Near Infrared Technology: Fast and Non-destructive Approach to Predict Inner Quality Parameters of Intact Coffee Beans

Abstract

In this study, the potential ability of near infrared technology to rapidly predict moisture content (MC) and chlorogenic acid (CGA) of intact coffee bean samples is investigated. To achieve this purpose, absorbance spectral data were acquired and recorded in wavelength range from 1000 to 2500 nm. Prediction models, used to predict MC and CGA contents, were developed using two different regression approaches namely principal component regression (PCR) and partial least square regression (PLSR). The results showed that MC, and CGA contents can be predicted rapidly and simultaneously with maximum correlation coefficient (r) were 0.99 for MC and 0.93 for CGA. We also found that PLSR provided better and robust prediction results than PCR one. Thus, we may conclude that NIRS method can be used and applied as a fast, simultaneous and non-destructive method in determining inner quality attributes of intact green coffee beans.