Authenticity and traceability in beverages

Efficient separation of organic anions in beverages using aminosilane-functionalized capillary electrophoresis with contactless conductivity detection

BACKGROUND

Capillary electrophoresis (CE) has the advantage of rapid anion analysis, when employing a reverse electroosmotic flow (EOF). The conventional CE method utilizes dynamic coatings with surfactants like cetyltrimethylammonium bromide (CTAB) in the run buffer to reverse the EOF. However, this method suffers from very slow equilibration leading to drifting effective migration times of the analyte anions, which adversely affects the identification and quantification of peaks. Permanent coating of the capillary surface may obviate this problem but has been relatively little explored. Thus, permanent capillary surface modification by the covalent binding of 3-aminopropyltriethoxysilane (APTES) was studied as an alternative.

RESULTS

This study investigates the effect of APTES concentration for surface functionalization on EOF mobility, separation efficiency, and reproducibility of anion separation. The performance data was complemented by X-ray photoelectron spectroscopy (XPS) and contact angle (CA) measurements. The XPS measurements showed that the coverage with APTES was dependent on its concentration in the coating solution. The XPS measurements correlated well with the EOF values determined for the capillaries tested. A standard mixture of 21 anions could be baseline separated within 10 min in the capillaries with lower EOF, but not in the capillary with the highest EOF as the residence time of the analytes was too short in this case. Compared to conventional dynamic coating with CTAB, APTES-functionalized capillaries provide faster equilibration and long-term EOF stability. The application of APTES-functionalized capillaries in analyzing different beverages demonstrates the precision, reliability, and specificity in determining organic anions, providing valuable insights of their compositions.

SIGNIFICANCE

APTES coating on capillaries provides a facile approach to achieve a permanent reversal of the stable EOF to determine anions. The control of the coverage via the concentration of the reagent solution allows the tailoring of the EOF to different needs, a faster EOF for less complex samples where resolution is not challenging, while a lower EOF for higher complex samples where the focus is on separation efficiency. This enhancement in efficiency and sensitivity has been applied to analyzing organic acids in several beverages.

Compact 3D-Printed Unit for Separation of Simple Gas Mixtures Combined with Chemiresistive Sensors

Inexpensive chemiresistive sensors are often insufficiently selective as they are sensitive to multiple components of the gas mixture at the same time. One solution would be to insert a device in front of the sensor that separates the measured gas mixture and possibly isolates the unwanted components. This study focused on the fabrication and characterization of a compact unit, which was fabricated by 3D printing, for the separation and detection of simple gas mixtures. The capillary, the basic part of the compact unit, was 4.689 m long and had a diameter of 0.7 mm. The compact unit also contained a mixing chamber on the inlet side and a measuring chamber with a MiCS-6814 sensor on the outlet side. Mixtures of ethanol and water at different concentrations were chosen for characterization. The measured calibration curve was found to have a reliability of R2 = 0.9941. The study further addressed the elements of environmental friendliness of the materials used and their sustainability.

Targeted analysis for the detection of phenolics and authentication of Albanian wines using LC-DAD/ESI-MS/MS combined with chemometric tools

In recent years, Albania has seen a significant increase in wine production, which can be attributed to the growing interest in the diversity of native grape varieties. Among the most popular grape varieties are Kallmet, Shesh i zi (ShiZ), Shesh i bardhë (ShiB), and Cerruje, which are known for their distinctive wines as well as the planted area. A study was conducted to investigate the influence of the territory and vintage on phenolic compounds of single-variety wines from these grape varieties. Liquid chromatography identified and quantified thirty-one phenolic compounds, sub-grouped into flavonoids and non-flavonoids, with diode-array detection coupled to electrospray ionization tandem mass spectrometry (LC-DAD-ESI/MSn). Within the red wines group, the ShiZ variety wine presented the highest phenolic content (1037 mg/L), followed by Kallmet cv. (539 mg/L); conversely, in the white wine group, the ShiB wines (699 mg/L) were distinguished from the Cerruje variety. Gallic acid was the main phenolic compound, followed by procyanidin B3. ShiB and ShiZ had the highest levels, at 215 and 136 mg/L, respectively. Among flavanols, (+)-catechin was found in the highest levels, with the maximum in Kallmet cv. red wine (58.9 mg/L), followed by (-)-epicatechin (29.1 mg/L). The ShiB wine had the highest content of flavonols, with quercetin-3-O-glucuronide and quercetin-3-O-glucoside as the main contributors. The highest quantity of stilbenoids belonged to Kallmet red wine (1.59 mg/L). Applying Principal Component Analysis (PCA) in red and white wine groups made a good separation possible according to variety and region. However, a separation according to vintage year was not successful.

