Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics

Abstract

Rice is the second most important food staple worldwide and the demand will continue to increase with the growth of the world population. As reports grow that frauds is prevalent in many supply chains there is the need for an effective and rapid technique for monitoring the authenticity and quality of rice. This study investigated the novel application of hand-held NIR spectrometry coupled to chemometric for the estimation of rice authenticity and quality in real time. A total of 520 Rice samples from different quality grades (high quality, mid quality 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 different categories could be clearly clustered under the first three PCs using the MSC preprocessing method. The performance of Knearest neighbor (KNN) revealed that for authentication of rice quality grades, the classification rate gave 91.62% and 91.81% in training set and prediction set respectively while identification rate based on different country of origin was 90.84% and 90.64% in both training set and prediction set respectively. For the differentiation of local rice from the imported, KNN and SVM all had 100% in both the training set and prediction set. These gives very strong evidence that hand-held spectrometry coupled with MSC-PCA-KNN could successfully be used to

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

“…The main component of rice is starch. Rice starch is a polysaccharide polymer compound composed of glucose, which contains amylopectin mainly composed of a branched structure and amylose mainly composed of a linear structure [18,19]. Due to the influence of geographical location, the rice's starch content, molecular weight, spatial structure, and their relationship are different.…”

“…A total of 720 rice samples, including genetically modified rice Bt Shanyou 63 (Bt-SY63) and its non-transgenic isogenic counterparts Shanyou63 (SY63), were used to discriminate transgenic (Bt63)/non-transgenic characteristics; both transgenic and non-transgenic rice samples were 360 in total (Table 5) The main component of rice is starch. Rice starch is a polysaccharide polymer compound composed of glucose, which contains amylopectin mainly composed of a branched structure and amylose mainly composed of a linear structure [18,19]. Due to the influence of geographical location, the rice's starch content, molecular weight, spatial structure, and their relationship are different.…”

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

“…In our study, SNV processing proved to be a suitable technique for baseline correction (Figure 2A(ii)), in line with other studies. 22,24,30 As NIR spectra, in general, and these small-range spectra, in particular, are relatively information poor, a subsequent derivative preprocessing step is often suggested to put emphasis on the spectral differences. Consistent with an earlier NIR study on narcotics from Liu et al, 22 Figure S3 (supporting information).…”

Geographical Origin Authentication of Agri-Food Products: A Review

“…Teye et al [47] suggested that NIR spectroscopy, in combination with the appropriate chemometrics, could be used for rapid and nondestructive detection of rice authenticity and quality. The systematic selection of different preprocessing methods (MC, DT, SNV, and MSC) with principal component analysis (PCA) and modeling with the k-nearest neighbor (KNN) and support vector machine (SVM) multivariate calibration model showed that MSC + PCA plus KNN showed superiority in this study, with more than a 90% classification rate for all categories of rice samples studied.…”