Multi-element analysis of wines by ICP-MS and ICP-OES and their classification according to geographical origin in Slovenia

Construction of a reaction-based fluorescent sensor for tandem detection of Cu2+ and glutathione in wine

The purpose of this study was to develop a novel reaction-based fluorescent sensor for the detection of Cu2+ and glutathione in real wine samples. The sensor, tris-(2-pyridyl)-methylamine rhodol derivative, was synthesized and validated for the tandem and selective detection of both Cu2+ and glutathione. The sensor exhibited a strong linear correlation between fluorescence intensity and Cu2+ concentration ranging from 100 to 900 nM, while the in situ generated Cu2+ ensemble selectively detected glutathione with a robust linear response from 3 to 30 μM. The detection limits for Cu2+ and glutathione were as low as 28 nM and 0.60 μM, respectively. Additionally, the sensor enabled quantitative detection of Cu2+ and glutathione in real wine samples. This work provides the first reaction-based fluorescence sensor with an "on-off-on" fluorescence response for the tandem detection of Cu2+ and glutathione in wine, offering potential applications in food and beverage quality control.

Inductively Coupled Plasma Mass Spectrometry Performance for the Measurement of Key Serum Minerals: A Comparative Study With Standard Quantification Methods

BACKGROUND

Inductively coupled plasma mass spectrometry (ICP-MS) is widely used for the accurate measurement of minerals. However, its application to serum essential mineral measurement has not been fully evaluated. The present study aimed to assess the performance of ICP-MS for serum minerals by comparing its measurements to those obtained using standard quantification methods.

METHODS

Cross-sectional data were collected from 282 participants from a single facility in Japan. Serum concentrations of eight key minerals, namely sodium, potassium, calcium, phosphorus, magnesium, iron, zinc, and copper, measured via ICP-MS and standard methods were compared using Passing-Bablok regression and Bland-Altman plots.

RESULTS

All minerals, except phosphorus, exhibited good agreement with standard methods, with more stable regression coefficients observed for minerals with greater interindividual variability. After systematically filtering outliers, the mean relative errors were approximately -3% for sodium, potassium, calcium, and magnesium; +5% for iron; 0% for zinc; and -19% for copper. The outliers for iron were primarily due to mild hemolysis, whereas those for zinc were largely attributed to nonhemolysis factors. For phosphorus, the serum total phosphorus concentration measured using ICP-MS was approximately 3.5 times higher than the serum inorganic phosphorus concentration measured using standard methods, with a weak correlation observed between the two methods.

CONCLUSION

This study provides a practical foundation for future research. Understanding ICP-MS characteristics will facilitate the development of new approaches in clinical diagnostics.

Determination of Elemental Content in Vineyard Soil, Leaves, and Grapes of Sangiovese Grapes from the Chianti Region Using ICP-MS for Geographical Identification

To fight counterfeits and to protect the consumer, the interest in certifying the origin of agricultural goods has been steadily growing in the last years. While numerous works focus on the finished product, an aspect often overlooked is the origin of the raw materials and the direct correlation between chemicals in the soil and the plants. With inductively coupled plasma mass spectrometry (ICP-MS) analysis, trace and ultratrace elements in Sangiovese grapes (the main component of Chianti wine) were measured and their levels were used to investigate the geographical origin of the samples. This was achieved despite the extreme closeness of some of the vineyard partners of this study (10-20 km range) by computing a multivariate model using selected elements as levels. The model was then validated on samples coming from different zones of the Chianti area, with good results for discriminating even extremely close regions.

Deep reinforcement learning classification of sparkling wines based on ICP-MS and DOSY NMR spectra

An approach that combines NMR spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) and advanced tensor decomposition algorithms with state-of-the-art deep learning procedures was applied for the classification of Croatian continental sparkling wines by their geographical origin. It has been demonstrated that complex high-dimensional NMR or ICP-MS data cannot be classified by higher-order tensor decomposition alone. Extension of the procedure by deep reinforcement learning resulted in an exquisite neural network predictive model for the classification of sparkling wines according to their geographical origin. A network trained on half of the sample set was able to classify even 94% of all samples. The model can particularly be useful in cases where the number of samples is limited and when simpler statistical methods fail to produce reliable data. The model can further be exploited for the identification and differentiation of sparkling wines including a high potential for authenticity or quality control.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA)

