## 2 Results and Discussion  
### PCA, PLS-DA, and OPLS-DA  
To elucidate the overall metabolome patterns in rice seeds of three different colors, GC\(\times\)GC-TOF-MS, solid-phase micro-extraction (SPME)-GC-TOF-MS, GC-qMS, GC-FID,HPLC-MS, and HPLC-UV were used to comprehensively profile primary and secondary metabolites in seeds of 16 cultivars of rice. Overall, 110 metabolites, including amino acids, organic acids, sugars, sugar alcohols, phenolic acids, flavonoids, anthocyanins, carotenoids, phytosterols, policosanols, tocopherols, fatty acids, and volatiles, were identified. Multivariate statistical analysis is an important tool used for obtaining an overview of patterns in complex experimental data. Specifically, PCA is a preliminary step in a multivariate analysis performed to discern novel information hidden in the big data. Accordingly, PCA of the obtained metabolite profiles was performed so that each point in the score plot indicated an individual sample, and samples with similar metabolite composition clustered together. PCA did not reveal a clear separation of the rice seeds by color (Figure S1). PLS-DA was then performed to optimize the separation of samples and to determine metabolic differences arising from the rice color. PLS-DA is a projection method, which rotates the PCA projection to obtain maximum separation by classes of observations based on their variables. In the analysis, the rice seed colors were set as classes and an internal validation method was used for model validation. For the latter, validation parameters (\(R^{2}\) and \(Q^{2}\)) indicate the quality of the model. \(R^{2}\) specifies the proportion of variation in the data that is explained by the model, and \(Q^{2}\) specifies the proportion of variation in the data that is predictable by the model. The \(R^{2}\)- and \(Q^{2}\)-values fall between zero and one: \(R^{2}\) close to 1 is desirable; \(Q^{2}>0.5\) indicates a good prediction model; and \(Q^{2}>0.9\) indicates an excellent prediction model. PLS-DA showed clear separation by color of rice seeds (Figure 1A). The validation analysis of the PLS-DA model yielded \(R^{2}\)X of 0.312, \(R^{2}\)Y of 0.792, and \(Q^{2}\) of 0.725. The \(Q^{2}\)-value was larger than 0.50, indicating a good predictive ability of the model. PLS 1 significantly contributed to the separation of BR from other rice samples. Metabolites in the loading plots explain the separation of corresponding samples on score plots. Significant metabolites of PLS 1 were fumaric acid, malic acid, epicatechin, catechin, xylose, lutein, vanillic acid, \(\beta\)-carotene, stigmasterol, and protocatechuic acid, for which the eigenvector values were \(-0.145934\), \(-0.133728\), \(-0.129379\), \(-0.122005\), \(-0.103077\), \(0.203814\), \(0.200285\), \(0.190348\), \(0.180684\), and \(0.178313\), respectively (Figure 1B and Table S1). The eigenvector values of fumaric acid, malic acid, epicatechin, catechin, and xylose were negative, and contributed to the separation of BR from RR, and their content in RR was higher than that in other rice samples (Tables S2 and S3). RR contains high amounts of proanthocyanidin, which is composed of oligomers of catechin and epicatechin, and has a red color [4,19]. Hence, catechin and epicatechin significantly contributed to the separation of RR from other rice samples. By contrast, lutein, vanillic acid, \(\beta\)-carotene, stigmasterol, and protocatechuic acid, which had positive eigenvector values, contributed significantly to BR identity, and their amounts were higher in BR than in RR and WR (Tables S1, S2 and S4). According to a previous study, BR contains high amounts of carotenoids and phytosterols [3,20]. This is in agreement with our findings. Stigmasterol was the most abundant phytosterol in BR (Table S4). The BR color is driven by anthocyanins, such as peonidin-3-glucoside and cyanidin-3-glucoside [21,22]. Furthermore, most volatiles, such as, nonanal, octanal, benzaldehyde, 2-heptanone, 1-octen-3-ol, and naphthalene, were more abundant in BR than in other rice samples (Table S5).