Combining portable NIR spectroscopy and multivariate calibration for the determination of ethanol in fermented alcoholic beverages by a multi-product model

• Published in: Talanta open Vol. 7; p. 100180
• Main Authors: Fulgêncio, Ana Carolina da Costa, Resende, Glaucimar Alex Passos, Teixeira, Marden Claret Fontoura, Botelho, Bruno Gonçalves, Sena, Marcelo Martins
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-08-2023; Elsevier
• Subjects: Alcoholic strength; Chemometrics; Direct analysis; Handheld NIR sensor; Miniaturization; Portability; Portability; Alcoholic strength; Miniaturization; Handheld NIR sensor; Direct analysis; Chemometrics
• ISSN: 2666-8319; 2666-8319
• DOI: 10.1016/j.talo.2023.100180

•Portability and NIR spectroscopy combined for an environmentally friendly method.•Direct, simple, rapid and green quantification of ethanol in fermented beverages.•A robust multivariate calibration model built with spectra obtained by a NIR sensor.•A multiproduct model applied to four matrices: beer, wine, mead and cider.•Multivariate analytical validation of the method in the ethanol range of 4.3–15.3%.
In this study, a multivariate calibration multi-product model was built by combining partial least square regression (PLS) and portable near infrared (NIR) spectroscopy for the determination of ethanol content in fermented alcoholic beverages. Reference values were obtained by gas chromatography with flame ionization detection (GC-FID). Aiming at building a robust model, a great variety of beers, ciders, meads, and wines were incorporated into the model. NIR spectra were recorded between 908 and 1676 nm for 153 alcoholic beverage samples, corresponding to a range from 4.3 to 15.3% (v/v) of alcohol content. PLS model provided accurate results with root mean square errors of calibration (RMSEC) and prediction (RMSEP) of 0.8% and 0.9%, respectively. The developed method was validated through the estimate of proper figures of merit, such as linearity, trueness, precision, analytical sensitivity, bias, and residual prediction deviation (RPD). This method was simple, direct, rapid, of low-cost and environmentally friendly, not consuming reagents or solvents nor generating chemical waste. It could be incorporated in analytical platforms for quality inspection, contributing to provide better transparency in the food supply chain.
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