Predictive Modeling of Beer Quality Parameters via FT-NIR Spectroscopy: Calibration, Accessory Comparison, and Chemometric Validation

Near-infrared spectroscopy (NIR) is a non-destructive technique for rapid and accurate food analysis. FT-NIR analysis requires no solvents and is particularly suitable for routine quality control applications. This study evaluated the application of Fourier-transform near-infrared (FT-NIR) spectroscopy for estimating key physicochemical parameters in beer, including pH, final gravity, alcohol content, acidity, bitterness units (IBU), and α-acid content, in finished products and during late fermentation and maturation. A dataset of 350 samples was used for calibration and an independent hold-out subset of 50 samples was used for validation. Spectral data were collected with quartz cuvettes of 1.0, 0.5, and 0.1 cm path lengths, and a NIRA reflectance accessory (0.1 cm). Models were developed using Partial Least Squares (PLS) regression with 10 latent variables and processed via Spectrum Quant software. All configurations produced calibration R2 values above 0.84. Baseline corrections with first and second derivatives were tested. While most models exhibited satisfactory calibration performance, only NIRA maintained satisfactory performance in the independent hold-out validation subset, with regression slopes closest to unity and comparatively low standard errors. The model was applied as an exploratory case study to monitor physicochemical changes during a single fermentation and maturation process, showing no statistically significant differences from conventional analytical methods.