A Near-Infrared Spectroscopy Dataset for Chemical Composition Prediction and Origin Identification of Tobacco Leaves

This database contains two core asset types: Data Files and Model Files.
1. Data Files
The dataset is provided in two separate .xlsx files:
Raw-nir-spectra-data: This file contains the raw near-infrared spectral dataset. It records the spectral information for all 347 tobacco samples and includes metadata such as each sample's unique ID, cultivation year, and country of origin.
13-Chemical-Components-data: This file contains the reference dataset for the chemical constituents. It includes the quantitative analysis results for the 13 key chemical components for all 347 samples, corresponding one-to-one with the spectral data.
2. Model Files
The database provides 99 pre-trained prediction and classification models in .joblib format. All models were built in a Python 3.9 environment and can be loaded and called directly. To facilitate easy identification and use, the model files adhere to the following naming conventions:
A. Quantitative Models (Chemical Prediction)
This naming format is used for the quantitative prediction models of the 13 chemical constituents.
Format: [Chemical_Component]_[Preprocessing_Method]_[Modeling_Method].joblib
Example: TotalSugars_MSC_PLS.joblib represents a PLS model for predicting Total Sugars using MSC preprocessing.
B. Classification Models (Origin Prediction)
This naming format is used for classification models built with different types of input data.
Format (based on spectral data): [Preprocessing_Method]_[Modeling_Method].joblib
Example: SecondDerivative_RF.joblib represents a Random Forest (RF) classification model built using second-derivative spectral data.
Special Note:
The file Thirteen_chemical_components-RF.joblib is a special classification model. It does not use spectral data; instead, it is built using the quantitative results of the 13 chemical components directly as its input features.