Detection of fraud in high-quality rice by near-infrared spectroscopy

A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. This study was based on near-infrared spectroscopy (NIRS) analysis technology, partial least squares discriminant analysis (PLS-DA), and a support vector machine (SVM) to detect whether high-quality rice was mixed with other varieties of rice. As an aid to qualitative discrimination, PLS was used to establish the quantitative analysis model to assist in the recognition of the degree of fraud. Due to the direct correlation between the results of NIRS analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared, each group consisted of 20 pure samples and 140 mixed samples, and the mixing ratio was between 5% and 50%, with an interval of 5%. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit, respectively. Regarding quantitative analysis, the performance of the prediction model was closely related to the particle size of the samples: 100 mesh > 70 mesh > 40 mesh > full grains. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. These results demonstrate that NIRS analysis technology is a reliable and fast tool to determine whether high-quality rice contains other varieties of rice. PRACTICAL APPLICATION: The work of this article is based on the current background of increasingly serious rice fraud, using near-infrared spectroscopy to quickly identify fraudulent rice, to a certain extent, and effectively alleviate the rice fraud. This technology can serve for the supervision of food regulatory agencies on rice fraud, and can also be used in food factories to ensure the authenticity of raw materials of rice.