ArticleACS omega2022
Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Infrared Spectroscopy for Variety Identification and Authenticity Analysis of Tobacco Samples.Sensors (Basel, Switzerland) · 2026Article
- A dataset for geographical origin identification of tobacco leaves from multiple countries using near-infrared spectroscopy and chemometric analysis.Data in brief · 2026Article
- Machine learning-driven identification of key chemical determinants of tobacco leaf sensory irritation.Frontiers in chemistry · 2026Article
- Enabling rapid and accurate grand discrimination of flue-cured tobacco: a near-infrared hyperspectral and machine learning approach.Frontiers in plant science · 2026Article
- Rapid discrimination of geographical origin and analysis of chemical characterization of tobacco leaves from multiple countries.Frontiers in chemistry · 2026Article
- Quantitative analysis of tobacco blending proportions based on hyperspectral imaging and data fusion.Frontiers in plant science · 2025Article
- Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning.Frontiers in plant science · 2025Article
- Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning.Frontiers in plant science · 2025Article
- Using preprocessed datasets to construct and interpret multiclass identification models.Frontiers in plant science · 2025Article
- Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 to evaluate the practical predicting ability of the proposed method. The most suitable similar subsets for each test sample could be selected by limiting the Euclidean distance and number of similar subsets to 0-3.0 × 10
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Registered trials
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