A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples
A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples
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DOI：
10.1080/00032710600724088
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发表时间：
2006-06
影响因子：
2
通讯作者：
Z. Su;W. Tong;Leming Shi;X. Shao;W. Cai
中科院分区：
文献类型：
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作者：
Z. Su;W. Tong;Leming Shi;X. Shao;W. Cai
摘要研究了一种基于偏最小二乘回归的一致性回归方法(CPLS)用于校正近红外光谱数据。在该方法中，多个独立的偏最小二乘模型被开发并集成到单个共识模型中。通过与常规偏最小二乘法对玉米样品水分、脂肪、蛋白质和淀粉含量近红外光谱数据预测结果的比较，验证了该方法的实用性和优越性。研究发现，在预测精度和稳健性方面，CPLS优于常规的PLS。
Abstract 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 consensus model. The utility and merits of the cPLS method were demonstrated by comparing its results with those from a regular PLS method in predicting moisture, oil, protein, and starch contents of corn samples using the NIR spectral data. It was found that cPLS was superior to regular PLS with respect to prediction accuracy and robustness.