Enhancing Coffee Quality and Traceability: Chemometric Modeling for Post-Harvest Processing Classification Using Near-Infrared Spectroscopy

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification.