Zulfahrizal Zulfahrizal, Agus Arip Munawar
This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. © 2021, Walailak University. All rights reserved.
Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh, 23111, Indonesia
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