scholarly journals Geographical Origin Traceability of Red Wines Based on Chemometric Classification via Organic Acid Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Yong Huang ◽  
Zi-Tao Jiang ◽  
Jin Tan ◽  
Rong Li
Keyword(s):  
Organic Acid ◽  
High Performance ◽  
Principal Component ◽  
Pinot Noir ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Grape Varieties ◽  
Chromatographic Profiles

A preliminary study on the chemometric classification of red wines produced from different grape varieties and geographical origins was performed based on their chromatographic profiles of organic acids. Tartaric, malic, citric, lactic, acetic, and succinic acids in wines were detected via high performance liquid chromatography (HPLC). Employing multivariate statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA), pattern recognition models were built for the classification of the investigated wines regarding the grape varieties and geographical origins. The PCA clearly grouped the wines according to variety, and the LDA further offered 100% classification ability toward geographical identification of the wines and the leave-one-out cross-validated assignments were 100%, 86.7%, and 100% correct for Cabernet Sauvignon, Merlot, and Pinot Noir wines, respectively. The results reveal the potential of using chromatographic profiles of organic acid as the characteristic indices for chemometric classification of red wines.

scholarly journals Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Giuseppina P. Parpinello ◽  
Arianna Ricci ◽  
Panagiotis Arapitsas ◽  
Andrea Curioni ◽  
Luigi Moio ◽  
...  
Keyword(s):  
Principal Component ◽  
Rapid Screening ◽  
Support Vector ◽  
Red Wines ◽  
Grape Variety ◽  
Absorption Bands ◽  
Linear Discriminant ◽  
Mir Spectroscopy ◽  
Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

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

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4166 ◽  
Author(s):  
Elisabeta-Irina Geană ◽  
Corina Teodora Ciucure ◽  
Constantin Apetrei ◽  
Victoria Artem
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Total Variance ◽  
Red Wines ◽  
Linear Discriminant ◽  
Vis Spectroscopy ◽  
Ft Ir ◽  
Wine Authentication ◽  
Analytical Approaches

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

scholarly journals Study of phenolic compounds in red grapes and wines from Murfatlar wine center

2014 ◽  
Vol 25 (1) ◽  
pp. 47-52
Author(s):  
Victoria Artem ◽  
Elisabeta-Irina Geana ◽  
Arina Oana Antoce
Keyword(s):  
Phenolic Compounds ◽  
High Performance ◽  
Red Wine ◽  
Pinot Noir ◽  
Red Wines ◽  
Phenolic Profile ◽  
Quality Control Parameters ◽  
Red Grape ◽  
Grape Varieties

Abstract The latest research revealed that phenolic compounds play an important role in the quality of red wine, particularly on colour and astringency and also are responsible for the sanogenic or multiple benefic effects on human health after a moderate consumption of wine. This paper presents the ripening evolution of routine quality control parameters (sugars, acids, weight of 100 berries) and phenolic compounds (anthocyanins and polyphenolic index) during 2013 year for the most representative red grape varieties (Cabernet Sauvignon, Merlot, Feteasca Neagra, Pinot Noir and Mamaia) authorized to obtain wines with denomination of origin controlled in Murfatlar wine center. Also, the phenolic profile of obtained red wines was evaluated by reversedphase high performance liquid chromatography. The reported results were useful to find the optimum moment for grape harvest ensuring the production of high quality wines.

scholarly journals Discrimination of brazilian red varietal wines according to their sensory descriptors

2011 ◽  
Vol 35 (6) ◽  
pp. 1172-1176 ◽  
Author(s):  
Alberto Miele ◽  
Luiz Antenor Rizzon
Keyword(s):  
Principal Component Analysis ◽  
Total Variation ◽  
Principal Component ◽  
Pinot Noir ◽  
The Other ◽  
Sensory Characteristics ◽  
Cabernet Sauvignon ◽  
Sensory Panel ◽  
Red Grape ◽  
Grape Varieties

The purpose of this paper was to establish the sensory characteristics of wines made from old and newly introduced red grape varieties. To attain this objective, 16 Brazilian red varietal wines were evaluated by a sensory panel of enologists who assessed wines according to their aroma and flavor descriptors. A 90 mm unstructured scale was used to quantify the intensity of 26 descriptors, which were analyzed by means of the Principal Component Analysis (PCA). The PCA showed that three important components represented 74.11% of the total variation. PC 1 discriminated Tempranillo, Marselan and Ruby Cabernet wines, with Tempranillo being characterized by its equilibrium, quality, harmony, persistence and body, as well as by, fruity, spicy and oaky characters. The other two varietals were defined by vegetal, oaky and salty characteristics; PC 2 discriminated Pinot Noir, Sangiovese, Cabernet Sauvignon and Arinarnoa, where Pinot Noir was characterized by its floral flavor; PC 3 discriminated only Malbec, which had weak, floral and fruity characteristics. The other varietal wines did not show important discriminating effects.

scholarly journals Metabolomics profiling of Polygoni Multiflori Radix and Polygoni Multiflori Radix Preparata extracts using UPLC-Q/TOF-MS