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

A sense of ginger fraud: prevalence and deconstruction of the China-European union supply chain

As an important spice, ginger has been widely distributed in the Chinese and the European Union (EU) markets, the two largest trading areas, in various forms. The ginger supply chain between China and the EU is long and complex, providing opportunities for fraudsters to deceive consumers. However, limited attention has been given to food fraud in ginger, and there is a lack of research on this topic. In this review, ginger was used as an example for interpreting the fraud issues within low-priced and high-trade volume spice products. This review aims to summarize the open access information from food and food fraud databases, literature, and stakeholders about ginger fraud, and to map, deconstruct and analyse the food fraud vulnerability in the supply chain. In addition, potential testing strategies to detect ginger fraud were also discussed. The investigation of food fraud databases, a semi-structured literature review and online interviews with stakeholders revealed that adulteration is the major fraud type in ginger products. And the most vulnerable ginger products are ground ginger and finely processed ginger. The ginger supply chain from China to the EU comprises nine stages and is medium vulnerable to food fraud, both in regard to opportunities and motivational drivers. To ensure the integrity of the ginger supply chain, there is a need to apply fraud vulnerability tools in the companies of the industry. In addition, screening and confirmatory techniques based on the characteristics of ginger should be utilised for monitoring fraud issues in the supply chain.

Application of benchtop total-reflection X-ray fluorescence spectrometry and chemometrics in classification of origin and type of Croatian wines

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A low-power benchtop TXRF system was used for analysis of 70 wine samples.

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The metal content of K, Ca, Fe, Cu, Zn, Mn, Sr, Rb, Ba, Pb, Ni, Cr and V was estimated by chemometric methods.

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Mn, K, Ni, Sr, Rb and Ba were the main variables used to differentiate by wine type and origin.

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LDA showed good detection and prediction abilities with selected elements.

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Classification of origin and type of Croatian wines by chemometric tools.

The contents of selected metals (K, Ca, Fe, Cu, Zn, Mn, Sr, Rb, Ba, Pb, Ni, Cr and V) in 70 wine samples from Continental and Adriatic part of Croatia and different types of wine (red and white) were determined by TXRF. The aim of this study was to compare the elemental composition of wines from two different regions and to determine the discriminant ability of each variable and to indicate which variables discriminate between the four categories considered.

Principal component analysis and cluster analysis showed that K, Mn, Ba and Ni can be considered as the most important characteristics to distinguish between Continental red and white wines, Rb, Ni and Ba for Continental red and Adriatic red wines while Sr is the only metal that completely distinguishes the samples of each category. Finally, linear discriminant analysis showed good recognition (100%) and prediction abilities (96.43%) using these selected elements.

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.

Chemometric approaches for determining the geographical origin of Japanese Chardonnay wines using oxygen stable isotope and multi-element analyses

Determining the geographical origin of wines is a major challenge in wine authentication, but little information is available regarding non-parametric statistical approaches for wines. In this study, we collected 33 domestic Chardonnay wines vinified on a small scale from grapes cultivated in Japan, and 42 Chardonnay wines imported from 8 countries, for oxygen stable isotope and multi-element analyses. Non-metric multidimensional scaling (NMDS), kernel principal component analysis (KPCA) and principal component analysis (PCA) were applied to the oxygen stable isotopic compositions (δ¹⁸O) and the concentrations of 18 elements in the wines to compare the extractions by parametric and non-parametric methods. The non-parametric methods, NMDS and KPCA, separated domestic from imported Chardonnay wines better than the parametric method, PCA. Of 19 variables, 18 were important for geographical discrimination, with the δ¹⁸O value being the most significant in all statistic methods. Non-parametric multivariate analyses will help discriminate domestic from imported Chardonnay wines.

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.

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.