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 that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's 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 were 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). In addition, 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 with 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.

Smoothies Marketed in Spain: Are They Complying with Labeling Legislation?

There is no legal definition of a smoothie, so the European legislation applicable to its labeling is that of fruit juice. The smoothie market has grown in recent years, as it can include a wide variety of ingredients: fruits, fruit purees, honey, milk and vegetable milks, vegetables, herbs, cereals, cereal flours, seaweed, and crushed ice. In this study, 57 smoothies were reviewed. All of them were purchased in supermarkets and classified into eight types according to the main ingredients. Fifteen legal statements/items were reviewed on the pack labels: eleven mandatory and three optional. Moreover, nutrition labels, nutritional claims, images, marks, and other symbols were also reviewed. Only 22.8% of the samples complied with EU and Spanish labeling legislation. More incorrectness was related to the name of the food and the fruits included as main ingredients; other errors related to the allergy/intolerance statements, and some nutritional claims concerning vitamin C were also detected to a lesser extent. General advice is provided for consumers to interpret smoothie labels correctly. Lawmakers should amend legislation to accept the term "smoothie" as a legal name.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Can Volatiles Fingerprints be an Alternative to Isotope Ratio Mass Spectrometry in the Botanical Origin Determination of Spirits?

Mass spectrometry based quasi-electronic nose using solid-phase microextraction to introduce volatiles directly to mass spectrometer without chromatographic separation (HS-SPME-MS) was used to discriminate 45 raw spirits produced from C3 (potato, rye, wheat) and C4 (corn, sorghum) plants. The samples were also subjected to isotope ratio mass spectrometry (IRMS), which unequivocally distinguished C3 from C4 samples; however, no clear differentiation was observed for C3 samples. On the contrary, HS-SPME-MS, which uses unresolved volatile compounds "fingerprints" in a form of ions of a given m/z range and various intensities provided excellent sample classification and prediction after OPLS-DA data processing verified also by the artificial neural network (ANN).

Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review

Food adulteration refers to the alteration of food quality that takes place deliberately. It includes the addition of ingredients to modify different properties of food products for economic advantage. Color, appearance, taste, weight, volume, and shelf life are such food properties. Substitution of food or its nutritional content is also accomplished to spark the apparent quality. Substitution with species, protein content, fat content, or plant ingredients are major forms of food substitution. Origin misrepresentation of food is often practiced to increase the market demand of food. Organic and synthetic compounds are added to ensure a rapid effect on the human body. Adulterated food products are responsible for mild to severe health impacts as well as financial damage. Diarrhea, nausea, allergic reaction, diabetes, cardiovascular disease, etc., are frequently observed illnesses upon consumption of adulterated food. Some adulterants have shown carcinogenic, clastogenic, and genotoxic properties. This review article discusses different forms of food adulteration. The health impacts also have been documented in brief.

Sustainability issues along the coffee chain: From the field to the cup

The coffee industry is one of the most important commercial value chains worldwide. Nonetheless, it is also associated to several social, economic, and environmental concerns that impair its sustainability. The present review is focused on these main sustainability concerns from the field to the coffee cup, as well as on the strategies that are being developed and/or implemented to attain sustainability and circular economy principles in the different chain segments. In this context, distinct approaches have been applied, such as sustainable certifications (e.g., voluntary sustainability standards), corporate sustainability initiatives, direct trade, relationship coffee concepts, geographical indication, legislations, waste management, and byproducts valorization, among others. These strategies are addressed and discussed throughout this review, as well as their recognized advantages and limitations. Overall, there is still a long way to go to attain the much-desired sustainability in the coffee chain, being essential to join the efforts of all actors and entities directly or indirectly involved, namely, producers, retailers, roasters, governments, educational institutions (such as universities and scientific research institutes), and organizations.

