Nguyen Phuoc Long

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice

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.

Simultaneous Profiling of Lysoglycerophospholipids in Rice (Oryza sativa L.) Using Direct Infusion-Tandem Mass Spectrometry with Multiple Reaction Monitoring

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.

The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L.

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 (R2), goodness-of-prediction (Q2), 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.

Optimized Mass Spectrometry-Based Metabolite Extraction and Analysis for the Geographical Discrimination of White Rice (Oryza sativa L.): A Method Comparison Study

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.

Multi-platform metabolomics and a genetic approach support the authentication of agarwood produced by Aquilaria crassna and Aquilaria malaccensis

HighlightsCurrent examination of agarwood is mostly based on visual examination and thus prone to error.Multi‐platforms metabolomics well distinguished two popular agarwood types.DNA markers also showed clear differences in genetic variation between two agarwood species.Metabolomics and genetic approach both well supported the authentication of agarwood. Abstract Agarwood, the resinous heartwood produced by some Aquilaria species such as Aquilaria crassna, Aquilaria malaccensis and Aquilaria sinensis, has been traditionally and widely used in medicine, incenses and especially perfumes. However, up to now, the authentication of agarwood has been largely based on morphological characteristics, a method which is prone to errors and lacks reproducibility. Hence, in this study, we applied metabolomics and a genetic approach to the authentication of two common agarwood chips, those produced by Aquilaria crassna and Aquilaria malaccensis. Primary metabolites, secondary metabolites and DNA markers of agarwood were authenticated by 1H NMR metabolomics, GC–MS metabolomics and DNA‐based techniques, respectively. The results indicated that agarwood chips could be classified accurately by all the methods illustrated in this study. Additionally, the pros and cons of each method are also discussed. To the best of our knowledge, our research is the first study detailing all the differences in the primary and secondary metabolites, as well as the DNA markers between the agarwood produced by these two species.

A rapid and reliable method for discriminating rice products from different regions using MCX-based solid-phase extraction and DI-MS/MS-based metabolomics approach

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, R2=0.916, Q2=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, R2=0.931, Q2=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.

Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies.

Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Two microarray data sets included found 21 significant genes. Of these significant genes, ALDOA, IL8 (CXCL8), and PARP4 had strong potential as prognostic candidates. LAMB2, MCM7, CXCL23A, SERPINA3, ABCA3, ALDH3A2, and POLR2I also have potential. Other candidates were more controversial, possibly because of the biologic heterogeneity of tumor cells, which is a major obstacle to predicting metastasis. In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation.

Impact of Milling on Rice Constituents (Oryza Sativa L.): A Metabolomic Approach

ABSTRACT Milling is an influential factor that affects the nutritional components in rice. However, the alteration of rice constituents by milling has not been thoroughly examined. In this study, rice with various degrees of milling was analyzed by gas chromatography–mass spectrometry and high-performance liquid chromatography–mass spectrometry. Principal component analysis and partial least square-discriminant analysis were performed to characterize the nutritional components that have significant changes during milling. The concentrations of sugars and sugar alcohols decreased while the phospholipids increased in accordance with the milling degree. These results provide a contrast to the common idea that brown rice is nutritionally superior to white rice. In conclusion, the knowledge of nutritional alterations related to milling may benefit rice production and consumption.