X-ray Fluorescence Techniques for Element Abundance Analysis in Wine

The elemental composition has been extensively used to characterize wine and to find correlations with environmental and winemaking factors. Although X-ray fluorescence (XRF) techniques offer many advantages, they have been rarely used for wine analysis. Here, we show the comparison of wine elemental composition results obtained by total reflection X-ray fluorescence (TXRF) and energy dispersive X-ray fluorescence (EDXRF) for elements K, Ca, Mn, Fe, Cu, Zn, Br, Rb, and Sr. The results obtained by TXRF and EDXRF have been additionally verified by inductively coupled plasma-mass spectrometry. The important analytical features of XRF techniques in wine science have been described, the preservation of volatile elements (e.g., Br) being one of their main advantages. In addition, we have shown that XRF techniques offer an optimal analytical approach for building large data sets containing highly reliable and reproducible results of elemental abundances in wines, corresponding soils, and grape juice. Such data sets are especially important for the geographic authentication of wine. This has been shown for 37 Austrian and Croatian wines collected together with respective soils from selected wine regions. The element abundances in soil reflect in a large portion in grape juice and finished wine suggesting that the contribution of the soil, that is, the plant uptake capacity expressed as c _i(wine)/c _i(soil) concentration factors, can be a highly discriminating factor for wine fingerprinting. This indeed has been proved in the present study in comparison to discrimination based only on wine element abundances. We have identified Fe, Zn, Br, Rb, and Sr as the best discriminator elements for the geographical authentication of wine. The study opens a new perspective in extending the application of XRF techniques as a cost-effective analytical tool for creating large databases of soil, grape juice, and wine element abundances for the evaluation of soil characteristics and other environmental parameters on wine composition.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Geographical Discrimination of Croatian Wines by Stable Isotope Ratios and Multielemental Composition Analysis

The δ¹⁸O and δ¹³C (analyzed by isotope ratio mass spectrometry, IRMS) and concentration of 22 selected elements (analyzed by inductively coupled plasma—optical emission spectrometry, ICP-OES) in 190 Croatian microvinified and commercial wine samples from continental and coastal winegrowing areas and from three viticultural zones (B, CI, and CII) were measured to investigate whether multivariate statistical methods could provide the fingerprint for geographical origin determination. The highest power for discrimination of wines produced in Croatian winegrowing areas was achieved by general discriminant analysis (GDA) showing correct classification of 97.9% of all investigated samples, 100.0% of microvinified samples and 84.8% of commercial samples in the cross-validation matrix. The most significant markers for discrimination of coastal and continental areas found by GDA were δ¹⁸O and Co, followed by K, Rb, Sn, Li, and δ¹³C in descending order. GDA showed higher levels of correctly classified samples from three viticultural zones in Croatia if only microvinified samples were employed in the analysis (94.9%) than for all samples together (86.3%) or for commercial samples (66.1%) in the cross-validation matrix. The discrimination of viticultural zones B, CI, and CII in Croatia was achieved by δ¹⁸O, Co, Rb, Li, K, and Sn. The results obtained showed that the relationships between the isotopic ratios and concentrations of different considered elements combined with appropriate statistical model represent a powerful tool in discrimination of wines produced in different Croatian winegrowing areas.

Voltammetric Determination of Lead and Copper in Wine by Modified Glassy Carbon Electrode

This paper describes the determination of Pb and Cu with a Nafion-modified glassy carbon electrode and MnCo₂O₄ nanoparticles as working electrode for anodic stripping voltammetry. Pb and Cu were accumulated in HCl/KCl (0.1 mol dm^-3) at a potential of -1.4 V (vs. Ag/AgCl electrode) for 480 s, followed by a linear sweep anodic stripping voltammetry (ASV) scan from -1.0 to +0.5 V. Under optimum conditions, the calibration curves were linear in the range of 0.01 - 8 and 0.01 - 5 mg dm^-3 for Pb and Cu, respectively. Effect of sample dilution, accumulation time and potential were optimized. A study of interfering substances was performed. A significant increase in current was obtained at the modified electrode in comparison with the bare glassy carbon electrode. The modified electrode was successfully applied for determination of Pb and Cu in wine samples after a simple preparation procedure. Pb and Cu content in wine was used for estimation of the target hazard quotient (THQ) values for minimal and maximal levels of the metals.

Multielement Analysis of Pakchoi (Brassica rapa L. ssp. chinensis) by ICP-MS and Their Classification according to Different Small Geographical Origins

Statistical interpretation of the concentrations of 42 elements, determined using solution-based inductively coupled plasma mass spectrometry (ICP-MS) analysis and multivariate statistical methods, such as principal components analysis (PCA), was used to establish the provenance of pakchoi (Brassica rapa L. ssp. chinensis) from 6 areas in Ha Noi, Vietnam. Although pakchoi is widely cultivated and manufactured, no universal method is used to discriminate the origin of this vegetable. Our study introduced for the first time a method to classify pakchoi in small geographical areas. 42 metallic elements of pakchoi were detected by ICP-MS, which were further analyzed using multivariate statistical analysis to perform clusters based on the geographical locations. Eleven elements, i.e., ²⁸Si; ⁵⁶Fe; ⁵⁹Co; ⁶³Cu; ⁶⁹Ga; ⁷⁵As; ⁸⁵Rb; ⁹³Nb; ¹⁰⁷Ag; ¹¹⁸Sn, and ¹³⁷Ba, were identified as discriminators to distinguish pakchoi from those areas. Results from this study presented a new method to discriminant the geographical origins of pakchoi, which could apply to other types of vegetables on the food market.

