The Role of Liquid Chromatography-Mass Spectrometry in Food Integrity and Authenticity

Liquid chromatography coupled to mass spectrometry (LC-MS), tandem mass spectrometry (LC-MS/MS), and high resolution mass spectrometry (LC-HRMS) today are among the most common techniques to guarantee food integrity and authenticity. Targeted approaches, where a family of characteristic bioactive substances in the analyzed food products are monitored, are a common practice to ensure food authenticity regarding the production region since bioactive substances content and distribution in food depend on multiple parameters such as climate conditions, water resources, agrochemical practices, etc. On the other hand, non-targeted approaches, such as metabolomic fingerprinting, are a common practice where a huge number of spectral detected variables in the analyzed foods are monitored. In both approaches, characteristic patterns are searched among the analyzed food products by means of statistical chemometric methods to address food characterization, classification, and authentication. In the present chapter, the role of LC-MS in combination with chemometrics to guarantee food integrity and authenticity will be discussed. Coverage of all kinds of applications is beyond the scope of the present contribution, so we will focus on the most relevant applications published in the last years by addressing the most interesting examples and important aspects in the food authenticity field

Non-targeted HPLC-FLD fingerprinting for the detection and quantitation of adulterated coffee samples by chemometrics

Coffee is today one of the most popular beverages in the world and the determination of its authenticity is an important issue considering the increase of adulteration cases in the last years. In this work, a simple and efficient non-targeted HPLC-FLD fingerprinting method was employed to detect and quantify adulteration levels in coffee samples by partial least squares (PLS) regression to guarantee food integrity and authenticity. For that purpose, different adulteration cases, involving both coffee production region and variety, were evaluated by pairs (Colombia-Ethiopia, Colombia-Nicaragua, India-Indonesia, Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia, and Vietnam Robusta-Cambodia adulteration cases). Overall, the proposed non-targeted HPLC-FLD fingerprinting strategy showed very good results with PLS cross-validation and prediction errors below 3.4% and 7.5%, respectively, for adulteration levels below 15%. Therefore, non-targeted HPLC-FLD fingerprints demonstrated to be suitable to assess coffee integrity and authenticity in the control and prevention of frauds

Liquid Chromatography-High-resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained was considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Besides, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica – Vietnam Robusta, Vietnam Arabica – Cambodia and Vietnam Robusta – Cambodia). The coffee adulteration studies carried out by partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively

Polyphenolic Profiling of Coffee Beverages by Liquid Chromatography-High-Resolution Mass Spectrometry for Classification and Characterization

The importance of monitoring the presence of bioactive compounds as food attributes for sample classification and characterization is increasing. In this study, targeted Liquid Chromatography coupled with High-Resolution Mass Spectrometry (LC-HRMS) was employed to analyze the chemical profile of polyphenolic compounds as the source of information for the characterization and classification of 306 commercial coffee samples. Coffee holds a distinguished position as one of the most widely popular beverages globally but also one of the most easily adulterated. Regrettably, in recent times, instances of coffee adulteration have been on the rise. Consequently, implementing rigorous quality control measures for coffee becomes imperative to guarantee its quality. The results obtained in this work confirm that the proposed chemical profiles serve as excellent descriptors for sample characterization and classification through the implementation of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving classification rates higher than 83.3% in PLS-DA validation. Moreover, the proposed LC-HRMS polyphenolic approach was employed to identify and measure adulteration levels in coffee samples using partial least squares (PLS) regression with prediction errors below 7.8%

Authenticity assessment and fraud quantitation of coffee adulterated with chicory, barley and blours by untargeted HPLC-UV-FLD fingerprinting and chemometrics

Coffee, one of the most popular drinks around the world, is also one of the beverages most sus-ceptible of being adulterated. Untargeted high-performance liquid chromatography with ultra-violet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulter-ated coffees involving three different and common adulterants: chicory, barley and flours. The methodologies were applied after a solid-liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulter-ants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regres-sion-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. 100% classification rates for both PLS-DA calibration and prediction models were obtained. Besides, Arabica and Robusta coffee samples were adulterated with chicory, bar-ley and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively

Untargeted HPLC-UV-FLD Fingerprinting for the Characterization, Classification, and Authentication of Tea

Tea (Camellia sinensis) is one of the most popular beverages, commonly consumed all over the world. Depending on the fermentation process, tea leaves can be categorized into three major groups: unfermented green tea, semifermented Oolong tea, and fully fermented black tea. The latter accounts for over 80% of worldwide production. The quality of tea products is determined by color, freshness, strength, and aroma. Phenolic and polyphenolic components contribute to the color and taste, whereas volatile components are directly related to the aroma. Unfortunately, food fraud is increasing globally. The widespread adulteration is the main concern for commercial functional tea extracts and tea-based nutraceuticals on the market. Especially for powdered extracts, the product quality of functional tea extracts varies highly on the market. The growing demand and interest in functional tea extracts are causing the proliferation of frauds that can seriously affect public health. Chicory, husk of pulses, and cereal starch are non-permitted materials typically employed as adulterants in tea extracts. The aim of this work was to develop an efficient untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) method in combination with chemometrics to address the characterization, classification, and authentication of tea samples, together with possible adulterants such as chicory extracts. A reversed-phase chromatographic separation was optimised, using a C18 column, and 0.1% formic acid aqueous solution and acetonitrile as the mobile phase components. The proposed methodology was applied to 87 tea samples, differing in variety and production region, and 12 chicory samples. In any case, the sample treatment consisted of sample infusion with hot? water and filtration, and the obtained HPLC-UV-FLD fingerprints were subjected to principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA) chemometric methods. Perfect discrimination was achieved between different tea varieties and chicory demonstrating that untargeted HPLC-UV-FLD fingerprints can be proposed as good sample chemical descriptors to assess tea authentication and to prevent frauds dealing with adulteration with chicory

