Potential and limitations of non-targeted fingerprinting for authentication of food in official control

Hue Parameter Fluorescence Identification of Edible Oils with a Smartphone

Food fraud can be highly lucrative, and high accuracy authentication of various foodstuffs is becoming essential. Olive oil is one of the most investigated food matrices, due to its high price and low production globally, with recent food fraud examples showing little or no high quality olive oil in the tested oils. Here a simple method using a 405 nm LED flashlight and a smartphone is developed for edible oil authentication. Identification is fingerprinted by intrinsic fluorescent compounds in the oils, such as chlorophylls and polyphenols. This study uses the hue parameter of HSV-colorspace to authenticate 24 different edible oils of 9 different types and 15 different brands. For extra virgin olive oil, all nine samples are well separated from the other oil samples. The rest of the samples were also well type-distinguished by the hue parameter, which is complemented by hue-histogram analysis. This opens up opportunities for low-cost and high-throughput smartphone field-testing of edible oils on all levels of the production and supply chain.

Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.

From the Field to the Bottle—An Integrated Strategy for Wine Authenticity

The wine sector is one of the most economically important agro-food businesses. The wine market value is largely associated to terroir, in some cases resulting in highly expensive wines that attract fraudulent practices. The existent wine traceability system has some limitations that can be overcome with the development of new technological approaches that can tackle this problem with several means. This review aims to call attention to the problem and to present several strategies that can assure a more reliable and authentic wine system, identifying existent technologies developed for the sector, which can be incorporated into the current traceability system.

Integrated analytical assets aid botanical authenticity and adulteration management

This article reviews and develops a perspective for the meaning of authenticity in the context of quality assessment of botanical materials and the challenges associated with discerning adulterations vs. contaminations vs. impurities. Authentic botanicals are by definition non-adulterated, a mutually exclusive relationship that is confirmed through the application of a multilayered set of analytical methods designed to validate the (chemo)taxonomic identity of a botanical and certify that it is devoid of any adulteration. In practice, the ever-increasing sophistication in the process of intentional adulteration, as well as the growing number of botanicals entering the market, altogether necessitate a constant adaptation and reinforcement of authentication methods with new approaches, especially new technologies. This article summarizes the set of analytical methods - classical and contemporary - that can be employed in the authentication of botanicals. Particular emphasis is placed on the application of untargeted metabolomics and chemometrics. An NMR-based untargeted metabolomic model is proposed as a rapid, systematic, and complementary screening for the discrimination of authentic vs. potentially adulterated botanicals. Such analytical model can help advance the evaluation of botanical integrity in natural product research.

Recent Developments and Digital Perspectives in Food Safety and Authenticity

Food safety is of fundamental importance for the food processing industry, food retailers and distributors, and competent authorities because of its potentially direct impact on the health of consumers. Next to the prevention of microbiological, chemical, and physical hazards, increasing efforts are currently made to combat risks associated with food fraud or food authenticity. Food safety management systems nowadays comprise food safety, food defense, and food fraud prevention measures, trying to cope with the increasing complexity and globalization of the food supply chains. Future digital opportunities include the prediction of food safety and food authenticity issues by handling structured and unstructured data retrieved from various sources and origins to ensure the health of consumers and to minimize economical losses.

Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

Overview on Untargeted Methods to Combat Food Frauds: A Focus on Fishery Products

Authenticity and traceability of food products are of primary importance at all levels of the production process, from raw materials to finished products. Authentication is also a key aspect for accurate labeling of food, which is required to help consumers in selecting appropriate types of food products. With the aim of guaranteeing the authenticity of foods, various methodological approaches have been devised over the past years, mainly based on either targeted or untargeted analyses. In this review, a brief overview of current analytical methods tailored to authenticity studies, with special regard to fishery products, is provided. Focus is placed on untargeted methods that are attracting the interest of the analytical community thanks to their rapidity and high throughput; such methods enable a fast collection of “fingerprinting signals” referred to each authentic food, subsequently stored into large database for the construction of specific information repositories. In the present case, methods capable of detecting fish adulteration/substitution and involving sensory, physicochemical, DNA-based, chromatographic, and spectroscopic measurements, combined with chemometric tools, are illustrated and commented on.

High Resolution NMR Spectroscopy as a Structural and Analytical Tool for Unsaturated Lipids in Solution

Mono- and polyunsaturated lipids are widely distributed in Nature, and are structurally and functionally a diverse class of molecules with a variety of physicochemical, biological, medicinal and nutritional properties. High resolution NMR spectroscopic techniques including 1H-, 13C- and 31P-NMR have been successfully employed as a structural and analytical tool for unsaturated lipids. The objective of this review article is to provide: (i) an overview of the critical 1H-, 13C- and 31P-NMR parameters for structural and analytical investigations; (ii) an overview of various 1D and 2D NMR techniques that have been used for resonance assignments; (iii) selected analytical and structural studies with emphasis in the identification of major and minor unsaturated fatty acids in complex lipid extracts without the need for the isolation of the individual components; (iv) selected investigations of oxidation products of lipids; (v) applications in the emerging field of lipidomics; (vi) studies of protein-lipid interactions at a molecular level; (vii) practical considerations and (viii) an overview of future developments in the field.

