Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

Enriched Flavonoid Compounds Confer Enhanced Resistance to Fusarium-Induced Root Rot in Oil Tea Plants

Root rot in Camellia oleifera complicates the development of targeted control measures owing to its complex aetiology. Although breeding resistant varieties of C. oleifera presents a promising solution, research into cultivation strategies and potential resistance mechanisms against root rot remains limited. In this study, we investigated six cultivars of C. oleifera that exhibit varying levels of resistance to root rot. We conducted transcriptome analysis, measurements of soil physicochemical properties and an analysis of the fungal microbiome to explore the relationship between Fusarium-induced root rot and flavonoid compounds in the rhizosphere. The resistant cultivar CL18 demonstrated superior performance concerning root rot incidence, root health status and the expression levels of genes associated with flavonoid biosynthesis in this study. Significant differences were observed in the composition and diversity of rhizosphere fungal communities among the various cultivars of C. oleifera. The abundance of Fusarium in the rhizosphere soil of CL18 was low, and a negative correlation was identified between the flavonoid content in the soil and the abundance of Fusarium. Our study uncovers the role of flavonoids in the resistance of C. oleifera to root rot, thereby offering new strategies for disease management and the breeding of resistant cultivars.

What Can Fluorescence Tell Us About Wine?

Rapid and cost-effective measurements of the autofluorescence of wine can provide valuable information on the brand, origin, age, and composition of wine and may be helpful for the authentication of wine and detection of forgery. The list of fluorescent components of wines includes flavonoids, phenolic acids, stilbenes, some vitamins, aromatic amino acids, NADH, and Maillard reaction products. Distinguishing between various fluorophores is not simple, and chemometrics are usually employed to analyze the fluorescence spectra of wines. Front-face fluorescence is especially useful in the analysis of wine, obviating the need for sample dilution. Front-face measurements are possible using most plate readers, so they are commonly available. Additionally, the use of fluorescent probes allows for the detection and quantification of specific wine components, such as resveratrol, oxygen, total iron, copper, hydrogen sulfite, and haze-forming proteins. Fluorescence measurements can thus be useful for at least a preliminary rapid evaluation of wine properties.

Use of energy dispersive X-ray fluorescence to authenticate European wines with protected designation of origin. Challenges of a successful control system based on modelling

Consumers are willing to pay a higher price for food with geographical origin labels such as Protected Designation of Origin and Protected Geographical Indication. In this work, the elemental profile of wine obtained by XRF, combined with multivariate analyses, is used to authenticate 111 Croatian, Italian and Spanish red and white wines, 102 of them from 20 Protected Designations of Origin, reproducing the circumstances faced by control laboratories, using commercially available wines without traceability records. Wines that shared origin clustered together and separated from those of other regions following multivariate statistical tests. Classifications made using Soft Independent Modelling by Class Analogy were characterised by poor sensitivity and specificity. An alternative approach based on successive Partial Least Square Discriminant Analyses with consecutive classifications at country, region and finally, Protected Designation of Origin level, was developed and implemented with good accuracy results. In total, 88 % of the samples were correctly classified.

Recent advances of machine learning in the geographical origin traceability of food and agro-products: A review

The geographical origin traceability of food and agro-products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting-edge solutions to erstwhile intractable issues to identify the origin of food and agro-products. By utilizing advanced algorithms, ML can extract feature information of food and agro-products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry. This paper provides a comprehensive overview of the state-of-the-art applications of ML in the geographical origin traceability of food and agro-products. First, commonly used ML methods are summarized. The paper then outlines the whole process of preparation for modeling, model training as well as model evaluation for building traceability models-based ML. Finally, recent applications of ML combined with different traceability techniques in the field of food and agro-products are revisited. Although ML has made many achievements in solving the geographical origin traceability problem of food and agro-products, it still has great development potential. For example, the application of ML is yet insufficient in the geographical origin traceability using DNA or computer vision techniques. The ability of ML to predict the geographical origin of food and agro-products can be further improved, for example, by increasing model interpretability, incorporating data fusion strategies, and others.