Prospect on Rare Earth Elements and Metals Fingerprint for the Geographical Discrimination of Commercial Spanish Wines

This paper presents a novel tool for Spanish commercial wine discrimination according to their designation of origin (PDO). A total of 65 commercial wines from different Spanish designation of origin (Alicante, Bullas, Campo de Borja, Jumilla, Castilla la Mancha, Ribeiro, Ribera de Duero, Rioja, Rueda, Utiel-Requena, Valdepeñas and Valencia) were characterized. The rare earth elements (REEs) content was determined by a high-temperature torch integrated sample introduction system (hTISIS) coupled to inductively coupled plasma mass spectrometry (ICP-MS). The REE content was used to draw characteristic PDOs radar charts. Results indicated that the REEs fingerprint provides a good prospect to discriminate the different Spanish PDOs, except for Alicante, Castilla la Mancha, Jumilla, Utiel-Requena and Valdepeñas. Finally, for those PDOs that were not properly distinguished, a second fingerprint obtained from Ba, Co, Cr, Mn, Ni, Pb and V content was used for discrimination purposes.

Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles

Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.

Geographical Origin Classification of Chinese Wines Based on Carbon and Oxygen Stable Isotopes and Elemental Profiles

ABSTRACT

Wines from different regions have different qualities due to the impact of geographical location and climate. The sale of inferior wines seriously violates the fair-trade rights of consumers. This article provides an elemental analysis classification method for verifying the geographical origin of wines in the People's Republic of China. Inductively coupled plasma mass spectrometry, liquid chromatography isotope ratio mass spectrometry, and an isotope ratio mass spectrometer were used to analyze 142 wine samples collected from Helan Mountain, Xinjiang, Yunchuanzang, the Yanhuai Valley, and the Hexi Corridor regions. The data included elemental profiles, carbon isotope ratios (δ13C), and oxygen isotope ratios (δ18O). The results of multivariate analysis revealed that the geographical origin of wine is closely related to variations in elemental profiles and isotope ratios. Introducing δ18O and the elements Li, Mn, Ag, In, Th, Ta, and Re into the discriminant model yielded correct classification rates of the linear discriminant model of 90.8% for the training set and 87.3% for the test set.

HIGHLIGHTS

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.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

The Use of Macro, Micro, and Trace Elemental Profiles to Differentiate Commercial Single Vineyard Pinot noir Wines at a Sub-Regional Level

The compositional authentication of wine is of great interest, as the geographic origin of the grapes is often associated with quality, uniqueness, and authenticity. Previous elemental fingerprinting studies mainly discriminated wines from different countries or regions within a country. Here, we report the use of element profiles to distinguish commercial Pinot noir wines from five sub-regional appellations or neighborhoods within one American viticultural area (AVA). Fifty-three single cultivar wines were collected over two harvests and analyzed using microwave plasma-atomic emission spectroscopy (MP-AES) and inductively coupled plasma-mass spectrometry (ICP-MS). Of 62 monitored elements that were quantified with fully validated methods, 24 and 32 elements differed significantly across the neighborhoods and vintages, respectively (p < 0.05). Targeted canonical variate analysis (CVA) explained 85–90% of the variance ratio across the two vintages, indicating persistent and stable elemental fingerprints of wines at a sub-regional level. A sixth, newly founded neighborhood was correctly grouped separately from the others using a Soft Independent Modeling of Class Analogy (SIMCA), indicating the potential of elemental fingerprints for wine authenticity.

