From Pixels to Spectra: Predicting Wine Colorimetric Characteristics Through Machine Learning Models

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dc.authorid0000-0003-0028-7286
dc.contributor.authorErdemir, Naz
dc.contributor.authorSinop, Celal Deniz
dc.contributor.authorUysal, Reyhan Selin
dc.contributor.authorDalyan, Tugba
dc.date.accessioned2026-04-04T18:55:24Z
dc.date.available2026-04-04T18:55:24Z
dc.date.issued2026
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractThis study employs digital image processing and machine learning techniques to predict all colorimetric characteristics (color intensity, density, tonality, and color index percentages) of colored wines in a novel, cost-effective, and rapid manner. To determine the values of colorimetric characteristics, ultraviolet-visible (UV-Vis) absorbance was measured at three key wavelengths (A420, A520, and A620) using UV-Vis spectrophotometry, corresponding to the yellow, red, and blue color percentages, respectively. Simultaneously, the pictures of 86 wine samples were acquired, and the corresponding RGB and HSV color values were extracted from the images to serve as input features for multiple regression models. The models developed included principal component regression, k-nearest neighbors, linear regression, decision tree, random forest, and partial least squares (PLS). Among the models, random forest outperformed PLS in predicting A620 absorbance value due to its ability to capture non-linear patterns, whereas PLS demonstrated greater accuracy (R2 > 0.95) in predicting the A420 and A520 absorbance values. According to feature selection, hue and saturation had the biggest impact on prediction accuracy. By determining absorbance values using the developed models, the complete colorimetric characteristics of the wine samples can be calculated, enabling the evaluation of their physicochemical parameters during the fermentation process or post-fermentation. As a result, all the models, improved, offer a promising alternative for quick, easy, and scalable prediction methods by reducing measurement time, eliminating the need for laboratory instruments, and introducing a new methodology to complement conventional spectroscopic techniques, with potential applications in consumer-level analysis and the process of wine quality control.
dc.identifier.doi10.1007/s12161-025-02970-0
dc.identifier.doi10.1007/s12161-025-02970-0
dc.identifier.issn1936-9751
dc.identifier.issn1936-976X
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105027262216
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s12161-025-02970-0
dc.identifier.urihttps://hdl.handle.net/11411/10412
dc.identifier.volume19
dc.identifier.wosWOS:001656833100002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofFood Analytical Methods
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectWine Color
dc.subjectColor Intensity
dc.subjectDensity
dc.subjectTonality
dc.subjectColor Index Percentages
dc.subjectArtificial Intelligence
dc.subjectWine Quality
dc.titleFrom Pixels to Spectra: Predicting Wine Colorimetric Characteristics Through Machine Learning Models
dc.typeArticle

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