Geographical characterization of wines from seven regions of China by chemical composition combined with chemometrics: Quality characteristics of Chinese 'Marselan' wines

This study investigated the basic and functional compositions, volatile compounds, intelligent sensory characteristics and antioxidant capacity of the commercial 'Marselan' wines from seven Chinese regions. The Nei Mongol wines featured high total reducing sugar, fructose, ammonia nitrogen, 17 monomeric phenolic acids contents and elevated antioxidant capacity. Malic acid was the only organic acid that significantly different in all seven regions. Malvidin-3-O-glucoside and trans-peonidin-3-O-(6-O-p-coumaryl)-glucoside showed the highest and lowest contents. A total of 102 volatiles was detected and Hebei wines had the most (91). Hexanoic acid and β-damascenone were considered to have high potential sensory effects (OAV ≥ 1) as compounds detected in all regions. Floral, sweet, and fruity were the most important aroma series. E-eye analysis revealed the colors of the samples tended to yellowish with aging. PCA and OPLS-DA based on the basic wine composition, monomeric organic acids and anthocyanins allowed achieving a discrimination of the seven regions, respectively.

Machine Learning Model Stability for Sub-Regional Classification of Barossa Valley Shiraz Wine Using A-TEEM Spectroscopy

With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018-2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending.

A novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

Predicting Bordeaux red wine origins and vintages from raw gas chromatograms

Connecting chemical properties to various wine characteristics is of great interest to the science of olfaction as well as the wine industry. We explored whether Bordeaux wine chemical identities and vintages (harvest year) can be inferred from a common and affordable chemical analysis, namely, a combination of gas chromatography (GC) and electron ionization mass spectrometry. Using 12 vintages (within the 1990-2007 range) from 7 estates of the Bordeaux region, we report that, remarkably, nonlinear dimensionality reduction techniques applied to raw gas chromatograms recover the geography of the Bordeaux region. Using machine learning, we found that we can not only recover the estate perfectly from gas chromatograms, but also the vintage with up to 50% accuracy. Interestingly, we observed that the entire chromatogram is informative with respect to geographic location and age, thus suggesting that the chemical identity of a wine is not defined by just a few molecules but is distributed over a large chemical spectrum. This study demonstrates the remarkable potential of GC analysis to explore fundamental questions about the origin and age of wine.

Absorbance-Transmittance Excitation Emission Matrix Method for Quantification of Major Cannabinoids and Corresponding Acids: A Rapid Alternative to Chromatography for Rapid Chemotype Discrimination of Cannabis sativa Varieties

Background: Phytocannabinoids naturally occur in the cannabis plant (Cannabis sativa), and Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) predominate. There is a need for rapid inexpensive methods to quantify total THC (for statutory definition) and THC-CBD ratio (for classification into three chemotypes). This study explores the capabilities of a spectroscopic technique that combines ultraviolet-visible and fluorescence, absorbance-transmittance excitation emission matrix (A-TEEM). Methods: The A-TEEM technique classifies 49 dry flower extracts into three C. sativa chemotypes, and quantifies the total THC-CBD ratio, using validated gas chromatography (GC)-flame ionization (FID) and High-Performance Liquid Chromatography (HPLC) methods for reference. Multivariate methods used are principal components analysis for a chemotype classification, extreme gradient boost (XGB) discriminant analysis (DA) to classify unknown samples by chemotype, and XGB regression to quantify total THC and CBD content using GC-FID and HPLC data on the same samples. Results: The A-TEEM technique provides robust classification of C. sativa samples, predicting chemotype classification, defined by THC-CBD content, of unknown samples with 100% accuracy. In addition, A-TEEM can quantify total THC and CBD levels relevant to statutory determination, with limit of quantifications (LOQs) of 0.061% (THC) and 0.059% (CBD), and high cross-validation (>0.99) and prediction (>0.99), using a GC-FID method for reference data; and LOQs of 0.026% (THC) and 0.080% (CBD) with high cross-validation (>0.98) and prediction (>0.98), using an HPLC method for reference data. A-TEEM is highly predictive in separately quantifying acid and neutral forms of THC and CBD with HPLC reference data. Conclusions: The A-TEEM technique provides a sensitive method for the qualitative and quantitative characterization of the major cannabinoids in solution, with LOQs comparable with GC-FID and HPLC, and high values of cross-validation and prediction. As a spectroscopic technique, it is rapid, with data acquisition <45 sec per measurement; sample preparation is simple, requiring only solvent extraction. A-TEEM has the sensitivity to resolve and quantify cannabinoids in solution based on their unique spectral characteristics. Discrimination of legal and illegal chemotypes can be rapidly verified using XGB DA, and quantitation of statutory levels of total THC and total CBD comparable with GC-FID and HPLC can be obtained using XBD regression.

