Rice is the second most important food staple worldwide. In order to prevent fraud, there is the need for an effective and rapid technique for monitoring the origin and quality of rice. This study investigated the novel application of a hand-held NIR spectrometer and chemometric analysis to estimate the quality and country of origin in real-time. A total of 520 rice samples of different quality grades (high, mid and low quality) and different countries (Ghana, Thailand, and Vietnam) of origin were used. Among the pre-processing methods used, multiplicative scatter correction (MSC) was found to be superior. Principal component analysis (PCA) was used to extract relevant information from the spectral data set and the results showed that rice samples of the different categories could be clearly clustered under the first three PCs using the MSC preprocessing method. For determining rice quality grades, the classification rate gave 91.62% and 91.81% in training set and prediction set respectively. While the identification rate based on different countries of origin was 90.84% and 90.64% in both training set and prediction set respectively. Differentiation of local rice from the imported, the identification rate was 100% in both the training set and prediction set.
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