Effects of metals released in strong-flavor baijiu on the evolution of aroma compounds during storage

Storage is essential in improving the quality of strong-flavor baijiu (SFB). Here, we investigated the release behaviors of metals from containers into SFB and their effects on the evolution of aroma compounds during storage. Twenty-six metals were identified in SFB samples. The concentrations of Na, K, Ca, Mg, Al, and Fe obviously increased after storing in pottery jar, whereas those of Fe and Cu greatly increased after storing in stainless-steel vessel. The volatility of most esters, alcohols, ketone, furan, and aldehyde decreased, whereas that of most acids increased after adding the metal ions into fresh SFB. The fluorescence intensity of SFB decreased with increased aging time in pottery jar, whereas the fluorescence intensity of acids was quenched with adding Fe3+ and Cu2+. All these results suggested that some metals released from containers had binding affinities with acids, thereby reducing SFB organoleptic stimulation by forming metal-aroma compound complexes during storage.

Determination of the geographical origin of maize (Zea mays L.) using mineral element fingerprints

BACKGROUND

Maize (Zea mays L.) is a staple cereal crop and feed crop throughout the world. In this article, a mineral element fingerprinting technique was applied to single out suitable element indicators to determine the geographical origin of maize. A total of 90 fresh maize samples were collected in 2107 from Jilin, Gansu, and Shandong provinces in China. The contents of 25 mineral elements in all maize samples were measured by inductively coupled plasma mass spectrometry (ICP-MS). The composition of mineral elements was analyzed by multivariate statistical analysis, including one-way analysis of variance (one-way ANOVA), principal component analysis (PCA), k-nearest neighbor (KNN) analysis, and stepwise linear discriminant analysis (SLDA).

RESULTS

As compared by one-way ANOVA, the contents of 19 mineral elements in maize samples were significantly different among three provinces. Principal component analysis based on these 19 elements could obtain preliminary visual classification groups of maize samples. K-nearest neighbor analysis produced a total correct classification rate of 83.9% on the training set, and 82.2% on the prediction set. The SLDA model, based on eight indicative elements (Na, Cr, Rb, Sr, Mo, Cs, Ba, and Pb) obtained a total correct classification rate of 92.2% with cross-validation.

CONCLUSION

The mineral element fingerprinting technique combined with multivariate statistical analysis could be a helpful method to identify the geographical origin of maize. © 2019 Society of Chemical Industry.

Geographical identification of Chianti red wine based on ICP-MS element composition

Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this study, Chianti/Chianti Classico, authentic wines from vineyard of Toscana region (Italy), together samples from 18 different geographical regions, were analyzed with the objective of differentiate them from other Italian wines. Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to discriminate wine geographical origin. Rare Earth Elements (REE), major and trace elements all contributed to the discrimination of Chianti samples. General model was not suited to distinguish PDO red wines from samples, with similar chemical fingerprints, collected in some regions. Specific classification models enhanced the capability of discrimination, emphasizing the discriminant role of some elements.

Authentication of Tokaj Wine (Hungaricum) with the Electronic Tongue and Near Infrared Spectroscopy

Tokaj wines (Hungaricum) are botrytized wines acknowledged for the unique organoleptic properties bestowed by botrytized grape berries during production. Excluding these berries during wine production or manipulating the sugar content of low-grade wines to imitate high-grade wines are some recent suspicious activities that threaten the wine quality. Advanced methods such as spectroscopy and sensor-based devices have been lauded for rapid, reliable, and cost-effective analysis, but there has been no report of their application to monitor grape must concentrate adulteration in botrytized wines. The study aimed to develop models to rapidly discriminate lower grade Tokaj wines, "Forditas I" and "Forditas II," that were artificially adulterated with grape must concentrate to match the sugar content of high-grade Tokaj wines using an electronic tongue (e-tongue) and two near infrared spectrometers (NIRS). Data were evaluated with the following chemometrics: principal component analysis (PCA), discriminant analysis (LDA), partial least square regression (PLSR), and aquaphotomics (a novel approach). There was a noticeable pattern of separation in PCA for all three instruments and 100% classification of adulterated and nonadulterated wines in LDA using the e-tongue. Aquagrams from the aquaphotomics approach showed important water absorption bands capable of being markers of Tokaj wine quality. PLSR models showed coefficient of determination (R² CV) of 0.98 (e-tongue), 0.97 (benchtop NIRS), 0.87 (handheld NIRS), and low root mean squared errors of cross-validation. All three instruments could discriminate, classify, and predict grape must concentrate adulteration in Tokaj with a high accuracy and low error. The methods can be applied for routine quality checks of botrytized wines. PRACTICAL APPLICATION: Tokaj wines (Hungaricum) are botrytized wines acknowledged for the unique organoleptic properties bestowed by botrytized grape berries during production. Excluding these berries during wine production or manipulating the sugar content of low-grade wines to imitate high-grade wines are some recent suspicious activities that threaten the wine quality. Using advanced instruments, the electronic tongue, benchtop near infrared spectroscopy, and a handheld near infrared spectroscopy, we could discriminate, classify, and predict grape must concentrate adulteration in Tokaj with a high accuracy and low error. The models in our study can be applied for routine quality checks of botrytized wines.

