Identification of adulterated white pepper powder with roasted rice by using near-infrared hyperspectral imaging

The substitution of powdered white pepper on the commercial market with similar, but cheaper powders extracted from various food products can make it more profitable, but reduce its quality. Near-infrared hyperspectral imaging (NIR-HSI) is a technique that has been successfully used to detect contamination in other food products. Therefore, NIR-HSI was tested on powdered white pepper that had been adulterated with various levels of roasted rice powder, using partial least squares discriminant analysis (PLS-DA), support vector machine classification (SVMC), partial least squares regression (PLSR), and support vector machine regression (SVMR) methods to test whether adulteration could be detected, and if so, at what level. The results showed that the highest predictive accuracy of classification was 100% by using PLS-DA. The calibration model was also developed to determine the level of adulteration in white pepper powder by roasted rice powder. The SVMR model gave the highest predictive accuracy with a coefficient of determination for prediction (R2p) of 0.95, and root mean square error of prediction (RMSEP) of 6.82%. The results indicate that NIR-HSI has the potential for detecting adulteration of powdered white pepper and can be successfully applied in food quality control for ensuring consumer confidence.