Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy.

ABSTRACT: Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indicators. In this study, the just-in-time learning-integrated partial least-squares (JIT-PLS) modeling strategy was applied for the first time to quantitatively analyze 71 chemical components in Chinese tobacco. Approximately 18000 tobacco samples from China were analyzed to find appropriately similar measurements and propose suitable and flexible similar subsets from the calibration for each test sample. In total, 879 representative aged tobacco leaf samples and 816 cigarette samples were used as external instances

SUBMITTER: Liang Y 

PROVIDER: S-EPMC9631892 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature