| Abstrak/Abstract |
The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of
adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa
powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study
were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved
using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine
successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares
(PLS), Lasso, Ridge, elastic Net, and RF regressions provided R
2
higher than 0.96 and root mean square error
<2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R
2
= 1, RMSE =
0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa
powder. |
