Authenticity and traceability in beverages

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.

Liquid Chromatographic Approach for the Discrimination and Classification of Cava Samples Based on the Phenolic Composition Using Chemometric Methods

Phenolic profiles obtained by liquid chromatography with UV/vis detection were here exploited to classify cava samples from the protected designation of origin Cava. Wine samples belonging to various classes which differed in grape varieties, blends and fermentation processes were studied based on profiling and fingerprinting approaches. Hence, concentrations of relevant phenolic acids and chromatograms registered at 310 nm were preliminarily examined by Principal Component Analysis (PCA) to extract information on cava classes. It was found that various hydroxybenzoic and hydroxycinnamic acids such as gallic, gentisic, caffeic or caftaric acids were up- or down-expressed depending on the wine varieties. Additionally, Partial Least Squares Discriminant Analysis (PLS-DA) was applied to classify the cava samples according to varietal origins and blends. The classification models were established using well-known wines as the calibration standards. Subsequently, models were applied to assign unknown samples to their corresponding classes. Excellent classification rates were obtained thus proving the potentiality of the proposed approach for characterization and authentication purposes.

Traditional and Artisanal Beverages in Nigeria: Microbial Diversity and Safety Issues

A review of up to 90 articles on the microorganisms associated with important artisanal or traditional beverages in Nigeria was carried out. This resulted in an overview of the prevalent microorganisms associated with soymilk, nono (fermented cow milk), tiger nut milk, yoghurt, kunu, zobo, palm wine and the local beers pito and brukutu. The bacteria genera, namely Bacillus, Escherichia, Lactobacillus, Staphylococcus, and Streptococcus, were detected in all nine beverages. On the contrary, this survey resulted in finding that the genera Saccharomyces, Aspergillus, Candida, and Penicillium were the eukaryotic microorganisms isolated in all beverages. The occurrence of fungal isolates, which can be responsible for producing mycotoxins, is a concern and shows the need for post-production tests. Overall, there is a low prevalence of bacteria associated with hygiene, especially the Escherichia genus in alcoholic beverages such as palm wine, pito and burukutu, which may be due both to a low acidity and high ethanol content. However, the prevalence of hygiene indicator genera was higher in nonalcoholic drinks, probably because of incorrect practices during processing. The magnitude of the production and sales of unregulated local beverages in Nigeria has reached the stage where significant regulation and food safety standards are required to safeguard public health. An opportunity exists to monitor and characterize the microbial flora of the artisanal beverages using molecular methods at all stages of production and storage.

Modern Methods for Assessing the Quality of Bee Honey and Botanical Origin Identification

This paper is a summary of the latest literature on methods for assessing quality of natural bee honey. The publication briefly characterizes methods recommended by the International Honey Commission, published in 2009, as well as newer methods published in the last 10 years. Modern methods of assessing honey quality focus mainly on analyzing markers of individual varieties and classifying them into varieties, using, among others, near infrared spectroscopy techniques (NIR), potentiometric tongue, electronic nose, nuclear magnetic resonance (NMR), zymography, polymerase chain reaction (PCR), DNA metabarcoding, and chemometric techniques including partial least squares (PLS), principal component analysis (PCA) and artificial neural networks (ANN). At the same time, effective techniques for analyzing adulteration, sugar, and water content, hydroxymethylfurfural (HMF), polyphenol content, and diastase activity are being sought. Modern techniques enable the results of honey quality testing to be obtained in a shorter time, using the principles of green chemistry, allowing, at the same time, for high precision and accuracy of determinations. These methods are constantly modified, so that the honey that is on sale is a product of high quality. Prospects for devising methods of honey quality assessment include the development of a fast and accurate alternative to the melissopalynological method as well as quick tests to detect adulteration.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Ready to Use Therapeutical Beverages: Focus on Functional Beverages Containing Probiotics, Prebiotics and Synbiotics