Food Targeting: Geographical Origin Determination of Hazelnuts (Corylus avellana) by LC-QqQ-MS/MS-Based Targeted Metabolomics Application

A targeted metabolomics LC-ESI-QqQ-MS application for geographical origin discrimination based on 20 nonpolar key metabolites was developed, validated according to accepted guidelines and used for quantitation via stable isotope labeled internal standards in 202 raw authentic hazelnut samples from six countries (Turkey, Italy, Georgia, Spain, France, and Germany) of harvest years 2014 and 2015. Multivariate statistics were used for detection of significant variations in metabolite levels between countries and, moreover, a prediction model using support vector machine classification (SVM) was calculated yielding 100% training accuracy and 97% cross-validation accuracy, which was subsequently applied to 55 hazelnut samples for the confectionary industry gaining up to 80% correct classifications compared to declared origin. The present method demonstrates the great suitability for targeted metabolomics applications in the geographical origin determination of hazelnuts and their applicability in routine analytics.

Food Fingerprinting: Metabolomic Approaches for Geographical Origin Discrimination of Hazelnuts (Corylus avellana) by UPLC-QTOF-MS

Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for geographical origin discrimination of hazelnuts (Corylus avellana L.). Four different LC-MS methods for polar and nonpolar metabolites were evaluated with regard to best discrimination abilities. The most suitable method was used for analysis of 196 authentic samples from harvest years 2014 and 2015 (Germany, France, Italy, Turkey, Georgia), selecting and identifying 20 key metabolites with significant differences in abundancy (5 phosphatidylcholines, 3 phosphatidylethanolamines, 4 diacylglycerols, 7 triacylglycerols, and γ-tocopherol). Classification models using soft independent modeling of class analogy (SIMCA), linear discriminant analysis based on principal component analysis (PCA-LDA), support vector machine classification (SVM), and a customized statistical model based on confidence intervals of selected metabolite levels were created, yielding 99.5% training accuracy at its best by combining SVM and SIMCA. Forty nonauthentic hazelnut samples were subsequently used to estimate as realistically as possible the prediction capacity of the models.

Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy

Concerns about meat authenticity are increasing recently, due to great fraud scandals. This paper analysed real samples (43 adulterated and 12 controls) originated from criminal networks dismantled by the Brazilian Police. This fraud consisted of injecting solutions of non-meat ingredients (NaCl, phosphates, carrageenan, maltodextrin) in bovine meat, aiming to increase its water holding capacity. Five physico-chemical variables were determined, protein, ash, chloride, sodium, phosphate. Additionally, infrared spectra were recorded. Supervised classification PLS-DA models were built with each data set individually, but the best model was obtained with data fusion, correctly detecting 91% of the adulterated samples. From this model, a variable selection based on the highest VIPscores was performed and a new data fusion model was built with only one chemical variable, providing slightly lower predictions, but a good cost/performance ratio. Finally, some of the selected infrared bands were specifically associated to the presence of adulterants NaCl, tripolyphosphate and carrageenan.

Saffron authentication based on liquid chromatography high resolution tandem mass spectrometry and multivariate data analysis

Saffron is one of the oldest and most expensive spices, which is often target of fraudulent activities. In this research, a new strategy of saffron authentication based on metabolic fingerprinting was developed. In the first phase, a solid liquid extraction procedure was optimized, the main aim was to isolate as maximal representation of small molecules contained in saffron as possible. In the second step, a detection method based on liquid chromatography coupled with high-resolution mass spectrometry was developed. Initially, principal component analysis (PCA) revealed clear differences between saffron cultivated and packaged in Spain, protected designation of origin (PDO), and saffron packaged in Spain of unknown origin, labeled Spanish saffron. Afterwards, orthogonal partial least square discriminant analysis (OPLS-DA) was favorably used to discriminate between Spanish saffron. The tentative identification of markers showed glycerophospholipids and their oxidized lipids were significant markers according to their origin.

Non-invasive sensing for food reassurance

The main food quality traits of interest using non-invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health-protecting properties, nutritional characteristics and safety. A wide range of non-invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, X-ray, biosensor, microwave and terahertz, are organized according to physical principle.

Holistic Analysis Enhances the Description of Metabolic Complexity in Dietary Natural Products

In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic analysis of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concentrations. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its composition, thereby leading to an untargeted analysis. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR–based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.

Origin authentication of distillers' dried grains and solubles (DDGS)--application and comparison of different analytical strategies

In the context of products from certain regions or countries being banned because of an identified or non-identified hazard, proof of geographical origin is essential with regard to feed and food safety issues. Usually, the product labeling of an affected feed lot shows origin, and the paper documentation shows traceability. Incorrect product labeling is common in embargo situations, however, and alternative analytical strategies for controlling feed authenticity are therefore needed. In this study, distillers' dried grains and solubles (DDGS) were chosen as the product on which to base a comparison of analytical strategies aimed at identifying the most appropriate one. Various analytical techniques were investigated for their ability to authenticate DDGS, including spectroscopic and spectrometric techniques combined with multivariate data analysis, as well as proven techniques for authenticating food, such as DNA analysis and stable isotope ratio analysis. An external validation procedure (called the system challenge) was used to analyze sample sets blind and to compare analytical techniques. All the techniques were adapted so as to be applicable to the DDGS matrix. They produced positive results in determining the botanical origin of DDGS (corn vs. wheat), and several of them were able to determine the geographical origin of the DDGS in the sample set. The maintenance and extension of the databanks generated in this study through the analysis of new authentic samples from a single location are essential in order to monitor developments and processing that could affect authentication.

Data fusion methodologies for food and beverage authentication and quality assessment - a review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.