Detection of ground coffee adulteration using FTIR coupled with pattern recognition techniques

Ground coffee is highly susceptible to economically motivated adulteration, necessitating robust analytical methods for authenticity verification. This study investigates the efficacy of Fourier Transform Infrared (FTIR) spectroscopy combined with pattern recognition techniques for detecting adulteration in ground coffee with barley, chickpea, and date pit powders. Spectral fingerprints of pure and adulterated samples were acquired, preprocessed and analyzed via supervised (Random Forest, K-Nearest Neighbors, Decision Tree) and unsupervised (Principal Component Analysis, Hierarchical Cluster Analysis) learning models. Results demonstrated that spectral preprocessing significantly enhanced classification performance, with Random Forest achieving the highest accuracy (94 % training, 83 % testing) among supervised models. Hierarchical Cluster Analysis (HCA) outperformed all methods, achieving 96.8 % accuracy with perfect classification for pure coffee, date pit, and chickpea samples. This study uniquely provides a comprehensive comparative evaluation of supervised and unsupervised models, highlighting the superior potential of HCA for rapid and label-free screening of coffee adulteration.