Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine

Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and inertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and inertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except inertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae

Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine

Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and inertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and inertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except inertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae

Formation of Volatile Tea Constituent Indole During the Oolong Tea Manufacturing Process

Indole is a characteristic volatile constituent in oolong tea. Our previous study indicated that indole was mostly accumulated at the turn over stage of oolong tea manufacturing process. However, formation of indole in tea leaves remains unknown. In this study, one tryptophan synthase α-subunit (TSA) and three tryptophan synthase β-subunits (TSBs) from tea leaves were isolated, cloned, sequenced, and functionally characterized. Combination of CsTSA and CsTSB2 recombinant protein produced in <i>Escherichia coli</i> exhibited the ability of transformation from indole-3-glycerol phosphate to indole. CsTSB2 was highly expressed during the turn over process of oolong tea. Continuous mechanical damage, simulating the turn over process, significantly enhanced the expression level of CsTSB2 and amount of indole. These suggested that accumulation of indole in oolong tea was due to the activation of CsTSB2 by continuous wounding stress from the turn over process. Black teas contain much less indole, although wounding stress is also involved in the manufacturing process. Stable isotope labeling indicated that tea leaf cell disruption from the rolling process of black tea did not lead to the conversion of indole, but terminated the synthesis of indole. Our study provided evidence concerning formation of indole in tea leaves for the first time