Characterization and Classification of Direct and Commercial Strawberry Beverages Using Absorbance–Transmission and Fluorescence Excitation–Emission Matrix Technique

The subject of this study was to characterize the absorption and fluorescence spectra of various types of strawberry beverages and to test the possibility of distinguishing between direct juices and pasteurized commercial products on the basis of their spectral properties. An absorbance and transmission excitation–emission matrix (A-TEEM^TM) technique was used for the acquisition of spectra. The obtained spectra were analyzed using chemometric methods. The principal component analysis (PCA) revealed differences in both the absorption spectra and excitation–emission matrices (EEMs) of two groups of juices. The parallel factor analysis (PARAFAC) enabled the extraction and characterization of excitation and emission profiles and the relative contribution of four fluorescent components of juices, which were related to various groups of polyphenols and nonenzymatic browning products. Partial least squares–discriminant analysis (PLS-DA) models enabled 100% correct class assignment using the absorption spectra in the visible region, unfolded EEMs, and set of emission spectra with excitation at wavelengths of 275, 305, and 365 nm. The analysis of variable importance in projection (VIP) suggested that the polyphenols and nonenzymatic browning products may contribute significantly to the differentiation of commercial and direct juices. The results of the research may contribute to the development of fast methods to test the quality and authenticity of direct and processed strawberry juices.

High-Throughput Flow Injection Analysis–Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea Application to the Detection of Teas Adulterated with Chicory

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, including its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprinting methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to discriminate tea samples from chicory independently of the tea product variety as well as to classify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases—i.e., each tea product variety versus chicory—by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adulteration cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors below 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.

Synchronous fluorescence spectroscopy and multivariate classification for the discrimination of cachaças and rums

Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.

Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm^-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r_p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

Characterization of Musts, Wines, and Sparkling Wines Based on Their Elemental Composition Determined by ICP-OES and ICP-MS

Samples from the different processing stages in the elaboration of sparkling wine (cava)—including must, base wine, and sparkling wine—of Pinot Noir and Xarel·lo grape varieties from different vineyard qualities (A, B, C, D) have been analyzed by inductively coupled plasma (ICP) techniques to determine their elemental composition. The resulting data has been used to characterize these products according to oenological features and product qualities. For this purpose, box plot diagrams, bar charts, and principal components analysis (PCA) have been used. The study of the behavior of each given species has pointed out the relevance of some elements as markers or descriptors of winemaking processes. Among others, Cu and K are abundant in musts and their concentrations progressively decrease through the cava production process. S levels suddenly increase at the base wine step (and further decay) due to the addition of sulfites as preserving agents. Finally, concentrations of Na, Ca, Fe, and Mg increase from the first fermentation due to the addition of clarifying agents such as bentonite. PCA has been applied to try to extract solid and global conclusions on trends and chemical markers within the groups of samples more easily and efficiently than more conventional approaches.

Origin geographical classification of green coffee beans (Coffea Arabica L.) produced in different regions of the Minas Gerais state by FT-MIR and chemometric

The present work was proposal the potential evaluation of Fourier-Transform Mid-Infrared (FT-MIR) associated with chemometric approach in green beans, in order to discriminate the origin of special Arabica coffees in a single state that has heterogeneous environments. Partial Least Squares Discriminant Analysis (PLS-DA) model presented as result: 3 latent variables, R2X (cum) = 0.892, R2Y (cum) = 0.659; Q2Y (cum) = 0.494, RMSEP = 0.182387, p-value CV-Anova = 0.009, 100% of both sensitivity and specificity and the prediction classification obtained was: 100, 83.33, 100, 83.33% for class 1, class 2, class 3 and class 4, respectively. These results can be considered adequate for the proposed hypothesis. The obtained results that the regions have markers such as trigonelline, chlorogenic and fatty acids, sensitive to absorption in the mid-infrared and that are able to determine the origin of green coffee beans of Arabica. Thus, the FT-MIR associated with chemometrics has the potential to employ speed, modernity and cost reduction in the certification of origin of coffees.

