Dataset Information

Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

ABSTRACT: A method has been developed to authenticate aged high-quality wines and to quantify their potential adulterations through multivariate analysis and regression techniques applied to the obtained RGB digital images. Wines of pure Gran Reserva, Crianza, and Joven Rioja as well as synthetic adulterated Gran Reserva samples were studied. Digital images were obtained by a single and inexpensive lab-made device. Each sample was characterized by means of the 256 channels intensities from the RGB-colorgram. Multivariate image analysis revealed differences among the wine classes, and between genuine-aged and adulterated samples. Partial least squares regression was used to develop a model for estimating the adulteration degree of Gran Reserva wines. The model achieved good prediction (RMSEP?=?1.6), appropriate precision (RSD?=?2.5%) and suitable LOD (2.3%) to quantify cost-effective adulterations. The present method, due to simplicity and low cost, could provide an appropriate alternative to the traditional chemical authentication methods.

SUBMITTER: Herrero-Latorre C

PROVIDER: S-EPMC6694846 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

Herrero-Latorre Carlos C Barciela-García Julia J García-Martín Sagrario S Peña-Crecente Rosa M RM

Food chemistry: X 20190705

A method has been developed to authenticate aged high-quality wines and to quantify their potential adulterations through multivariate analysis and regression techniques applied to the obtained RGB digital images. Wines of pure Gran Reserva, Crianza, and Joven Rioja as well as synthetic adulterated Gran Reserva samples were studied. Digital images were obtained by a single and inexpensive lab-made device. Each sample was characterized by means of the 256 channels inte ...[more]

PMID: 31432023

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Project description: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.

Project description:ImportanceLissamine green (LG) staining of the conjunctiva is a key biomarker in evaluating ocular surface disease. The disease currently is assessed using relatively coarse subjective scales. Objective assessment would standardize comparisons over time and between clinicians.ObjectiveTo develop a semiautomated, quantitative system to assess lissamine green staining of the bulbar conjunctiva on digital images.Design, setting, and participantsUsing a standard photography protocol, 35 digital images of the conjunctiva of 11 patients with a diagnosis of dry eye disease based on characteristic signs and symptoms were obtained after topical administration of preservative-free LG, 1%, solution. Images were scored independently by 2 masked ophthalmologists in an academic medical center using the van Bijsterveld and National Eye Institute (NEI) scales. The region of interest was identified by manually marking 7 anatomic landmarks on the images. An objective measure was developed by segmenting the images, forming a vector of key attributes, and then performing a random forest regression. Subjective scores were correlated with the output from a computer algorithm using a cross-validation technique. The ranking of images from least to most staining was compared between the algorithm and the ophthalmologists. The study was conducted from April 26, 2012, through June 2, 2016.Main outcomes and measuresCorrelation and level of agreement among computerized algorithm scores, van Bijsterveld scale clinical scores, and NEI scale clinical scores.ResultsThe scores from the automated algorithm correlated well with the mean scores obtained from the gradings of 2 ophthalmologists for the 35 images using the van Bijsterveld scale (Spearman correlation coefficient, rs = 0.79), and moderately with the NEI scale (rs = 0.61) scores. For qualitative ranking of staining, the correlation between the automated algorithm and the 2 ophthalmologists was rs = 0.78 and rs = 0.83.Conclusions and relevanceThe algorithm performed well when evaluating LG staining of the conjunctiva, as evidenced by good correlation with subjective gradings using 2 different grading scales. Future longitudinal studies are needed to assess the responsiveness of the algorithm to change of conjunctival staining over time.

Dataset Information

Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

Publications

Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

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