A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples
作者： Wensheng Cai , Zhenqiang Su , Weida Tong , Leming Shi , Xueguang Shao
DOI: 10.1080/00032710600724088
关键词:
摘要: A consensus regression approach based on partial least square (PLS) regression, named as cPLS, for calibrating the NIR data was investigated. In this approach, multiple independent PLS models were developed and integrated into a single model. The utility merits of cPLS method demonstrated by comparing its results with those from regular in predicting moisture, oil, protein, starch contents corn samples using spectral data. It found that superior to respect prediction accuracy robustness.
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