In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1541-4337.12341

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

This study introduces an innovative procedure for the development of a molecularly imprinted electrochemical sensor for ultra-sensitive and selective detection of patulin. Firstly, the surface of a glassy carbon electrode (GCE) was decorated by nitrogen doped graphene quantum dots (N-GQDs) and AuNPs-functionalized Cu-metal organic framework (Au@Cu-MOF) and then a layer of molecularly imprinted polymer (MIP) was grown on Au@Cu-MOF/N-GQDs/GCE by electropolymerization. Electrochemical techniques were used to characterize and study the electrochemical behavior of the MIP/Au@Cu-MOF/N-GQDs/GCE, which exhibited a stable reference peak of Au@Cu-MOF at −0.11 V after elution of patulin molecules. This peak current density decreased with rebinding of patulin molecules therefore it was considered as indicator signal. The designed MIP sensor presented a wide linear range from 0.001 to 70.0 ng mL−1, and a low detection limit (0.0007 ng mL−1). This newly developed method based on the synergistic effects of N-GQDs and Au@Cu-MOF combined with MIP technique offered outstanding selectivity, sensitivity, stability, and reproducibility. The good accuracy (recovery%, 97.6–99.4) and high precision (RSD%, 1.23–4.61) of this sensing system for analysis of apple juices proved the high potential of it for rapid and low cost determination of patulin compared to chromatographic methods.

Ultra-sensitive molecularly imprinted electrochemical sensor for patulin detection based on a novel assembling strategy using Au@Cu-MOF/N-GQDs

Food safety affects everyone worldwide and will remain a global challenge to human health in the foreseeable future requiring the rapid, sensitive, efficient and inexpensive detection of food contaminants. Biosensors have long been investigated to be part of a solution. In fact, current research trends of nanoscale science and technology, efforts of miniaturization and connectivity enabled through the internet of things boost biosensors’ capabilities to a degree that they surely will play a major part of the answer to this global challenge. Surprisingly though, the adaption of such biosensors to function along the entire food value chain and hence also include important aspects of sustainable agriculture and food fraud has been neglected so far. In this review, the latest developments of biosensors addressing these issues are presented for the years 2015–2019 and point toward important new strategies needed to truly ensure safe food in a sustainable global market.

Biosensors to support sustainable agriculture and food safety

Recent advances of molecularly imprinted polymer-based sensors in the detection of food safety hazard factors

This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.

Eunyoung Hong

Papers

Modern analytical methods for the detection of food fraud and adulteration by food category

Standardized ethanolic extract of the rhizome of Curcuma xanthorrhiza prevents murine ulcerative colitis by regulation of inflammation