The growing global interest in functional foods containing nutrients capable of adding possible beneficial health effects is rapidly increasing both interest and consumer demand. In particular, functionalized beverages for their potential positive effect on health e.g., decreasing cholesterol level, lowering sugar, high fiber content, ability to enhance the immune system, and help digestion, have recently received special attention. Among the different beverages available on the market, probiotic dairy and non-dairy products have attracted much attention because of their affordable cost and their numerous therapeutic activities. Fermented milk and yogurt are currently worth €46 billion, with 77% of the market reported in Europe, North America, and Asia. Consumption of dairy beverages has some limitations due for example to lactose intolerance and allergy to milk proteins, thereby leading consumers to use non-dairy beverages such as fruit, grains, and vegetable juices to add probiotics to diet as well as driving the manufacturers to food matrices-based beverages containing probiotic cultures. The purpose of this review article is to evaluate the therapeutic performance and properties of dairy and non-dairy beverages in terms of probiotic, prebiotic, and synbiotic activities.

Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

First Science & Wine World Congress

The First Science & Wine World Congress entitled "The Wine of the Future" was held in Porto, Portugal, from May 8th to 10th, 2019. The congress comprised 34 oral communications and 40 poster presentations. These contributions were distributed among six sessions: wine traceability and authenticity, wine chemistry and sensorial wine experience, wine analysis, vine and wine technology, wine and health, and wine waste. The intention of this congress was to bring together the leading wine researchers to discuss the current state of knowledge as well as research needs aiming to design the wine that will be consumed in the future.

Review of Analytical Methods to Detect Adulteration in Coffee

As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the “fingerprinting” concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.

Determination of Phenolic Compounds in Paprika by Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectrometry: Application to Product Designation of Origin Authentication by Chemometrics

An ultrahigh performance liquid chromatography-electrospray-tandem mass spectrometry method was developed for the determination of 36 phenolic compounds in paprika. The proposed method showed good method performance with limits of quantitation between 0.03 and 50 μg/L for 16 compounds and between 50 μg/L and 1 mg/L for 12 compounds. Good linearity (R² > 0.995), run-to-run and day-to-day precisions (%RSD values < 12.3 and < 19.2%, respectively), and trueness (relative errors < 15.0%) were obtained. The proposed method was applied to the analysis of 111 paprika samples from different production regions: Spain (La Vera PDO and Murcia PDO) and Czech Republic, each one including different flavor varieties (sweet, bittersweet, and spicy). Phenolic profiles and concentration levels showed to be good chemical descriptors to achieve paprika classification and authentication according to the production region by principal component analysis and partial least squares regression-discriminant analysis. In addition, perfect classification among flavor varieties for Murcia PDO and Czech Republic samples was also obtained.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics

BACKGROUND

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds.

RESULTS

Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples.

CONCLUSION

The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Shift of Aromatic Profile in Probiotic Hemp Drink Formulations: A Metabolomic Approach

Vegetal drinks as a substitute for milk consumption are raising striking interest in the food industry. Soy and rice drinks are the most successful milk substitutes but are low in protein and fiber contents, are rich in sugars, and their cultivation systems are unsustainable; thus, alternative vegetal sources to resolve these limits must be found. A winning candidate could be hemp seed, which is a powerhouse of nutrients, is sugarless, rich in fiber and proteins, and little land and nutrients demanding. The aim is to develop novel drinks obtained from hemp seeds mixed or not with soy and rice and fermented with probiotics (Lactobacillus fermentum, Lb. plantarum, and Bifidobacterium bifidum). The drinks were characterized for their microbial growth, by means of culture-dependent and -independent techniques, and for their volatilome, by means of solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. The results showed that hemp seed drinks have a specific aroma and its compounds are dependent on the type of formulation and to the probiotic used. For example, in hemp seed drinks, 2-heptanol, 2-methyl, 2,4-decadienal, 2-butanone, 3-hydroxy, 2,3-butanedione, and propanoic acid were fine descriptors of probiotics fermentations. Multivariate analysis of volatile metabolites and their correlation to some physiological parameters and nutritional values offered a novel approach to assess the quality of functional hemp drinks which could result in a decisional tool for industrial applications.