Grape Heterogeneity Index: Assessment of Overall Grape Heterogeneity Using an Aggregation of Multiple Indicators

Uniform grape maturity can be sought by producers to minimise underripe and/or overripe proportions of fruit and limit any undesirable effects on wine quality. Considering that grape heterogeneity is a multifaceted phenomenon, a composite index summarising overall grape heterogeneity was developed to benefit vineyard management and harvest date decisions. A grape heterogeneity index (GHI) was constructed by aggregating the sum of absolute residuals multiplied by the range of values from measurements of total soluble solids, pH, fresh weight, total tannins, absorbance at 520 nm (red colour), 3-isobutyl-2-methoxypyrazine, and malic acid. Management of grape heterogeneity was also studied, using Cabernet Sauvignon grapes grown under four viticultural regimes (normal/low crop load, full/deficit irrigation) during the 2019/2020 and 2020/2021 seasons. Comparisons of GHI scores showed grape variability decreased throughout ripening in both vintages, then significantly increased at the harvest time point in 2020, but plateaued on sample dates nearing the harvest date in 2021. Irrigation and crop load had no effect on grape heterogeneity by the time of harvest in both vintages. Larger vine yield, leaf area index, and pruning weight significantly increased GHI score early in ripening, but no significant relationship was found at the time of harvest. Differences in the Ravaz index, normalised difference vegetation index, and soil electrical conductivity did not significantly change the GHI score.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Production regions discrimination of Huangguanyin oolong tea by using the content of chemical components and rare earth elements

Oolong tea is one of the most popular tea beverages in China. Tea cultivars, processing technology and origin of production affect the quality and price of oolong teas. To investigate the differences in Huangguanyin oolong tea from different production regions, the chemical components, mineral elements and rare earth elements of Huangguanyin oolong tea produced in Yunxiao (YX) and Wuyishan (WY) were analyzed by using spectrophotometry methods, targeted metabolomics and inductive plasma coupled mass spectrometry (ICP-MS). The results of spectrophotometry methods revealed that there were significant differences in thearubigin, tea polyphenols and water extract between Huangguanyin oolong teas from different production regions. Targeted metabolomics identified a total of 31 chemical components in Huangguanyin oolong teas from the two production regions, of which 14 chemical components were significantly different and contributed to the regional differentiation of Huangguanyin oolong tea. Yunxiao Huangguanyin had relatively higher contents of (-)-Epigallocatechin-3-O-(3-O-methylgallate) (EGCG3″Me), ornithine (Orn) and histidine (His), while Wuyishan Huangguanyin had relatively higher contents of glutamic acid (Glu), γ-aminobutyric acid (GABA), β-aminobutyric acid (β-ABA) and other components. Moreover, ICP-MS identified a total of 15 mineral elements and 15 rare earth elements in Huangguanyin oolong tea from the two production regions, of which 15 elements were significantly different between YX and WY, and contributed to the regional differentiation of Huangguanyin oolong tea. K had a relatively higher content in Yunxiao Huangguanyin, while rare earth elements had relatively higher contents in Wuyishan Huangguanyin. The classification results by the production region showed that the discrimination rate of the support vector machine (SVM) model based on the 14 different chemical components reached 88.89%, while the SVM model based on the 15 elements reached 100%. Therefore, we used targeted metabolomics and ICP-MS techniques to screen and explore the chemical components, mineral elements and rare earth elements differences among two production regions, which indicated the feasibility of Huangguanyin oolong tea classification by production regions in the study. The results will provide some reference for the distinction between the two production regions of Huangguanyin oolong tea.