•

The origin of special arabica coffee beans in the same state was discriminated using MIR.

•

The study identified green coffee beans of the same species from neighboring regions.

•

Trigonelline, chlorogenic and fatty acid absorption bands are good origin markers.

•

The coffee cultivation environment interferes decisively in the final composition.

Identification of Tentative Traceability Markers with Direct Implications in Polyphenol Fingerprinting of Red Wines: Application of LC-MS and Chemometrics Methods

This study investigated the potential of using the changes in polyphenol composition of red wine to enable a more comprehensive chemometric differentiation and suitable identification of authentication markers. Based on high performance liquid chromatography-mass spectrometry (HPLC-MS) data collected from Feteasca Neagra, Merlot, and Cabernet Sauvignon finished wines, phenolic profiles of relevant classes were investigated immediately after vinification (Stage 1), after three months (Stage 2) and six months (Stage 3) of storage, respectively. The data were subjected to multivariate analysis, and resulted in an initial vintage differentiation by principal component analysis (PCA), and variety grouping by canonical discriminant analysis (CDA). Based on polyphenol common biosynthesis route and on the PCA correlation matrix, additional descriptors were investigated. We observed that the inclusion of specific compositional ratios into the data matrix allowed for improved sample differentiation. We obtained simultaneous discrimination according to the considered oenological factors (variety, vintage, and geographical origin) as well as the respective clustering applied during the storage period. Subsequently, further discriminatory investigations to assign wine samples to their corresponding classes relied on partial least squares-discriminant analysis (PLS-DA); the classification models confirmed the clustering initially obtained by PCA. The benefits of the presented fingerprinting approach might justify its selection and warrant its potential as an applicable tool with improved authentication capabilities in red wines.

Tracing geographical origin of Lambrusco PDO wines using isotope ratios of oxygen, boron, strontium, lead and their elemental concentration

Wine identification is one of the most important aspects in the classification of wines and consumer protection. In particular, assuring wine authenticity is a crucial issue on which researchers are focusing on. This study aims to evaluate the feasibility of using chemical (B, Pb and Sr concentration) and isotopic compositions (δ¹¹B, ^20yPb/^20xPb, ⁸⁷Sr/⁸⁶Sr and δ¹⁸O) of wine samples to trace their geographic origins. Different PDO Lambrusco wines coming from a confined area of northern Italy were analyzed and all the isotopic systematics were monitored by using a multi collector inductively coupled plasma mass spectrometer (MC-ICP/MS).

The obtained results showed that boron isotope ratio measurements led to a satisfactory degree of accuracy and precision (measured value, n = 28, ¹¹B/¹⁰B of NIST SRM 951a equal to 4.04343 ± 0.00178, (u = 2s) with a certified value of 4.04362 ± 0.00136 (u = 2s). Furthermore, in the present study, it has been possible to highlight significant differences among samples by means of one-way analysis of variance (ANOVA) and post hoc Tukey-Kramer test. Finally, Principal Component Analysis (PCA) was also carried out in order to evaluate to which extent the different PDOs can be distinguished from each other, taking into account the whole set of geographical origin descriptors. Although δ¹¹B provided more sensitive information, the obtained results highlighted the important to consider the synergistic combination of all the investigated parameters to trace the different samples and the need to combine the obtained values with the same parameters evaluated in the soil, water and fertilizer as well.

•

Multi-parameter approach was used to assess wine geographical origin.

•

Validation of ¹¹B/¹⁰B measurements was accomplished by multicollector ICP/MS.