Geographical origin of Chinese apples based on multiple element analysis

BACKGROUND

Apples from different regions of China show different qualities and internal characteristics, and appeal to different customers. However, these aspects have not been studied in depth. We characterized the profiles of 14 elements in 317 apple samples collected from five regions of China. Principal component analysis (PCA), linear discriminant analysis (LDA), and back-propagation artificial neural networks analysis (BP-ANN) were used to build models for apple authentication.

RESULTS

Fourteen elements were successfully identified in apple samples by performing graphite furnace atomic absorption spectrometry (GFAAS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) analyses. Comparative analysis showed significantly different element profiles in samples from different regions. The first five principal components obtained by PCA accounted for 71.8% of the total variance. The LDA obtained 70.0% classification rates. The BP-ANN obtained 82.7% classification rates.

CONCLUSION

This study indicated the possibility that apples could be authenticated based on differences in their element profiles, and provided a basis for further geographical origin studies. © 2019 Society of Chemical Industry.

Dietary intake assessment of macro, trace, and toxic elements via consumption of kimchi in South Korea

BACKGROUND

Kimchi, a Korean food, is now produced and consumed in many other countries. In this study, the macro, trace, and toxic element content in varieties of kimchi from Korea and China was evaluated. A health-risk assessment, involving the calculation of estimated dietary intake values, was undertaken. To discriminate among samples with different origins, linear discriminant analysis (LDA) and principal component analysis (PCA) were applied.

RESULTS

The analytical methods used for analyses were successfully validated. In the quantification of elements, no significant differences were detected in the concentrations of the elements that were analyzed. Based on the fresh weight of kimchi, among the macro elements, the concentrations of Na and K were relatively high, with values of 4181.9-9919.8 mg kg^-1 and 1661.8-6623.5 mg kg^-1 , respectively. Among the trace elements that were analyzed, zinc in Korean samples, and strontium in Chinese samples showed the highest concentrations, at 2.67 and 2.98 mg kg^-1 , respectively. Concentration levels of the toxic trace elements were within permissible limits. The amount of daily intake and provisional daily intake of each element calculated based on kimchi intake confirmed its safety for consumption. Linear discriminant analysis and PCA successfully discriminated among the samples of Chinese and Korean origin.

CONCLUSION

The provisional daily intake of the toxic elements was within provisional tolerable daily intake values. The normal intake of kimchi could not pose a threat upon consumption. © 2019 Society of Chemical Industry.

Geographical origin of Vitis vinifera cv. Cannonau established by the index of bioaccumulation and translocation coefficients

Geochemical fingerprints in grape require an identification of major and trace elements that show correlations between concentrations in soil and in plant tissues: these correlations are relevant to discriminate grapes according to geographical origin. The Vitis vinifera cultivar Cannonau is used to produce the renowned Italian controlled designation of origin (DOC) wine "Cannonau" from Sardinia. Two Cannonau vineyards located in Sardinia Region were studied to establish the relationship between geochemical features of vineyard soil and chemical composition of leaves and grape berries. Major and trace elements were determined by X-ray fluorescence and inductively coupled plasma-mass spectrometry in soil, leaf, and grape berry samples. The index of bioaccumulation and the translocation coefficients were also calculated for all elements. Data from the two study areas were compared by a non-parametric test and multivariate statistics (principal component analysis). The results showed a specific assimilation of these elements in leaf and grape berry from vine in two different soils. Moreover, geochemical characterization and statistical analysis enabled to discriminate the cultivar "Cannonau" according to geographical origin. The results showed that the elements that could establish a reliable correlation between the soil vineyard and leaves and grape berries from the two study areas were B, Sr, and Zr. These elements may therefore be used as geochemical fingerprints to identify the geographic origin of V. vinifera cv. Cannonau in the two study areas.

