Enhancing beer authentication, quality, and control assessment using non-invasive spectroscopy through bottle and machine learning modeling.

ABSTRACT: Fraud in alcoholic beverages through counterfeiting and adulteration is rising, significantly impacting companies economically. This study aimed to develop a method using near-infrared (NIR) spectroscopy (1596-2396 nm) through the bottle, along with machine learning (ML) modeling for beer authentication, quality traits, and control assessment. For this study, 25 commercial beers from different brands, styles, and three types of fermentation were used. To obtain the ground-truth data, a quantitative descriptive analysis was conducted with 11 trained panelists to evaluate the intensity of 16 sensory descriptors, and volatile aromatic compounds were analyzed using gas chromatography-mass spectroscopy (GC-MS). The ML models were developed using artificial neural networks with NIR absorbance va

SUBMITTER: Harris N 

PROVIDER: S-EPMC11745409 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature