determination of moisture content in ground coffee using near-infrared spectroscopy and chemometrics

Moisture content (mc) is one of the parameters that can affect the quality of coffee. the high moisture content in ground coffee can cause the growth of microbes or fungi, decreasing coffee quality. this study aims to determine a calibration model using nir spectroscopy and chemometrics to ascertain the moisture content of ground coffee. the samples of ground coffee with varying moisture content were divided into thirty training sets and ten test samples. the nir spectra of the ground coffee samples were correlated with the moisture content to form a calibration model. the calibration models used were partial least squares (pls), principal component regression (pcr), and support vector regression (svr). the model formed was then validated using leave-one-out cross-validation and external validation, and the accuracy of the stable calibration model was evaluated. data analysis revealed that the pls of the original spectra is the best calibration model, with r2 and rmse values of 0.993 and 0.414, respectively. leave one out cross-validation of pls shows an r2 of 0.993 and rmse of 0.422. external validation shows r2 (pearson), r2, rmsep, and bias of 0.974, 0.972, 0.578, and 0.015, respectively. the accuracy of mc determined by the pls model has a %recovery of 100.4% with a tolerance interval of 92.65%-108.22%. overall, the study proposes the pls model to have good stability. the tandem protocols of nir spectroscopy combined with chemometrics can determine the moisture content of the ground coffee, suggesting a rapid, simple, precise, and accurate alternative method.