The bibliometric analysis pinpoints that the botanical origin discrimination was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. Based on these results, we comprehensively reviewed analytical techniques that have been used in honey authenticity studies. Analytical breakthroughs and bottlenecks on methodologies to assess honey quality parameters using separation, bioanalytical, spectroscopic, elemental and isotopic techniques are presented. Emphasis is given to authenticity markers, and the necessity to apply chemometric tools to reveal them. Altogether, honey authenticity is an ever-growing field, and more advances are expected that will further secure honey quality.

An Overview of the Factors Influencing Apple Cider Sensory and Microbial Quality from Raw Materials to Emerging Processing Technologies

Given apple, an easily adapted culture, and a large number of apple varieties, the production of apple cider is widespread globally. Through the fermentation process, a series of chemical changes take place depending on the apple juice composition, type of microorganism involved and technology applied. Following both fermentations, alcoholic and malo-lactic, and during maturation, the sensory profile of cider changes. This review summarises the current knowledge about the influence of apple variety and microorganisms involved in cider fermentation on the sensory and volatile profiles of cider. Implications of both Saccharomyces, non-Saccharomyces yeast and lactic acid bacteria, respectively, are discussed. Also are presented the emerging technologies applied to cider processing (pulsed electric field, microwave extraction, enzymatic, ultraviolet and ultrasound treatments, high-pressure and pulsed light processing) and the latest trends for a balanced production in terms of sustainability, authenticity and consumer preferences.

Multivariate approach for the authentication of vanilla using infrared and Raman spectroscopy

Many different versions of vanilla extracts exist in the market in a variety of origins, purity levels and composition with little effective regulation. In this study, vanilla is authenticated both in terms of purity and geographical origin applying a multivariate approach using near infrared (NIR), mid infrared (MIR) and Raman spectroscopy following a complex experimental design. Partial least squares-discriminant analysis (PLS-DA) was applied to the spectral data to produce qualitative models. The prediction accuracy of the models was externally validated from the specific success/error contingencies. The results showed that MIR and Raman are reliable for authenticating vanilla in terms of purity, obtaining sensitivity, specificity, precision, and efficiency values equal to 1.00, and Raman is especially suitable for indicating the geographical origin of vanilla extracts, achieving performance metrics around 0.9.

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

Assessment of paprika geographical origin fraud by high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprinting

Paprika production under the protected designation of origin (PDO) standardized procedures leads to more quality products. However, it is also related to higher retail prices, making them susceptible to adulteration with low-quality paprika or its agricultural origin's mislabeling. Therefore, in this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints, strongly related to phenolic acid and polyphenolic compounds, were proposed as chemical markers to assess the classification of paprika from five European regions (three Spanish PDO, Hungary, and the Czech Republic), through a classification decision tree constructed by partial least squares regression-discriminant analysis (PLS-DA) models. After external validation, an excellent classification accuracy of 97.9% was achieved. Moreover, the chromatographic fingerprints were also proposed to detect and quantitate two different paprika geographical origin blend scenarios by partial least squares (PLS) regression. Low external validation and prediction errors -with values below 1.6 and 10.7%, respectively- were obtained.