{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T10:14:30Z","timestamp":1788430470864,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Economically motivated adulteration of powdered ginger with low-cost cereal flours represents an increasing concern for food authenticity and quality control. The aim of this study was to develop and validate a rapid, reliable, and non-destructive method for the quantitative determination of powdered ginger adulteration using visible and near-infrared (Vis\u2013NIR) spectroscopy coupled with chemometric modelling. Ginger powder samples were adulterated with wheat, corn, and rice flours at concentrations ranging from 5 to 50% (w\/w), with particular emphasis on the low-to-medium adulteration interval (10\u201325%), where reliable quantification is especially relevant for food fraud detection. Spectral data acquired in the visible (400\u2013700 nm), near-infrared (700\u20132500 nm), and combined Vis\u2013NIR (400\u20132500 nm) regions were preprocessed using Savitzky\u2013Golay filtering and evaluated using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). In addition, Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF) were compared for sample classification. Among the evaluated approaches, LDA achieved the highest classification accuracy (&gt;95%) using the NIR spectroscopic region, while PLSR models developed from the NIR spectral region provided the best quantitative performance, with validation coefficients of determination above 0.99, prediction errors below 1%, and RPD values greater than 13. The results demonstrate that Vis\u2013NIR spectroscopy combined with chemometric modelling enables accurate discrimination between authentic and adulterated samples, as well as reliable quantification of flour adulteration in powdered ginger without sample preparation or chemical reagents. The proposed methodology constitutes a rapid, environmentally friendly, and cost-effective analytical strategy with strong potential for routine quality control and food fraud prevention.<\/jats:p>","DOI":"10.3390\/molecules31173091","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T09:20:43Z","timestamp":1788427243000},"page":"3091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliable Quantification of Powdered Ginger Adulteration by Vis\u2013NIR Spectroscopy and Chemometrics"],"prefix":"10.3390","volume":"31","author":[{"given":"Rim","family":"Amine","sequence":"first","affiliation":[{"name":"Department of Chemistry, Faculty of Sciences, University Abdelmalek Essaadi, B.P. 2121, Mhannech II, Tetouan 93002, Morocco"},{"name":"Department of Analytical Chemistry, Faculty of Sciences, University of C\u00e1diz, Agri-Food Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo F.","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Chemical Metrology, National Institute of Industrial Technology (INTI), Buenos Aires B1650WAB, Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hala","family":"Kharkhour","sequence":"additional","affiliation":[{"name":"Stochastic and Deterministic Modeling Laboratory, Department of Mathematics, Faculty of Sciences Oujda, Mohammed First University, Oujda 60000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anas","family":"El-Laghdach","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Faculty of Sciences, University Abdelmalek Essaadi, B.P. 2121, Mhannech II, Tetouan 93002, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8509-4226","authenticated-orcid":false,"given":"Miguel","family":"Palma","sequence":"additional","affiliation":[{"name":"Department of Analytical Chemistry, Faculty of Sciences, University of C\u00e1diz, Agri-Food Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latifa","family":"Azaroual","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Faculty of Sciences, University Abdelmalek Essaadi, B.P. 2121, Mhannech II, Tetouan 93002, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","first-page":"30","article-title":"Understanding and combating food fraud","volume":"67","author":"Spink","year":"2013","journal-title":"Food Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"R157","DOI":"10.1111\/j.1750-3841.2011.02417.x","article-title":"Defining the Public Health Threat of Food Fraud","volume":"76","author":"Spink","year":"2011","journal-title":"J. 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