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For the authentication of white rice from different geographical origins, the selection of outstanding discrimination markers is essential. In this study, 80 commercial white rice samples were collected from local markets of Korea and China and discriminated by mass spectrometry-based untargeted metabolomics approaches. Additionally, the potential markers that belong to sugars & sugar alcohols, fatty acids, and phospholipids were examined using several multivariate analyses to measure their discrimination efficiencies. Unsupervised analyses, including principal component analysis and k-means clustering demonstrated the potential of the geographical classification of white rice between Korea and China by fatty acids and phospholipids. In addition, the accuracy, goodness-of-fit (R 2 ), goodness-of-prediction (Q 2 ), and permutation test p-value derived from phospholipid-based partial least squares-discriminant analysis were 1.000, 0.902, 0.870, and 0.001, respectively. Random Forests further consolidated the discrimination ability of phospholipids. Furthermore, an independent validation set containing 20 white rice samples also confirmed that phospholipids were the excellent discrimination markers for white rice between two countries. In conclusion, the proposed approach successfully highlighted phospholipids as the better discrimination markers than sugars & sugar alcohols and fatty acids in differentiating white rice between Korea and China., (Copyright © 2017. Published by Elsevier B.V.)

In this study, we examined the effects of different extraction methods for the GC-MS- and LC-MS-based metabolite profiling of white rice (Oryza sativa L.). In addition, the metabolite divergence of white rice cultivated in either Korea or China was also evaluated. The discrimination analysis of each extraction method for white rice from Korea and China and the corresponding discriminatory markers were estimated by unpaired t-test, principal component analysis, k-means cluster analysis, partial least-squares discriminant analysis (PLS-DA), and random forest (RF). According to the prediction parameters obtained from PLS-DA and RF classifiers as well as features that could be identified, the extraction method using 75% isopropanol heated at 100°C coupled with LC-MS analysis was confirmed to be superior to the other extraction methods. Noticeably, lysophospholipid concentrations were significantly different in white rice between Korea and China, and they are novel markers for geographical discrimination. In conclusion, our study suggests an optimized extraction and analysis method as well as novel markers for the geographical discrimination of white rice.

The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice., (Copyright © 2017. Published by Elsevier B.V.)

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The expansion of the global rice marketplace ultimately raises concerns about authenticity control. Several analytical methods for differentiating the geographical origin of rice have been developed, yet a high-throughput method is still in demand. In this study, we developed a rapid approach using direct infusion-mass spectrometry (DI-MS) to distinguish rice products from different countries. Specifically, the elimination of the matrix effect by a polytetrafluoroethylene (PTFE) filter, a mixed-mode cation exchange (MCX) solid-phase extraction (SPE) with 20% methanol, and an MCX SPE with 100% methanol were measured. Afterward, partial least squares discriminant analysis and random forests were applied to seek the optimal discrimination method. The results revealed that the combination of MCX SPE with 100% methanol and DI-MS in positive ion mode (accuracy=1.000, R 2 =0.916, Q 2 =0.720, B/W-based p-value=0.015) or the combination of MCX SPE with 20% methanol and targeted DI-MS/MS in positive ion mode (accuracy=1.000, R 2 =0.931, Q 2 =0.849, B/W-based p-value=0.002) showed the excellent discriminatory ability. Furthermore, differentially expressed metabolites including sodiated lysophosphatidylcholine, lysophosphatidylcholine, lysophosphatidylethanolamines and lysophosphatidylglycerol classes were found. In conclusion, our study provides a rapid and reliable platform for geographical discrimination of white rice and will contribute to the authenticity control of rice products., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.

White rice is the final product after the hull and bran layers have been removed during the milling process. Although lysoglycerophospholipids (lysoGPLs) only occupy a small proportion in white rice, they are essential for evaluating rice authenticity and quality. In this study, we developed a high-throughput and targeted lipidomics approach that involved direct infusion-tandem mass spectrometry with multiple reaction monitoring to simultaneously profile lysoGPLs in white rice. The method is capable of characterizing 17 lysoGPLs within 1 min. In addition, unsupervised and supervised analyses exhibited a considerably large diversity of lysoGPL concentrations in white rice from different origins. In particular, a classification model was built using identified lysoGPLs that can differentiate white rice from Korea, China, and Japan. Among the discriminatory lysoGPLs, for the lysoPE(16:0) and lysoPE(18:2) compositions, there were relatively small within-group variations, and they were considerably different among the three countries. In conclusion, our proposed method provides a rapid, high-throughput, and comprehensive format for profiling lysoGPLs in rice samples.