Towards a Mathematical Framework for Method Validation and Performance Characteristics of Non-targeted Methods for Food Authenticity

Through its suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack". Instead, they exploit all the constituents of the haystack. This new form of analytical methods is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing needs to be propagated for the benefit of ones associated in academic research, commercial development, and official control. This paper addresses the frequently asked questions around notations and terminologies surrounding NTMs. The widespread development and adoption of these methods also necessitates the need to develop approaches to NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap to approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions thereof.

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.

Advances in fingerprint and rapid methods for improved traceability in agri-food supply chains

Agri-food supply and value chain markets have become increasingly complex due to the changes in consumers demands, the development of complex food standards associated with food safety and quality, advances in technology (e.g. big data, machine learning), and changes in the food industry structure. However, recent issues related to food authenticity, adulteration, fraud, mislabelling, traceability and provenance have added a new dimension to consumers’ concerns, and food industry and regulatory bodies worldwide. The incorporation of sensing technologies combined with data analytics, are determining a paradigm shift in the way food ingredients and foods are both evaluated and monitored. This chapter discusses the utilisation of data analytics and sensing technologies to address issues related with food authenticity, adulteration, fraud, traceability and provenance in the food supply and value chains. In particular, this chapter will focus on the use of rapid analytical methods based in vibrational spectroscopy in combination with data analytics.

Unauthorized Food Manipulation as a Criminal Offense: Food Authenticity, Legal Frameworks, Analytical Tools and Cases

Food fraud is a criminal intent motivated by economic gain to adulterate or misrepresent food ingredients and packaging. The development of a reliable food supply system is at great risk under globalization, but Food Business Operators (FBOs) have a legal obligation to implement and maintain food traceability and quality at all stages of food production, processing, and distribution. Incidents of food fraud have a strong negative impact on consumer confidence in the food industry. Therefore, local and international regulatory mechanisms are established to prevent or mitigate food fraud. This review brings new perspectives linking EU and US legislation, as well as new definitions and descriptions of the criminal aspect of food fraud incidents. It also describes certain new insights into the application of state-of-the-art methods and techniques that provide valuable tools for geographic, botanical, or other chemical markers of food authenticity. The review also provides an overview of the most common cases of food fraud worldwide from 2010 to 2020. Further research is needed to support the development of predictive models for innovative approaches to adulteration, especially when some valuable nutrients are replaced by toxic ingredients. A possible solution to minimize food fraud incidents is to increase the level of risk-based inspections, establish more productive monitoring and implementation of food protection systems in the supply chain, and implement better ingredient control and certification. National and international (e.g., regional) police offices for food fraud should be introduced, possessing knowledge and skills in food, food safety, food processing, and food products, as initial positive results have emerged in some countries.

Detecting Admixture to Mango Purée of the Alphonso Cultivar (Mangifera indica L. cv. Alphonso) by 1H-NMR Spectroscopy

Food authenticity is becoming increasingly important but challenges existing analytical methods. In this study, we analyze the mango cultivar Alphonso with regard to authenticity using 1H-NMR spectroscopy. This cultivar has been termed “the king of mangoes” due to its unique flavor. Regarding its metabolites however, little is known about unique constellations that allow for differentiation of the Alphonso cultivar. We find that the Alphonso cultivar is distinguished by high levels of niacin, trigonelline, and histidine but features relatively low levels of alanine. Furthermore, we develop a model based on the local outlier factor algorithm that effectively detects admixture of non-Alphonso cultivars to Alphonso purée. This task is highly challenging because we identified no metabolites that are unique or uniquely absent in the Alphonso cultivar compared to other mango cultivars analyzed in this study. Our model shows promising results on a test set: Admixtures consisting of 35% non-Alphonso and 65% Alphonso mango purée were uncovered with a sensitivity of 88%. At the same time, our model verified Alphonso samples with a good specificity of 86%.

Microsatellite Markers in Olives (Olea europaea L.): Utility in the Cataloging of Germplasm, Food Authenticity and Traceability Studies

The olive fruit, a symbol of Mediterranean diets, is a rich source of antioxidants and oleic acid (55–83%). Olive genetic resources, including cultivated olives (cultivars), wild olives as well as related subspecies, are distributed widely across the Mediterranean region and other countries. Certain cultivars have a high commercial demand and economical value due to the differentiating organoleptic characteristics. This might result in economically motivated fraudulent practices and adulteration. Hence, tools to ensure the authenticity of constituent olive cultivars are crucial, and this can be achieved accurately through DNA-based methods. The present review outlines the applications of microsatellite markers, one of the most extensively used types of molecular markers in olive species, particularly referring to the use of these DNA-based markers in cataloging the vast olive germplasm, leading to identification and authentication of the cultivars. Emphasis has been given on the need to adopt a uniform platform where global molecular information pertaining to the details of available markers, cultivar-specific genotyping profiles (their synonyms or homonyms) and the comparative profiles of oil and reference leaf samples is accessible to researchers. The challenges of working with microsatellite markers and efforts underway, mainly advancements in genotyping methods which can be effectively incorporated in olive oil varietal testing, are also provided. Such efforts will pave the way for the development of more robust microsatellite marker-based olive agri-food authentication platforms.

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.