Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee

Abstract

The agronomic practices and environmental conditions for coffee cultivation, such as climate, soil type and altitude, promote influence in the final chemical composition of the grain. Furthermore, the genotype directly influences the essential features of the beverage, increasing its aggregate price. Proof of geographic and genotypic origin of the coffee genotype must be done using reliable methods. Thus, near infrared spectroscopy (NIR) was used to analyze different coffee genotypes that were cultivated in Brazil. Due to complexity and quantity of information within the spectra, partial least square discriminant analysis (PLS-DA) were applied to analyze the NIR data. The multiplicative scatter correction (MSC) and the Savitzky–Golay second-derivative were tested as preprocessing techniques to find which one provides an appropriate identification model. The best model achieved correctly identified 94.4% of validation samples for both geographic and genotypic origin. The results demonstrate that NIR spectroscopy provides significant analytical data to be used in tandem with PLS-DA to distinguish green coffee samples geographically and genotypically.