A sustainable and straightforward approach for the authentication of roasted coffee samples based on absorption spectrophotometry coupled with Data Driven-Soft Independent Modelling of Class Analogy

Abstract

Coffee is one of the world's most consumed beverages and the second most traded commodity. Natural roasted coffee, produced by heating green beans to develop its characteristic flavor, is highly appreciated. A variant, torrefacto coffee, incorporates sugar during roasting. While legitimate, this practice can be used to mask off-flavors from lower-quality beans and artificially increase weight, sometimes leading to mislabeling. This study proposes a simple, robust method to authenticate roasted coffee using UV absorbance spectroscopy and a one-class modeling algorithm (Data Driven-Soft Independent Modelling of Class Analogies). Samples of natural, torrefacto, and in-lab adulterated coffee (10%, 25%, and 50%), underwent a water extraction and were diluted for absorbance measurements (200 to 400 nm). A discriminant model was built using the first four principal components, which explained 99.7% of the spectral variance. Trained on 80% of the natural coffee samples, the model was validated on a test set containing the remaining natural, torrefacto, and adulterated samples. The method proved highly effective, detecting adulteration levels as low as 10%. It achieved 100% sensitivity, 97% specificity, and 97% overall accuracy. A White Analytical Chemistry assessment yielded an 86.2% whiteness score, confirming a strong balance between sustainability and analytical performance.