Compound Identification Using Partial and Semipartial Correlations for Gas Chromatography–Mass Spectrometry Data

Compound identification is a key component of data analysis in the applications of gas chromatography–mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and <i>m</i>/<i>z</i> value, respectively

A ¹H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various <i>Oryza sativa</i> L. Cultivars

Rice grain metabolites are important for better understanding of the plant physiology of various rice cultivars and thus for developing rice cultivars aimed at providing diverse processed products. However, the variation of global metabolites in rice grains has rarely been explored. Here, we report the identification of intra- or intercellular metabolites in rice (<i>Oryza sativa</i> L.) grain powder using a ¹H high-resolution magic angle spinning (HR-MAS) NMR-based metabolomic approach. Compared with nonwaxy rice cultivars, marked accumulation of lipid metabolites such as fatty acids, phospholipids, and glycerophosphocholine in the grains of waxy rice cultivars demonstrated the distinct metabolic regulation and adaptation of each cultivar for effective growth during future germination, which may be reflected by high levels of glutamate, aspartate, asparagine, alanine, and sucrose. Therefore, this study provides important insights into the metabolic variations of diverse rice cultivars and their associations with environmental conditions and genetic backgrounds, with the aim of facilitating efficient development and the improvement of rice grain quality through inbreeding with genetic or chemical modification and mutation