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine

Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R² values above 0.7 and fifteen with R² values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product's sensory characteristics from raw material spectral attributes.

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin

Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.

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.

Methylnigakinone content determination and geographical origin discrimination for P. quassioides via fluorescence fingerprint and principal component analyses

Picrasma quassioides is used as a bittersweet stomach medicine. Because it is a natural product obtained from various geographical regions, the production area is important when P. quassioides is used as a crude drug. Herein, we developed a method to determine the content of methylnigakinone, one of the major active ingredients in P. quassioides, and a protocol for discriminating the geographical origin of this natural product using a fluorescence fingerprint analysis and principal component analysis (PCA). Because methylnigakinone is fluorescent (excitation wavelength: 352 nm, emission wavelength: 458 nm), the content of this molecule can be determined in the concentration range of 0.1-1 μg/mL. The quantification results of methylnigakinone obtained using the developed method were similar to those obtained from an HPLC analysis. Furthermore, the PCA of the fluorescence fingerprint of P. quassioides produced a score plot with the three different geographical origins (Kyushu island (Japan), Shikoku island (Japan), and China) plotted in the regions. Thus, it was possible to discriminate the geographical origin of the P. quassioides samples. The developed method is simple, quick, and has a minimal environmental impact. Therefore, the developed method will be useful for confirming the origin of P. quassioides.

Machine-learning assisted modelling of multiple elements for authenticating edible animal blood food

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The critical elements for identifying species of the animal blood food were selected.

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Elemental fingerprint coupled with ELM were proposed for species identification of the animal blood food.

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The optimal ELM model for identifying the species of the animal blood food was constructed.

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The absolute and relative content of 25 elements in animal blood food were reported for the first time.

Elemental fingerprint coupled with machine learning modelling was proposed for species authentication of the edible animal blood gel (EABG). A total of 25 elements were determined by inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS) in 150 EABG samples prepared from five species of animals, namely duck, chicken, bovine, pig, and sheep. Extreme learning machine (ELM) models were constructed and optimized. Principal component analysis and Fisher linear discriminant analysis were comparatively utilized for dimension reduction of the crucial input elements selected via stepwise discriminant analysis and one-way ANOVA. The optimal ELM model was obtained with the crucial elements selected by one-way ANOVA from the relative content of the measured elements, which afforded accuracies of 98.0% and 96.0% for the training and test set, respectively. All findings suggest that elemental fingerprint accompanied by ELM have great potential in authenticating the edible animal blood food.

The effects of drying methods and harvest season on piperine, essential oil composition, and multi-elemental composition of black pepper

This study aimed to evaluate the piperine content, essential oil composition, and multi-elemental composition of black pepper samples according to different drying methods and harvest season. Differences in essential oil composition and B, Ca, K, Mg, and S were noted according to sampling campaign, indicating secondary metabolism plant alterations. Mechanical drying resulted in essential oil composition changes due to high temperature exposure during processing. Increases in Fe and Cr contents when employing mechanical dryers with direct heating were also observed, due to direct contact with metallic structures and particulate material from the burning process. The As and Pb contents of several samples were higher than the maximum permissible limits, reaching 0.46 and 0.56 mg kg^-1, respectively, thus surpassing legislation safety limitations for human consumption.