Prediction Models to Control Aging Time in Red Wine

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

Quantification of 71 detected elements from Li to U for aqueous samples by simultaneous-inductively coupled plasma-mass spectrometry

Quantitative analysis of multi-element concentrations in aqueous solutions, such as water, beverages and biofluids, has long been performed by sequential inductively coupled plasma-mass spectrometry. Recently, a fully simultaneous mass spectrum monitoring ICP-MS instrument that fits a compact Mattauch-Herzog geometry (MH-ICP-MS) with a permanent magnet and a large, spatially resolving semiconductor ion detector has been introduced. This technology allows coverage of the complete inorganic relevant mass range from 6Li to 238U in a single measurement, which helps to mitigate the restriction on the number of inorganic elements whose concentrations may be routinely measured from one sample, thus reducing operational assay times and aqueous sample volumes for evaluations across the breadth of the periodic table. We report here on a detailed method for utilizing MH-ICP-MS to detect all elements of the relevant inorganic spectrum in aqueous samples; 7 types of water, 4 types of beverage, and 4 biofluid biological samples. With this method 71 elements can be routinely detected simultaneously in seconds and in as little as 1-4 mL sample, when using a specific set of calibration and internal standards. Quantitative results reveal distinct element patterns between each sample and within types of samples, suggesting that different types of aqueous solutions can be recognized and distinguished by their elemental patterns. The method has implications for understanding elemental distribution and concentration for many fields, including nutrition, studies of the biosphere, ecological stoichiometry, and environmental health fields, among others, where broad elemental information is actually required.

Potential use of surface-assisted LIBS for determination of strontium in wines

Laser-induced breakdown spectroscopy (LIBS) is a well-established technique for elemental analysis and has been widely used for qualitative and quantitative analysis of different solid samples. LIBS is also well-known for not requiring sample preparation, but the analysis of liquids is actually a great challenge. In the present work, a novel approach of elemental analysis of liquids with an organic matrix has been performed, to the best of our knowledge, making a liquid-to-solid matrix conversion by drying wine samples on aluminum and silicon wafers, which have demonstrated an increase in the analytical performance of LIBS. A red wine from Slovakia (not blended with any other variety or wine from other regions or adulterants) was prepared according to the procedure consisting of drying 2 ml of wine dropped on a solid wafer having a flat surface area of about 25  cm². Surface-assisted LIBS in combination with the conversion of liquid into solid avoids the difficulties and limitations of working with liquid samples by LIBS, improving the limit of detection (LOD), repeatability, and sensitivity. The quantification of strontium (Sr) has been realized by addition of the known concentration of SrCl₂·6H₂O in wine before the drying process. Sr is an important element among those usually used as markers for identification of the soils on which the vines grow. Two ionic (407.771 nm and 421.552 nm) and two neutral (460.733 nm and 481.188 nm) Sr lines were used to plot the calibration curves in order to study the LODs and the matrix effects for the analysis of Sr in the tested wines and for different wafer materials. This direct surface-assisted LIBS measuring method has been successfully applied for the determination of Sr in a red wine sample from Slovakia, and the obtained results with two kinds of substrates (Al and Si) were compared. Finally, a validation sample has been employed to test the accuracy of the established calibration curves.

Elemental profile and oxygen isotope ratio (δ18O) for verifying the geographical origin of Chinese wines

The elemental profile and oxygen isotope ratio (δ¹⁸O) of 188 wine samples collected from the Changji, Mile, and Changli regions in China were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES) and isotope ratio mass spectrometry (IRMS), respectively. By combining the data of δ¹⁸O and the concentration data of 52 elements, the analysis of variance (ANOVA) technique was firstly applied to obtain the important descriptors for the discrimination of the three geographical origins. Ca, Al, Mg, B, Fe, K, Rb, Mn, Na, P, Co, Ga, As, Sr, and δ¹⁸O were identified as the key explanatory factors. In the second step, the key elements were employed as input variables for the subsequent partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) analyses. Then, cross validation and random data splitting (training set: test set = 70:30, %) were performed to avoid the over-fitting problem. The average correct classification rates of the PLS-DA and SVM models for the training set were both 98%, while for the test set, these values were 95%, 97%, respectively. Thus, it was suggested that the combination of oxygen isotope ratio (δ¹⁸O) and elemental profile with multi-step multivariate analysis is a promising approach for the verification of the considered three geographical origins of Chinese wines.