Development of an HPLC-DAD Method Combined with Chemometrics for Differentiating Geographical Origins of Chinese Red Wines on the Basis of Phenolic Compounds

Phytochemical Profiling and Biological Activity of the Methanolic Extracts of Cirsium Monspessulanum (L.) Hill

The study of new plant species and the identification of their chemical composition may contribute to the discovery of a new breakthrough substances for pharmacotherapeutical applications. For the first time, we examined antioxidant and antimicrobial activity of 70 % v/v methanolic extracts from inflorescences and roots of Cirsium monspessulanum (L.) Hill. obtained by the ASE method. In the (2,2-diphenyl-1-picrylhydrazyl) DPPH analysis, tested extract of inflorescences showed antioxidant activity with an EC50=0.223±0.0479 mg/mL, and (Cupric Ion Reducting Antioxidant Capacity) CUPRAC test assessed the antiradical activity on 14.95±0.13 mgTE/g and for roots the values were EC50=0.307±0.0554 mg/mL and 11.18±0.49 mgTE/g, respectively. Furthermore, extract from the inflorescences possessed the highest antimicrobial activity against Staphylococcus aureus, Staphylococcus epidermidis and Micrococcus luteus with MIC=1.25 mg/mL for each. HPLC/ESI-QTOF-MS/MS method identified 7 phenolic acids and 14 flavonoids in inflorescences extract and only 7 phenolic acids in roots extract. To the best of our knowledge, this is the first qualitative analysis of Cirsium monspessulanum (L.) Hill. and all substances were described for the first time.

Advances in Chromatographic Analysis of Phenolic Phytochemicals in Foods: Bridging Gaps and Exploring New Horizons

Chromatographic analysis of phenolic phytochemicals in foods has significantly advanced over the past decade (2014-2024), meeting increasing demands for precision and efficiency. This review covers both conventional and advanced chromatographic techniques used for detecting phenolic phytochemicals in foods. Conventional methods like High-Performance Liquid Chromatography, Ultra High-Performance Liquid Chromatography, Thin-Layer Chromatography, and Gas Chromatography are discussed, along with their benefits and limitations. Advanced techniques, including Hydrophilic Interaction Liquid Chromatography, Nano-LC, Multidimensional Liquid Chromatography, and Capillary Electrophoresis, are highlighted for their innovations and improved capabilities. The review addresses challenges in current chromatographic methods, emphasizing the need for standardized and validated procedures according to the Food and Drug Administration, European Cooperation for Accreditation of Laboratories, and The International Organization for Standardization guidelines to ensure reliable and reproducible results. It also considers novel strategies for reducing the environmental impact of chromatographic methods, advocating for sustainable practices in analytical chemistry.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

Comparative HPLC–DAD–ESI-QTOF/MS/MS Analysis of Bioactive Phenolic Compounds Content in the Methanolic Extracts from Flowering Herbs of Monarda Species and Their Free Radical Scavenging and Antimicrobial Activities

Comparative analysis of flavonoids and phenolic acids composition, in plants of six species of Monarda from family Lamiaceae was carried out. The 70% (v/v) methanolic extracts of flowering herbs of Monarda citriodora Cerv. ex Lag., Monarda bradburiana L.C. Beck, Monarda didyma L., Monarda media Willd., Monarda fistulosa L. and Monarda punctata L. were analyzed for their polyphenol composition as well as antioxidant capacity and antimicrobial effect. Liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC–DAD–ESI-QTOF/MS/MS) was used to identify phenolic compounds. The in vitro antioxidant activity was assessed using a DPPH radical scavenging assay, while antimicrobial activity was measured by the broth microdilution method allowing for MIC (minimal inhibitory concentration) determination. The total polyphenol content (TPC) was assayed by the Folin–Ciocalteu method. The results showed the presence of eighteen different components including phenolic acids and flavonoids together with their derivatives. The presence of six constituents (gallic acid, hydroxybenzoic acid glucoside, ferulic acid, p-coumaric acid, luteolin-7-glucoside and apigenin-7-glucoside) was found to be dependent on the species. To differentiate the samples, the antioxidant activity of 70% (v/v) methanolic extracts was studied and expressed as a percent of DPPH radical inhibition and in EC50 values (mg/mL). The latter values were as follows: M. media (EC50 = 0.090 mg/mL), M. didyma (EC50 = 0.114 mg/mL), M. citriodora (EC50 = 0.139 mg/mL), M. bradburiana (EC50 = 0.141 mg/mL), M. punctata (EC50 = 0.150 mg/mL) and M. fistulosa (EC50 = 0.164 mg/mL). Moreover, all extracts indicated bactericidal activity against reference Gram-positive (MIC = 0.07–1.25 mg/mL) and Gram-negative bacteria (MIC = 0.63–10 mg/mL) as well as fungicidal effect towards yeasts (MIC = 1.25–10 mg/mL). Staphylococcus epidermidis and Micrococcus luteus were the most sensitive to them. All extracts showed promising antioxidant properties and noteworthy activity against the reference Gram-positive bacteria. Antimicrobial effect of the extracts against the reference Gram-negative bacteria as well as fungi (yeasts) from Candida spp. was slight. All extracts showed bactericidal and fungicidal effect. The obtained results indicated that the investigated extracts from Monarda spp. could be potential sources of natural antioxidants and antimicrobial agents, especially with activity towards Gram-positive bacteria. The differences in the composition and properties of the studied samples may influence the pharmacological effects of the studied species.

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (belongs to ML) was designed to predict Pinot noir wines' provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (within ML) could predict Pinot noir wines' vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

Novel Application of NIR Spectroscopy for Non-Destructive Determination of 'Maraština' Wine Parameters

This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., 'Maraština', harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R² of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.

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

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