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

Abstract

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.

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

“…NIR analysis technology, PLS-DA, and SVM have been used to detect whether highquality rice was mixed with other varieties of rice. NIR spectral data analyzed using PLS-DA and a SVM algorithm, was shown to be a feasible method (5% detection limit) for the rapid identification of fraudulent rice varieties blended with authentic Wuchang rice samples [133].…”

“…Studies performed by Liu et al showed that those techniques represent a significant support to qualitative discrimination [133]. PLS was used to establish the quantitative analysis model to support in the recognition of the degree of fraud.…”

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

“…In contrast, FTIR and NIR techniques are rapid, cost-effective, do not require specialized laboratory facilities and could therefore be used for pre-screening samples for SITE confirmatory or orthogonal analysis in enforcement work. Although FTIR and NIR spectroscopy were applied in several recent studies for the authentication of rice [27][28][29][30][31]33], to our knowledge, there have been no studies published that applied and compared benchtop FTIR-ATR and handheld NIR spectroscopy for differentiating the geographical origins of Thai Hom Mali rice.…”

“…The PLS-DA approach provided satisfactory results with total classification rates of 82.6% and 82.4% for authentic and adulterated samples, respectively. NIR spectroscopy was also reported as successfully discriminating authentic (n = 20) and adulterated (n = 140) rice from China [29]. Sampaio, Castanho, Almeida, Oliveira and Brites [30] applied NIR spectroscopy with principal component analysis (PCA), PLS-DA and support vector machines (SVM) for the discrimination and classification of rice varieties (Indica and Japonica) grown in Portugal.…”

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

“…Food fraud remains a significant problem for food regulators, importers, merchants, law enforcement personnel, and the consumer. NIR spectral data analyzed using partial least squares discriminant analysis and implementing a support vector machine algorithm, was recently shown to be a feasible method for the rapid identification of fraudulent rice varieties (5% detection limit) blended with authentic Wuchang rice samples [90]. The authors further noted that more uniform particle sizes aided in quantitative analysis (i.e., 100 mesh > 70 mesh > 40 mesh > full granules).…”