Use of 1H NMR to Detect the Percentage of Pure Fruit Juices in Blends

The consumption of high-nutritional-value juice blends is increasing worldwide and, considering the large market volume, fraud and adulteration represent an ongoing problem. Therefore, advanced anti-fraud tools are needed. This study aims to verify the potential of ¹H NMR combined with partial least squares regression (PLS) to determine the relative percentage of pure fruit juices in commercial blends. Apple, orange, pineapple, and pomegranate juices were selected to set up an experimental plan and then mixed in different proportions according to a central composite design (CCD). NOESY (nuclear Overhauser enhancement spectroscopy) experiments that suppress the water signal were used. Considering the high complexity of the spectra, it was necessary to pretreat and then analyze by chemometric tools the large amount of information contained in the raw data. PLS analysis was performed using venetian-blind internal cross-validation, and the model was established using different chemometric indicators (RMSEC, RMSECV, RMSEP, R²_CAL, R²_CV, R²_PRED). PLS produced the best model, using five factors explaining 94.51 and 88.62% of the total variance in X and Y, respectively. The present work shows the feasibility and advantages of using ¹H NMR spectral data in combination with multivariate analysis to develop and optimize calibration models potentially useful for detecting fruit juice adulteration.

Determination of Nutrient Supplementation by Means of ATR-FTIR Spectroscopy during Wine Fermentation

Nitrogen is a limiting factor for the development of wine alcoholic fermentation. The addition of nutrients and different nitrogen sources is a usual practice for many winemakers. Currently, there is a market trend toward wine that is additive-free and there are also restrictions on the amount of ammonium fermentation agents that can be added to the wine. In this work, the changes produced on the alcoholic fermentation by the addition of different nitrogen sources were evaluated by the use of ATR-FTIR. The results showed the feasibility of this technique to observe differences in the growth yeast capacity depending on the type of the nutrients added. A high influence on the development of the alcoholic fermentation was observed, especially at its exponential and the stationary phases. Moreover, the changes observed in the recorded spectra were related to the proteins and lipid esters composition of the yeast cell wall. This technique should be a useful tool to evaluate nitrogen deficiencies during winemaking although further studies should be done in order to evaluate more influential factors.

Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm⁻¹ and 4555-4353 cm⁻¹. Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through “external test set (n = 157)” validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China).

Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control

Lignocellulose feedstock (LCF) provides a sustainable source of components to produce bioenergy, biofuel, and novel biomaterials. Besides hard and soft wood, so-called low-input plants such as Miscanthus are interesting crops to be investigated as potential feedstock for the second generation biorefinery. The status quo regarding the availability and composition of different plants, including grasses and fast-growing trees (i.e., Miscanthus, Paulownia), is reviewed here. The second focus of this review is the potential of multivariate data processing to be used for biomass analysis and quality control. Experimental data obtained by spectroscopic methods, such as nuclear magnetic resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), can be processed using computational techniques to characterize the 3D structure and energetic properties of the feedstock building blocks, including complex linkages. Here, we provide a brief summary of recently reported experimental data for structural analysis of LCF biomasses, and give our perspectives on the role of chemometrics in understanding and elucidating on LCF composition and lignin 3D structure.

Developing anti-counterfeiting measures: The role of smart packaging

Counterfeiting of food and beverage products is rife and premium brands are often targeted by fraudsters. Such is the case with Scotch whisky, a global, reputable brand revered for its heritage and tradition. Using Scotch whisky as a case study, the aim of this paper is to review existing literature and industry information to determine the market and personal consequences of counterfeiting activities and consider the packaging related anti-counterfeiting measures that can be employed within a wider anti-counterfeiting strategy. A typology of counterfeiting activities is developed including: tear-down counterfeiting, product overruns, malicious activities and document counterfeiting. Anti-counterfeiting measures are used to deter, detect and control counterfeiting activities and different packaging related approaches include the use of smart covert and overt technology. Most smart packaging-related anti-counterfeit technologies are stand-alone systems and this presents a vulnerability. An integrated anti-counterfeiting measures strategy, employed by business, the supply chain and the government is required to reduce the risk of the sale of counterfeit food and beverage products.