Determination of Cabernet Sauvignon wine quality parameters in Chile by Absorbance-Transmission and fluorescence Excitation Emission Matrix (A-TEEM) spectroscopy

A-TEEM spectroscopy is presented as a novel rapid quantitative analysis method for 44 individual phenolic and basic wine chemistry compounds. To date no practical and combined analysis method for these recognized quality parameters important to the wine industry exists. The method was implemented in a Lambert-Beer linear concentration range to facilitate traceable absorbance and fluorescence spectral signatures. Both components were comparatively analyzed as single- and combined multi-block variable sets, and regressed against HPLC-DAD, UV-vis spectroscopy and other analytical reference data, using the Extreme Gradient Boost Regression (XGBR) and Partial Least Squares Regression (PLSR) algorithms. The approach was applied on 126 wines, and subsequently validated by a random split of 13% of the set and an additional independent set of 16 wines. XGBR with multi-block data organization systematically yielded the highest prediction accuracy and precision with respective overall valid fits indicated by mean R² and relative bias of 0.94 ± 0.04 and 4.1 ± 1.8%.

An overview on multi-elemental profile integrated with chemometrics for food quality assessment: toward new challenges

Food products, especially those with high value-added, are commonly subjected to strict quality controls, which are of paramount importance, especially for attesting to some peculiar features related, for instance, to their geographical origin and/or the know-how of their producers. However, the sophistication of fraudulent practices requires a continuous update of analytical platforms. Different analytical techniques have become extremely appealing since the instrumental analysis tools evolution has substantially improved the capability to reveal and understand the complexity of food. In light of this, multi-elemental composition has been successful implemented solving a plethora of food authentication and traceability issues. In the last decades, it has existed an ever-increasing trend in analysis based on spectrometry analytical platforms in order to obtain a multi-elemental profile that combined with chemometrics have been noteworthy analytical methodologies able to solve these problems. This review provides an overview of published reports in the last decade (from 2011 to 2021) on food authentication and quality control from their multi-element composition in order to evaluate the state-of-the-art of this field and to identify the main characteristics of applied analytical techniques and chemometric data treatments that have permit achieve accurate discrimination/classification models, highlighting the strengths and the weaknesses of these methodologies.

From Soil to Grape and Wine: Geographical Variations in Elemental Profiles in Different Chinese Regions

Elemental profiles are frequently applied to identify the geographical origin and authenticity of food products, to guarantee quality. The concentrations of fifteen major, minor, and trace elements (Na, Mg, K, Ca, Al, Fe, Mn, Cu, Zn, Rb, Sr, Li, Cd, Cs, and Ba) were determined in soils, “Meili” grapes, and wines from six regions in China by inductively coupled plasma mass spectrometry (ICP-MS). The elemental concentrations in these samples, according to the geographical origins, were analyzed by one-way analysis of variance (ANOVA) with Duncan’s multiple comparisons. The bioconcentration factor (BCF) from soil to grape and the transfer factor (TF) from grape to wine were calculated. Mg, K, Ca, Cu, Zn, Rb, Sr, and Ba presented higher BCF values than the other seven elements. The TF values of six elements (Na, Mg, K, Zn, Li, and Cs) were found to be greater than one. Moreover, the correlation of element content between the pairs of soil–grape, grape–wine, and bioconcentration factor (BCF)–environmental factor were analyzed. Significant correspondences among soil, grape, and wine were observed for K and Li. Two elements (Sr and Li) showed significant correlations between BCF and environmental factor (relative humidity, temperature, and latitude). A linear discriminant analysis (LDA) with three variables (K, Sr, Li) revealed a high accuracy (>90%) to determine the geographical origin for different Chinese regions.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

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.

Study of the Unique Characteristics of Multi-Elements of the Wild Astragali Radix from Shanxi Province by Inductively Coupled Plasma Mass Spectrometry

BACKGROUND

Astragali Radix (AR) is widely used because of its dual use in medicine and food. Wild Astragali Radix from Hunyuan county of Shanxi Province in China is accepted as a geo-authentic medicine with high quality and good medicinal effects. Multi-elements of Astragali Radix partially reflect its efficacy and safety. However, there is no systemic research about the elemental analysis of geo-authentic Astragali Radix until now.

