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Volume 8
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10.3390/separations8010009
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Article

Chromatographic Profiling with Machine Learning Discriminates the Maturity Grades of Nicotiana tabacum L. Leaves

by
Yi Chen
1,†,
Miao Tian
2,†,
Gaokun Zhao
1,
Hongmei Lu
2,
Zhimin Zhang
2,* and
Congming Zou
1,*
1
Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, China
2
College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
†
Yi Chen and Miao Tian contributed equally.
Separations 2021, 8(1), 9; https://doi.org/10.3390/separations8010009
Received: 28 December 2020 / Revised: 8 January 2021 / Accepted: 13 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Chromatographic Analysis of Biological Samples)
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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. View Full-Text
Keywords: UPLC-IT-TOF/MS; Nicotiana tabacum L.; maturity grades; classification; identification UPLC-IT-TOF/MS; Nicotiana tabacum L.; maturity grades; classification; identification
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MDPI and ACS Style

Chen, Y.; Tian, M.; Zhao, G.; Lu, H.; Zhang, Z.; Zou, C. Chromatographic Profiling with Machine Learning Discriminates the Maturity Grades of Nicotiana tabacum L. Leaves. Separations 2021, 8, 9. https://doi.org/10.3390/separations8010009

AMA Style

Chen Y, Tian M, Zhao G, Lu H, Zhang Z, Zou C. Chromatographic Profiling with Machine Learning Discriminates the Maturity Grades of Nicotiana tabacum L. Leaves. Separations. 2021; 8(1):9. https://doi.org/10.3390/separations8010009

Chicago/Turabian Style

Chen, Yi, Miao Tian, Gaokun Zhao, Hongmei Lu, Zhimin Zhang, and Congming Zou. 2021. "Chromatographic Profiling with Machine Learning Discriminates the Maturity Grades of Nicotiana tabacum L. Leaves" Separations 8, no. 1: 9. https://doi.org/10.3390/separations8010009

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