•

¹¹B/¹⁰B, ⁸⁷Sr/⁸⁶Sr, ^20yPb/^20xPb, ¹⁸O/¹⁶O, and relative elemental content, were measured in Lambrusco PDO wines.

•

Anova, Tukey-Kramer and multivariate (PCA) analysis was employed to investigate differences and similarities among samples.

•

Geographic differentiation of PDO wines resulted to be based more on B and Sr variables than Pb and O ones.

The application of right-angle fluorescence spectroscopy as a tool to distinguish five autochthonous commercial Greek white wines

White wine is among the most widely consumed alcoholic beverages. Varietal discrimination of wines has received increasing attention. Today's consumers require a sense of authenticity and are deterred by falsehood or misrepresentation in product marketing. However, wine can involve various types of frauds, which directly affects the distribution of wine in national and international markets. Right-angle fluorescence spectroscopy is a simple and rapid analytical technique that in combination with chemometric algorithms, constitutes a novel method for wine authentication. In this study, the stepwise-Linear Discriminant Analysis algorithm was applied in three representative spectral regions related to phenolic compounds for the purpose of distinguishing white wines according to the grape variety. The wavelength at 310 nm attributed to the hydroxycinnamic acids and stilbene provided a higher classification rate (95.5%) than the λex 280 and 295 nm regions (79.8%), suggesting that these compounds are highly related to the botanical origin of samples. The chemometric models were validated utilizing cross-validation and an external validation set to enhance the robustness of the proposed methodology. The above-mentioned methodology constitutes a powerful tool for the varietal discrimination of white wines and can be used in industrial setting. The ultimate goal of this study is to contribute to the efforts towards the authentication of Greek white wine which will eventually support producers and suppliers to remain competitive and simultaneously protect the consumers from fraudulent practices.

Authentic or cosmetic: stakeholders’ attribution of firms’ corporate social responsibility claims

Purpose

To date, many firms tend to use corporate social responsibility (CSR) communication and marketing as a means to offset their irresponsible behaviors and unscrupulous business practices. Often time, they can easily get away with this in the context where the institutional settings are weak, and corporate social irresponsibility (CSIR) and corruption are widespread. The purpose of this study is to explore stakeholders’ attribution concerning CSR claims of four beverage manufacturing companies operating in America’s poorest country (Haiti) where CSIR and corruption remain widespread. This study also explores whether there are differences in demographic characteristics (e.g. gender, corporate affiliation and education) regarding stakeholders’ attribution of CSR claims of these companies.

Design/methodology/approach

Given the exploratory nature of this study, an inductive research approach (qualitative plus quantitative) and supported by an interpretive approach were used.

Findings

The overall results of this study show that internal (employees) and external stakeholders alike consider the CSR claims of these companies as “cosmetic,” with no significant difference in their affiliation. The results also show no significant differences in the age groups but significant differences in gender and level of education regarding stakeholders’ attribution of firms’ CSR claims.

Originality/value

By addressing firms’ CSR claims from the perspectives of internal and external stakeholders through means of a mixed methods approach, this study adds an important contribution to the relevant literature.

Recent advances in NMR-based metabolomics of alcoholic beverages

•

NMR-based techniques can be used for establishing metabolic “fingerprint” .

•

Biomarkers for discrimination of wine varietals were identified.

•

COSY and DOSY techniques may aid in assigning phenolic compounds and disaccharides.

•

NMR-based metabolomic studies of alcoholic beverages remain limited in Asia.

Alcoholic beverages have a complex chemistry that can be influenced by their alcoholic content, origin, fermentation process, additives, and contaminants. The complex composition of these beverages leave them susceptible to fraud, potentially compromising their authenticity, quality, and market value, thus increasing risks to consumers’ health. In recent years, intensive studies have been carried out on alcoholic beverages using different analytical techniques to evaluate the authenticity, variety, age, and fermentation processes that were used. Among these techniques, NMR-based metabolomics holds promise in profiling the chemistry of alcoholic beverages, especially in Asia where metabolomics studies on alcoholic beverages remain limited.