OBJECTIVE

In this paper, multi-elemental profiling of Astragali Radix from Gansu, Jilin, Inner Mongolia, Shaanxi and Shanxi provinces in China was implemented.

METHODS

A microwave digestion coupled with ICP-MS, principle component analysis and partial-least square-discriminate analysis were used for the analysis of unique elemental accumulation ability of Shanxi wild-type.

RESULTS

For 53 stably detected elements, the contents of most elements (Ba, Cs, Ga, La, Pr and so on) were significantly higher while some others (Cd, Cu, P, W and Zn) were significantly lower in wild Astragali Radix from Shanxi than those of the samples from Gansu, Jilin, Inner Mongolia, Shaanxi provinces and the cultivated samples from Shanxi. After binary logistic regression, combinational variable Ba-P was found to be a good marker to identify wild Astragali Radix of Shanxi Province from the samples with other origins, and the total positive prediction probability of the test samples from both market and their original field could reach 93.8% through external validation using the model.

CONCLUSIONS

Multi-elemental analysis coupled with PCA, PLS-DA, nonparametric analysis and binary logistic regression can be a good tool for the identification of wild Astragali Radix from Shanxi Province.

HIGHLIGHTS

An ICP-MS method was developed and validated for multi-elements. Fifty-three elements in Astragali Radix from differential origins were compared. The wild Astragali Radix from Shanxi had unique elemental characteristics. Combinational variable Ba-P is a good marker to identify wild-type from Shanxi.

Phenolic Characterization of Cabernet Sauvignon Wines From Different Geographical Indications of Mendoza, Argentina: Effects of Plant Material and Environment

The chemical and sensory characteristics of the wines are related to the geographical origin of the grape, as a result of the interplay between the plant material (G), its acclimatization to the environment (E) and the human factor that influences both the vineyard and the winery. The range of phenotypes that a single genotype can express depending on its environment is known as phenotypic plasticity and is the result of G × E interaction. The present study evaluated the independent and interactive effects of Cabernet Sauvignon plant materials (G: Clone 7 and Mount Eden) implanted in different geographical indications of Mendoza, Argentina (E: Agrelo, Pampa El Cepillo, Altamira and Gualtallary) according to fruit yield and phenolic profiles of wines. The experiment was carried out during 2018 and 2019 vintages using a multifactorial design. When berries reached 24 °Brix, the clusters were harvested, analyzed and wines elaborated by a standardized procedure. Then, the anthocyanin and non-anthocyanin phenolic profiles of wines were determined by high-performance liquid chromatography with diode array and fluorescence detection (HPLC-DAD–FLD). The results revealed significant G × E interactions for yield traits, including the number of clusters per plant. Differential chemical composition and quality parameters of the resulting wines, markedly affected by E, were observed; that is the geographical location of the vineyards. There were similarities in the phenolic composition between Pampa El Cepillo and Altamira, while larger differences between Agrelo and Gualtallary were observed. Gualtallary presented the highest levels of anthocyanins, quercetin and trans-resveratrol. The increased amount of these compounds in Gualtallary was associated with an increased UV-B exposure of plants at this high altitude environment. This is the first report that characterizes the effects of plant material and environment for Cabernet Sauvignon. These results are of oenological and viticulture interest for the wine industry demonstrating that the selection of the plant material and the vineyard location for Cabernet Sauvignon can considerably affect the quality attributes of wines.

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

The use of blockchain in the luxury industry: supply chains and the traceability of goods

Purpose

Most businesses strive to control the efficiency of their supply chains; however, luxury firms face additional challenges from counterfeit, gray market and copycat products. Blockchain technology can address these issues and enhance firms' supply chain management, guaranteeing the traceability and origin of luxury products. Therefore, this study aims to investigate the utility and contexts influencing the implementation of blockchain technology to optimize supply chain management and prevent fraud in the luxury industry.

