A chemometric approach toward the detection and quantification of coffee adulteration by solid-phase microextraction using polymeric ionic liquid sorbent coatings

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee

Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.

Pyrazines in food samples: Recent update on occurrence, formation, sampling, pretreatment and analysis methods

Pyrazines are a class of active aromatic substances existing in various foods. The accumulation of pyrazines has an impact on flavor and quality of food products. This review encompasses the formation mechanisms and control strategies of pyrazines via Maillard reaction (MR), including the new reactants and emerging techniques. Pyrazines characteristics are better understood through the developed sample pretreatments and detection methods. Herein, an in-depth review of pretreatments and analysis methods since 2010 is presented to explore the simple, fast, green, and effective strategies. Sample preparation methods include liquid phase extraction, solid phase extraction, supercritical fluid extraction, and microextraction methods such as liquid phase microextraction, and solid phase microextraction, etc. Detections are made by chromatographic methods, and sensors, etc. Advantages and limitations are discussed and compared for providing insights to further studies.

SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment

Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R_p² > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.

Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3).

Ionic Liquid-Assisted DLLME and SPME for the Determination of Contaminants in Food Samples

Developing effective and green methods for food analysis and separation has become an urgent issue regarding the ever-increasing concern of food quality and safety. Ionic liquids (ILs) are a new chemical medium and soft functional material developed under the framework of green chemistry and possess many unique properties, such as low melting points, low-to-negligible vapor pressures, excellent solubility, structural designability and high thermal stability. Combining ILs with extraction techniques not only takes advantage of ILs but also overcomes the disadvantages of traditional extraction methods. This subject has attracted intensive research efforts recently. Here, we present a brief review of the current research status and latest developments regarding the application of IL-assisted microextraction, including dispersive liquid–liquid microextraction (DLLME) and solid-phase microextraction (SPME), in food analysis and separation. The practical applications of ILs in determining toxic and harmful substances in food specimens with quite different natures are summarized and discussed. The critical function of ILs and the advantages of IL-based microextraction techniques over conventional extraction techniques are discussed in detail. Additionally, the recovery of ILs using different approaches is also presented to comply with green analytical chemistry requirements.

A comprehensive insight on modern green analyses for quality control determination and processing monitoring in coffee and cocoa seeds

Green analysis is defined as the analysis of chemicals in a manner where sample extraction and analysis are performed with least amounts of steps, low hazardous materials, while maintaining efficiency in terms of analytes detection. Coffee and cocoa represent two of the most popular and valued beverages worldwide in addition to their several products i.e., cocoa butter, chocolates. This study presents a comprehensive overview of green methods used to evaluate cocoa and coffee seeds quality compared to other conventional techniques highlighting advantages and or limitations of each. Green techniques discussed in this review include solid phase microextraction, spectroscopic techniques i.e., infra-red (IR) spectroscopy and nuclear magnetic resonance (NMR) besides, e-tongue and e-nose for detection of flavor. The employment of multivariate data analysis in data interpretation is also highlighted in the context of identifying key components pertinent to specific variety, processing method, and or geographical origin.

Molecular imprinting technology and poly (ionic liquid)s: Promising tools with industrial application for the removal of acrylamide and furanic compounds from coffee and other foods

Coffee is one of the most consumed beverages in the world. Coffee provides to the consumer special sensorial characteristics, can help to prevent diseases, improves physical performance and increases focus. In contrast, coffee consumption supplies a significant source of substances with carcinogenic and genotoxic potential such as furan, hydroxymethylfurfural (HMF), furfural (F), and acrylamide (AA). The present review addresses the issues around the presence of such toxic substances formed in Maillard reaction (MR) during thermal treatments in food processing, from chemical and, toxicological perspectives, occurrences in coffee and other foods processed by heating. In addition, current strategies advantages and disadvantages are presented along with application of molecular imprinting technology (MIT) and poly (ionic liquid) s (PIL) as an alternative to reduce the furan, HMF, F and AA content in coffee and other foods.

A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process

In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to a high-resolution mass spectrometer has been developed to provide the characterization of coffee beans roasted at three different levels (light, medium, and dark). In this way, it was possible to investigate how metabolites change during the roasting process in order to identify those than can be considered as relevant markers. Twenty-five percent methanol was selected as extracting solvent since it provided the highest number of molecular features. In addition, the effect of chromatographic and MS parameters was evaluated in order to obtain the most adequate separation and detection conditions. Data were analyzed using both non-supervised and supervised multivariate statistical methods to point out the most significant markers that allow group discrimination. A total of 24 and 33 compounds in positive and negative ionization modes, respectively, demonstrated to be relevant markers; most of them were from the hydroxycinnamic acids family. Graphical abstract ᅟ.

Opportunities for green microextractions in comprehensive two-dimensional gas chromatography / mass spectrometry-based metabolomics - A review

Microextractions have become an attractive class of techniques for metabolomics. The most popular technique is solid-phase microextraction that revolutionized the field of modern sample preparation in the early nineties. Ever since this milestone, microextractions have taken on many principles and formats comprising droplets, fibers, membranes, needles, and blades. Sampling devices may be customized to impart exhaustive or equilibrium-based characteristics to the extraction method. Equilibrium-based approaches may rely on additional methods for calibration, such as diffusion-based or on-fiber kinetic calibration to improve bioanalysis. In addition, microextraction-based methods may enable minimally invasive sampling protocols and measure the average free concentration of analytes in heterogeneous multiphasic biological systems. On-fiber derivatization has evidenced new opportunities for targeted and untargeted analysis in metabolomics. All these advantages have highlighted the potential of microextraction techniques for in vivo and on-site sampling and sample preparation, while many opportunities are still available for laboratory protocols. In this review, we outline and discuss some of the most recent applications using microextractions techniques for comprehensive two-dimensional gas chromatography-based metabolomics, including potential research opportunities.