Abstract
Fat, sugar, theobromine, and caffeine are important compounds in chocolates that influence the physico-chemical and sensory characteristics of the products. These parameters commonly are determined with conventional, time-consuming, environmentally pollutant methods. In this study, near infrared spectroscopy (NIRS) coupled with partial least square regression (PLSR) was used to predict the quantity of these compounds. Fat-free cocoa content was calculated based on the alkaloid concentration, while measured fat content was also used for the cocoa content. Prediction models have also been developed for these parameters. The determination coefficient (R2) of the models were as follows: R2cv fat 0.98, theobromine 0.94, caffeine 0.76, sucrose 0.92, cocoa 0.98. The root mean square error (RMSE) was found to be relatively low compared to the determined values (RMSECV fat 1.08%, theobromine 0.42mg/g, caffeine 0.1mg/g, sucrose 3.42%, cocoa content 1.93%). Therefore, NIRS can be suitable for the analysis of the quality of dark chocolate.
Published Version
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