Principal component analysis is widely applied to the multivariate calibration.

主成分分析是全光谱分光光度分析中常用的校正方法.

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The influence of pathlength selection on prediction accuracy of univariate calibration and multivariate calibration is investigated.

研究了光程长的选择对于单变量光谱分析和多变量光谱分析的测量精度的影响.

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Owing to good nonlinear multivariate calibration nature of ANN, the predicted results was reliable.

由于人工神经网络好的非线性的多变量校正特点, 预测结果是准确的.

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The chemometric algorithms of multivariate calibration and resolution developed recently are overviewed based on this.

在此基础上,综述了目前发展迅猛的多元校正与多元分辨的化学计量学算法.

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