Chocolates sold in Ghana are stored under different conditions that are suspected to affect their appearance, flavour and texture. Rapid and non-invasive techniques such as near-infrared spectroscopy (NIRS) have been lauded for their reliability and cost-effectiveness that can be very useful for chocolate monitoring.
This study developed a rapid and non-destructive method to predict the quality of chocolate obtained from three different sales outlets based on the location and conditions of retail. Data for physicochemical analysis (total color change, total phenolics, free fatty acid, peroxide value, moisture, hardness, and aluminum content) and mold count of chocolate were collected using standard protocols.
These data and results obtained from NIRS in the wavelength range 900–1700 nm, were used to develop chemometric models to predict the parameters measured and classified the chocolate samples. Chocolate from the street recorded the highest mold count of 10.00 ± 18.92 cfu/g. Although the physicochemical analysis showed that different retail conditions had no significant effect on the chocolate quality parameters, the NIRS models could classify the chocolates based on retail conditions, with an average recognition and prediction accuracy of 75.41 % and 71.59 %, respectively. The regression model could predict the total color change with R2CV of 0.503 and RMSECV of 4.96 w/w.
The findings suggest that NIRS combined with chemometrics could be used to classify chocolate sold under different conditions at different retail locations. However, the models could not predict other physicochemical quality parameters.
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