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhaoyan Zhang ◽  
Liang Yang ◽  
Xiaoyan Huang ◽  
Yue Gao
Keyword(s):  
High Performance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Alpha Linolenic Acid ◽  
Multivariate Statistical ◽  
Biochemical Indicators ◽  
Flight Mass Spectrometry ◽  
Underlying Mechanisms ◽  
Tof Ms

Abstract Background The side effects caused by Polygoni Multiflori Radix (PMR) and Polygoni Multiflori Radix Praeparata (PMRP) have often appeared globally. There is no research on the changes of endogenous metabolites among PMR- and PMRP-treated rats. The aim of this study was to evaluate the varying metabolomic effects between PMR- and PMRP-treated rats. We tried to discover relevant differences in biomarkers and endogenous metabolic pathways. Methods Hematoxylin and eosin staining and immunohistochemistry staining were performed to find pathological changes. Biochemical indicators were also measured, one-way analysis of variance with Dunnett’s multiple comparison test was used for biochemical indicators comparison among various groups. Metabolomics analysis based on ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-Q/TOF-MS) was performed to find the changes in metabolic biomarkers. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to reveal group clustering trend, evaluate and maximize the discrimination between the two groups. MetaboAnalyst 4.0 was performed to find and confirm the pathways. Results PMR extracts exhibited slight hepatotoxic effects on the liver by increasing aspartate and alanine aminotransferase levels. Twenty-nine metabolites were identified as biomarkers, belonging to five pathways, including alpha-linolenic acid metabolism, taurine and hypotaurine metabolism, glycerophospholipid metabolism, arginine and proline metabolism, and primary bile acid biosynthesis. Conclusion This study provided a comprehensive description of metabolomic changes between PMR- and PMRP-treated rats. The underlying mechanisms require further research.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Discrimination of the Geographical Origin of the Lateral Roots of Aconitum carmichaelii Using the Fingerprint, Multicomponent Quantification, and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4124 ◽  
Author(s):  
Lu-Lin Miao ◽  
Qin-Mei Zhou ◽  
Cheng Peng ◽  
Chun-Wang Meng ◽  
Xiao-Ya Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Geographical Origin ◽  
Lateral Roots ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Similarity Analysis ◽  
Diterpenoid Alkaloids ◽  
Linear Discriminant ◽  
Hplc Fingerprint ◽  
Aconitum Carmichaelii

Fuzi is a well-known traditional Chinese medicine developed from the lateral roots of Aconitum carmichaelii Debx. It is rich in alkaloids that display a wide variety of bioactivities, and it has a strong cardiotoxicity and neurotoxicity. In order to discriminate the geographical origin and evaluate the quality of this medicine, a method based on high-performance liquid chromatography (HPLC) was developed for multicomponent quantification and chemical fingerprint analysis. The measured results of 32 batches of Fuzi from three different regions were evaluated by chemometric analysis, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA). The content of six representative alkaloids of Fuzi (benzoylmesaconine, benzoylhypaconine, benzoylaconine, mesaconitine, hypaconitine, and aconitine) were varied by geographical origin, and the content ratios of the benzoylmesaconine/mesaconitine and diester-type/monoester-type diterpenoid alkaloids may be potential traits for classifying the geographical origin of the medicine. In the HPLC fingerprint similarity analysis, the Fuzi from Jiangyou, Sichuan, was distinguished from the Fuzi from Butuo, Sichuan, and the Fuzi from Yunnan. Based on the HCA and PCA analyses of the content of the six representative alkaloids, all of the batches were classified into two categories, which were closely related to the plants’ geographical origins. The Fuzi samples from Jiangyou were placed into one category, while the Fuzi samples from Butuo and Yunnan were put into another category. The LDA analysis provided an efficient and satisfactory prediction model for differentiating the Fuzi samples from the above-mentioned three geographical origins. Thus, the content of the six representative alkaloids and the fingerprint similarity values were useful markers for differentiating the geographical origin of the Fuzi samples.

scholarly journals Metabolomics-based authentication of wines according to grape variety

2019 ◽  
Vol 37 (No. 4) ◽  
pp. 239-245 ◽  
Author(s):  
Leos Uttl ◽  
Kamila Hurkova ◽  
Vladimir Kocourek ◽  
Jana Pulkrabova ◽  
Monika Tomaniova ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Models ◽  
High Performance ◽  
Geographical Origin ◽  
Principal Component ◽  
White Wine ◽  
Tandem Mass ◽  
Grape Variety ◽  
Varietal Identification

In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.

scholarly journals Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles

Beverages ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 54 ◽  
Author(s):  
Federica Bonello ◽  
Maria Cravero ◽  
Valentina Dell’Oro ◽  
Christos Tsolakis ◽  
Aldo Ciambotti
Keyword(s):  
Statistical Analysis ◽  
Geographical Origin ◽  
Central Italy ◽  
Principal Component ◽  
Climatic Conditions ◽  
Sensory Profile ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Sensory Parameters ◽  
Sensory Profiles

NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines—Verdicchio and Fiano—and red wines—Refosco dal Peduncolo Rosso and Nero d’Avola—produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009–2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions.

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