| Popis: |
This study delves into the ability of near infrared (NIR) techniques by means of a self-developed portable sensing device near-infrared reflectance spectroscopy (NIRS) i16 USK instrument to accurately predict the moisture content (MC) and chlorogenic acid (CGA) within intact coffee beans through the development of calibration models. Spectral absorbance measurements were conducted across the 1,000‒2,500 nm wavelength range. Leveraging two multivariate calibration approaches namely principal component regression and partial least square regression (PLSR) for 74 bulk coffee beans (60 g) in calibration and 36 bulk coffee beans samples in external validation. The results reveal a notably high determination coefficient (R 2) of 0.984 for MC and 0.908 for CGA in calibration using PLSR, indicating the feasibility of rapid, simultaneous, and non-destructive prediction. Furthermore, upon external validation, the PLSR model exhibited consistent predictive performance, with R 2 values for MC and CGA contents reaching 0.978 and 0.846, respectively. Consequently, these outcomes underscore NIR as an effective, concurrent, and non-invasive means to assess the quality parameters and attributes of intact coffee beans, presenting promising prospects for the advancement of coffee quality evaluation. |
