研究目的
To demonstrate the use of decision trees and their ensembles for analyzing NIR spectroscopic data in regression and classification tasks, comparing their performance with traditional methods like partial least squares.
研究成果
Decision trees are efficient for classification, and ensembles like random forests improve performance for regression, outperforming PLS in some cases. Random forests provide robust variable importance and are recommended for discrimination tasks.
研究不足
Decision trees have drawbacks such as instability and inappropriateness for multivariate data with high collinearity, which are mitigated by ensembles like random forests. The study is limited to specific datasets and methods, and results may vary with different data characteristics.
1：Experimental Design and Method Selection:
The study uses decision trees (CART) and random forests for regression and classification of NIR spectroscopic data, compared with PLS and PLS-DA. Methods include model optimization, validation, and variable importance assessment.
2：Sample Selection and Data Sources:
Four datasets are used: Tecator and Beer for regression, Olive and Oil for classification. Datasets are split into calibration and test subsets.
3：List of Experimental Equipment and Materials:
NIR spectrometers (e.g., Tecator Infratec Food and Feed Analyzer, FT-NIR Thermo Scientific spectrometer Antaris? II FT-NIR Analyser), software R with packages mdatools, rpart, and randomForest.
4：Experimental Procedures and Operational Workflow:
Data preprocessing (e.g., SNV, MSC), model building with cross-validation, prediction on test sets, and evaluation using metrics like RMSE and accuracy.
5：Data Analysis Methods:
Statistical analysis including Gini index for classification, mean squared error for regression, and variable importance metrics (e.g., selectivity ratio, VIP scores).
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