Abstract
Nicotiana tabacum L. (NTL) is an important agricultural and economical crop. Its maturity is one of the key factors affecting its quality. Traditionally, maturity is discriminated visually by humans, which is subjective and empirical. In this study, we concentrated on detecting as many compounds as possible in NTL leaves from different maturity grades using ultra-performance liquid chromatography ion trap time-of-flight mass spectrometry (UPLC-IT-TOF/MS). Then, the low-dimensional embedding of LC-MS dataset by t-distributed stochastic neighbor embedding (t-SNE) clearly showed the separation of the leaves from different maturity grades. The discriminant models between different maturity grades were established using orthogonal partial least squares discriminant analysis (OPLS-DA). The quality metrics of the models are R2Y = 0.939 and Q2 = 0.742 (unripe and ripe), R2Y = 0.900 and Q2 = 0.847 (overripe and ripe), and R2Y = 0.972 and Q2 = 0.930 (overripe and unripe). The differential metabolites were screened by their variable importance in projection (VIP) and p-Values. The existing tandem mass spectrometry library of plant metabolites, the user-defined library of structures, and MS-FINDER were combined to identify these metabolites. A total of 49 compounds were identified, including 12 amines, 14 lipids, 10 phenols, and 13 others. The results can be used to discriminate the maturity grades of the leaves and ensure their quality.
Highlights
IntroductionNicotiana tabacum L. (NTL) is a Solanaceae plant with important economic significance
Nicotiana tabacum L. (NTL) is a Solanaceae plant with important economic significance.Maturity of the NTL leaves is the primary factor for grading, which is an important index to measure the quality [1]
The untargeted metabolomic leaves based on liquid chromatography mass spectrometry (LC-MS)
Summary
Nicotiana tabacum L. (NTL) is a Solanaceae plant with important economic significance. It is important and necessary to study the differences in metabolites of the leaves from different maturity grades. Simultaneous separation and detection of metabolites using LC-MS will generate complex datasets, which requires preprocessing of the data before statistical analysis for multiple samples. XCMS is the most common preprocessing tool in the metabolomics It combines peak detection and retention time alignment, groups the peaks, and generates the peak table for further statistical analysis [21,24]. We have developed a method to analyze and identify the metabolites in flue-cured NTL leaves. Rich chemical information in the leaves were extracted from an LC-MS dataset by data preprocessing and statistical analysis methods. The self-built library can be used for the identification of Solanaceae metabolites in the future
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