High‐performance liquid chromatography with fluorescence detection fingerprints as chemical descriptors to authenticate the origin, variety and roasting degree of coffee by multivariate chemometric methods

BACKGROUND: Coffee is one of the most popular beverages around the world, consumed as an infusion of ground roasting coffee beans with a characteristic taste and flavor. Two main varieties, Arabica and Robusta, are worldwide produced. Besides, the interest of consumers in quality attributes related to coffee production region and varieties is increasing, being necessary encouraging the development of simple methodologies to authenticate and to guarantee the coffee origin, variety, as well as the roasting degree to prevent fraudulent practices. RESULTS: C18 high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints obtained after brewing the coffees without any sample treatment other than filtration (considerably reducing sample manipulation) were employed as sample chemical descriptors for coffee characterization and classification by principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA). PLS-DA showed good classification capabilities regarding coffee origin, variety and roasting degree when employing HPLC-FLD fingerprints although overlapping for some sample groups occurred. However, the discrimination power increased when selecting HPLC-FLD fingerprinting segments richer in discriminant features, which were deduced from PLS-DA loading plots. In this case, excellent separation was observed and 100% classification rates for both PLS-DA calibrations and predictions were obtained (all samples were correctly classified within their corresponding groups). CONCLUSION: HPLC-FLD fingerprinting segments resulted to be suitable chemical descriptors to discriminate the origin (country of production), variety (Arabica and Robusta) and roasting degree of coffee. Therefore, HPLC-FLD fingerprinting can be proposed as a feasible, simple and cheap methodology to address coffee authentication, especially for developing coffee production countries

FIA-ESI-MS Fingerprinting method with chemometrics for the characterization of adulterated coffee samples

Food products are very complex matrices, which makes the quality of these products an issue of great interest in our society. Considering the complexity of the food chain, the adulteration of food is increasing, causing food fraud cases. In this field, drinks are food products that can be very easily adulterated. This work will focus on the thematic of fraud detection in coffee, one of the most popular beverages in the world. Coffee contains an elevated number of bioactive substances (phenolic acids, polyphenols and alkaloids; being especially abundant ellagic, caffeic and chlorogenic acids) that give place to its important antioxidant activity, known for its beneficial health effects. The aim of this work was to develop an efficient non-targeted FIA-ESI-MS fingerprinting method in combination with chemometrics to achieve the characterization, classification, and authentication of coffee samples, together with possible adulterants (barley, chicory and flours) using partial least squares regression-discriminant analysis (PLS-DA) chemometric method. Besides, Arabica and Robusta coffee samples were adulterated with barley, chicory and flour and the obtained FIA-ESI-MS data subjected to partial least squares (PLS) regression. Results demonstrated the feasibility of the proposed methodology to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing good calibration and prediction errors

Non-targeted high-performance liquid chromatography with ultraviolet and fluorescence detection fingerprinting for the classification, authentication, and fraud quantitation of instant coffee and chicory by multivariate chemometric methods

Non-targeted strategies based on high-performance liquid chromatography with ultraviolet detection (HPLC-UV) and fluorescence detection (HPLC-FLD) fingerprints were evaluated to accomplish the classification and authentication of instant coffee (40 samples), instant decaf coffee (26 samples), and chicory (22 samples, including both ground and instant), as well as to detect and quantify frauds based on chicory adulteration by multivariate chemometric methods. HPLC-UV and HPLC-FLD fingerprints were simultaneously obtained with a HPLC-UV-FLD instrument, and they proved to be excellent chemical descriptors for the classification of coffee and decaf coffee against chicory samples by partial least squares regression-discriminant analysis (PLS-DA). In contrast, HPLC-UV fingerprints improved the classification results when addressing coffee against decaf coffee samples (94.4% classification rate in comparison to 83.3% for HPLC-FLD fingerprints). Besides, the proposed methodologies resulted to be excellent to detect and quantify fraud levels in coffee and decaf coffee samples adulterated with chicory by using partial least squares (PLS) regression, exhibiting good calibration linearities, calibration errors, and prediction errors. In this case, improved capabilities were observed with HPLC-FLD fingerprints, providing better PLS calibration linearities (R2>0.999), lower calibration errors (≤0.8%), and similar to better prediction errors (2.9-3.2%) in comparison to HPLC-UV fingerprints

Tea and Chicory Extract Characterization, Classification and Authentication by non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergo adulterations to reduce manufacture costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, and to detect and quantify frauds is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety, and except for some white tea extracts, perfectly dis-criminated from the chicory ones. 100% classification rates for the PLS-DA calibration models were achieved except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, showing also, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available