{"title":"利用非线性拉曼光谱模型快速检测乙醇溶液中的甲醇","authors":"Somaye Vali Zade , Hossein Rastegar , Hamid Abdollahi","doi":"10.1016/j.saa.2025.126993","DOIUrl":null,"url":null,"abstract":"<div><div>Methanol contamination in ethanol-based products poses a significant health risk due to its toxicity at low concentrations. This study developed a rapid, non-destructive method for detecting and quantifying methanol in alcoholic beverages using Raman spectroscopy combined with chemometric models, including Partial Least Squares (PLS), Interval PLS (iPLS), Kernel PLS, and Radial Basis Function Artificial Neural Networks (RBF-ANN). The Kernel PLS model, applied to optimized spectral intervals, achieved the lowest relative prediction error (REP%) of 6.84 % on a test set. Validation on real samples spiked with 3–20 % methanol confirmed the method's ability to quantify methanol with absolute relative errors below 10 % in most cases. Compared to conventional techniques like gas chromatography (GC) and high-performance liquid chromatography (HPLC), this approach requires minimal sample preparation and shorter analysis time, making it suitable for quality control in the food, pharmaceutical, and cosmetic industries.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"347 ","pages":"Article 126993"},"PeriodicalIF":4.6000,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid methanol detection in ethanol solutions via non-linear Raman spectroscopy modeling\",\"authors\":\"Somaye Vali Zade , Hossein Rastegar , Hamid Abdollahi\",\"doi\":\"10.1016/j.saa.2025.126993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Methanol contamination in ethanol-based products poses a significant health risk due to its toxicity at low concentrations. This study developed a rapid, non-destructive method for detecting and quantifying methanol in alcoholic beverages using Raman spectroscopy combined with chemometric models, including Partial Least Squares (PLS), Interval PLS (iPLS), Kernel PLS, and Radial Basis Function Artificial Neural Networks (RBF-ANN). The Kernel PLS model, applied to optimized spectral intervals, achieved the lowest relative prediction error (REP%) of 6.84 % on a test set. Validation on real samples spiked with 3–20 % methanol confirmed the method's ability to quantify methanol with absolute relative errors below 10 % in most cases. Compared to conventional techniques like gas chromatography (GC) and high-performance liquid chromatography (HPLC), this approach requires minimal sample preparation and shorter analysis time, making it suitable for quality control in the food, pharmaceutical, and cosmetic industries.</div></div>\",\"PeriodicalId\":433,\"journal\":{\"name\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"volume\":\"347 \",\"pages\":\"Article 126993\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386142525013009\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386142525013009","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
Rapid methanol detection in ethanol solutions via non-linear Raman spectroscopy modeling
Methanol contamination in ethanol-based products poses a significant health risk due to its toxicity at low concentrations. This study developed a rapid, non-destructive method for detecting and quantifying methanol in alcoholic beverages using Raman spectroscopy combined with chemometric models, including Partial Least Squares (PLS), Interval PLS (iPLS), Kernel PLS, and Radial Basis Function Artificial Neural Networks (RBF-ANN). The Kernel PLS model, applied to optimized spectral intervals, achieved the lowest relative prediction error (REP%) of 6.84 % on a test set. Validation on real samples spiked with 3–20 % methanol confirmed the method's ability to quantify methanol with absolute relative errors below 10 % in most cases. Compared to conventional techniques like gas chromatography (GC) and high-performance liquid chromatography (HPLC), this approach requires minimal sample preparation and shorter analysis time, making it suitable for quality control in the food, pharmaceutical, and cosmetic industries.
期刊介绍:
Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science.
The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments.
Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate.
Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to:
Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences,
Novel experimental techniques or instrumentation for molecular spectroscopy,
Novel theoretical and computational methods,
Novel applications in photochemistry and photobiology,
Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.