Investigation of carbon and nitrogen metabolism and online analysis of key substances in the tobacco leaf curing process using chromatography–mass spectrometry and near-infrared spectroscopy coupled with machine learning

Curing technology is commonly used in the post-harvest processing of fresh agricultural products and herbal medicines, to maintain a stable color, form a specific aroma, and enable long-term storage. Studying the transformation mechanisms and online monitoring of key substances is crucial for the optimization and intellectualization of the curing process. Herein, the curing process of the model plant Nicotiana tabacum L. was selected to study the above issues. Firstly, the untargeted metabolomics based on gas chromatography–mass spectrometry identified 202 metabolites in 17 categories including carbohydrates, organic acids, amino acids, and alkaloids, and 65 metabolic markers were screened using principal component analysis. Then, by using targeted metabolomics based on liquid chromatography–tandem mass spectrometry, the carbon and nitrogen metabolism was investigated, including glycolysis, sugar metabolism, the tricarboxylic acid cycle, amino acid metabolism, alkaloid metabolism, the pentose phosphate pathway, shikimic acid metabolism, phenylpropanoid metabolism, and the urea-polyamine cycle. Finally, rapid online process analysis methods were constructed for 40 markers using a fiber-optic probe near-infrared reflection spectrometer combined with machine learning. The determination coefficient of these models is above 0.8, and the ratio of the root mean square error of the test set to that of the training set is < 2. The proposed strategy is expected to be used for systematic analysis, optimization, and intelligent monitoring of the curing process of fresh agricultural products and herbal medicines.