Use of headspace GC/MS combined with chemometric analysis to identify the geographic origins of black tea

Some black teas demand high market prices. Black tea samples (306) collected from 10 geographic origins, including China (Guxi, Likou, Jinzipai, Guichi, Dongzhi, Changning, Wuyishan, Shaowu), India (Darjeeling), and Sri Lanka (Kandy), were analyzed using headspace volatilization followed by GC/MS (HS-GC/MS). Forty-eight volatile compounds were identified. The aroma compounds were mainly identified as alcohols, aldehydes, ketones, and esters. Analysis of either full-spectrum data or 22 tea compounds shared among the samples with k-Nearest Neighbor (k-NN) and Random Forest (RF) models discriminated all origins at 100% using KNN and 95% with RF using either data set. The discrimination rates using 2 key aroma compounds (linalool and geraniol) by k-NN were 100% for nine origins, with the rate for Guxi area at 89%, because 3 samples were classified to Jinzipai. The findings support the use of HS-GC/MS combined with chemometrics as a tool to identify the origin of black tea.

Stable isotopes verify geographical origin of Tibetan chicken

Due to the particularity of origin area, the yield and price of Tibetan chicken are different from those in the plain areas, which leads to chickens from the plains being disguised as Tibetan chickens. In present study, Tibetan chicken and chicken from other four provinces in plain areas were distinguished by stable isotopes and chemometric methods. The cross-validation rate was 97.6%. Secondly, correlation between mean δ¹³C, δ¹⁵N, δ²H and δ¹⁸O values in chicken samples and environmentalparameters was investigated. These indicators are closely related to geographical environment. δ¹³C and δ¹⁵N values were positively correlated with longitude (R = 0.955) and altitude (R = 0.796), respectively. δ¹⁸O and δ²H values were negatively correlated with latitude (R = 0.903) and altitude (R = 0.938), respectively. At last, we used stable isotopes to trace Tibetan chicken from the different area of Tibet. When four stable isotope ratios were combined, we can distinguish them.

Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours 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 susceptible of being adulterated. Untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulterated 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 adulterants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. In addition, Arabica and Robusta coffee samples were adulterated with chicory, barley, 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.

Volatilome changes during probiotic fermentation of combined soy and rice drinks

Plant-based drinks as a substitute for animal milk consumption are crucial products in the food industry. Soy and rice drinks are the most successful milk substitutes but are low in fiber and protein contents, respectively, whilst being rich in sugars. Generally, an improvement is foreseen; thus, apart from supplement addition, a natural occurring strategy is functionalizing the drinks by beneficial bacteria fermentation. The aim of this work is to develop novel plant-based drinks assessing different mixtures of soy and rice milks fermented with single or multi-strain probiotics (Lactobacillus fermentum, L. plantarum, L. helveticus, Bifidobacterium bifidum, and B. longum). The drinks were characterized to study bacterial performances, by means of culture-dependent and -independent techniques, and their volatilome, by means of solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. Through multivariate analysis, these features were investigated and correlated to define accurate descriptors of the produced functional drinks. The results showed that combined drinks and multi-strain fermentation generated higher-value products. For example, combined drinks in comparison with single ones had a lower amount of toxic 2-acetyl-3,5-dimethylfuran and higher abundances of desirable compounds such as 2-butanone, 3-hydroxy and butanoic acid. Multivariate analysis of volatile metabolites and physiological parameters could offer a novel approach to assess the quality of functional plant-based drinks and result in a decisional tool for industrial applications.

A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.

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 produced worldwide. Furthermore, interest of consumers in quality attributes related to coffee production region and varieties is increasing. Thus, it is necessary to encourage the development of simple methodologies to authenticate and guarantee the coffee origin, variety and roasting degree, aiming to prevent fraudulent practices.

RESULTS

C18 high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints obtained after brewing coffees without any sample treatment other than filtration (i.e. considerably reducing sample manipulation) were employed as sample chemical descriptors for subsequent 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 occurred for some sample groups. 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 were3 found to be suitable chemical descriptors for discriminating 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. © 2020 Society of Chemical Industry.

Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.