Design/methodology/approach

The research uses a qualitative approach based on the grounded theory method. Data are collected by semi-structured interviews with 12 stakeholders working on blockchain applications in the luxury business sector.

Findings

Highlighting the problems faced by luxury brands' supply chains, this study presents blockchain technology as a solution for disintermediation, traceability and transparency in the luxury goods sector. The constraints faced by luxury brands incorporating this technology into their ecosystem include the knowledge gap, the multiplicity of third parties involved in the production process and bias toward short-term returns on investment.

Originality/value

Blockchains promote greater transparency and efficiency within supply chains, which builds consumer trust and improves brand revenue. Considering luxury brands' reluctance to adopt blockchains, this study suggests that luxury firms adopt a staggered implementation of private blockchain networks starting with a small number of third-party suppliers.

Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine

Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.

Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin

An untargeted lipidomic profiling approach based on ultra - performance liquid chromatography - time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) was successfully used to study the origin of commercial Pinot noir wines. The total wine lipids were extracted using a modified Bligh-Dyer method. In all wine samples, the total lipids were less than 0.1% (w/w) of wine. The wines analyzed consisted of 222 lipids from 11 different classes. 48 commercial Pinot noir wine samples were collected from producers in Burgundy, California, Oregon, and New Zealand. Lipidomic data was studied using advanced multivariate analysis methods, random forest, k-nearest neighbor (k-NN), and linear discriminant analysis. The overall classification accuracy was 97.5% for random forest and 90% for k-NN. Wine lipids showed a strong potential for classifying wines by origin, with the top 58 lipids contributing to the discrimination. This information could potentially be used for further study of the impacts of lipids on wine characteristics and authenticity.

Sensory typicity of regional Australian Cabernet Sauvignon wines according to expert evaluations and descriptive analysis

The concept of wine typicity has been an important tool for the international wine trade, and especially for Old World wine producing countries, where provenance criteria are regulated and act as a quality indicator. Provenance in Australia is governed by Geographical Indications, for which typicity should also be evident in terms of regional sensory profiles of wine from a given grape cultivar. Two approaches were used to identify sensory drivers for regional typicity of commercial Cabernet Sauvignon wines from three Australian regions, namely Coonawarra, Margaret River, and Yarra Valley. Cabernet Sauvignon-dominant wines from Bordeaux were also assessed for benchmarking purposes. A set of 84 wines underwent a sorting task and rate-all-that-apply (RATA) analysis of the sorted groups with an expert panel. Agglomerative hierarchical clustering of the sorting task data did not show a clear regional driver upon separating the samples into four main clusters, although certain sensory traits could be associated with the different clusters. On the other hand, canonical variate analysis (CVA) of the group-RATA results indicated several sensory drivers for the separation between the regions, such as 'mint' and 'dark fruits' being important for Coonawarra wine profiles, 'floral' and 'green pepper' for Margaret River, 'stemmy' for Yarra Valley, and 'barnyard' and 'savoury' differentiating Bordeaux wines from the other regions. A subset (n = 52) of wines was selected for further evaluation by descriptive analysis with a trained panel. Statistical evaluation with CVA revealed similar results to the expert evaluation, with Bordeaux wines showing more dissimilarity when compared to Australian regions, and having 'savoury' and 'earthy' as significant characters. The results also demonstrated that 'mint' and 'Mallee leaf' were relevant characters for Coonawarra regional sensory profile, 'violets' and 'red fruits' for Margaret River, and 'cooked vegetables' for Yarra Valley. Analysing both data sets (expert RATA and DA) revealed some agreement between the sets of results for attributes such as 'mint', 'cooked vegetables', 'floral', green-related characters, and oak characters like 'vanilla' and 'chocolate'. Overall, experts and trained panellists were able to distinguish regions based on a few characteristic sensory traits.