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星光小草金虫 (小有名气)
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[求助]
偏最小二乘法 近红外光谱
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1、These methods with original and vector normalised spectra were used to develop calibration models。 2、The performance of the final PLS model was evaluated in terms of root mean square error of cross validation (RMSECV) for cross validation and root mean square error of prediction (RMSEP) during test validation, and the coefficient of determination (R2). 3、The residual (Res) is the difference between the true and fitted value. Thus the sum of squared errors (SSE) is the quadratic summation of these values ( Eq.1 ). 4、The root mean square error of estimation (RMSEE) is calculated from this sum, with “n” being the number of samples and “r” the rank ( Eq.2 ). 5、The determination coefficient, R2 ( Eq.3 ) gives the percentage of variance present in the true component values, which is reproduced in the regression. 6、R2 can be negative for low ranks, when the residual are larger than the variance in the true values (yi). In case of cross validation, the RMSECV is calculated using Eq.4 . 7、For the prediction set, the root mean square error of prediction (RMSEP) is calculated as follows ( Eq.5 ) [13]. 8、The absorption peaks of NIR spectra were broad and overlap, making single wavelength calibration impossible due to large hidden information in spectral data. 9、 As a form of principal component analysis (PCA), PLS made use of the information of the NIR spectrum and the established analyte values as sociated with the spectrum. 10、It had no restriction in using the number of wavelengths that could be selected for the calibration to make the model suitable to extract the maximum information from the spectra. 11、The information extracted could be condensed in the latent variables or factors which were used in the calibration and prediction steps [12]. |
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星光小草: 金币+5, 翻译EPI+1, ★★★很有帮助 2013-11-29 15:37:18
星光小草: 金币+5, 翻译EPI+1, ★★★很有帮助 2013-11-29 15:37:18
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1、这些方法和原矢量归一化光谱都被用于校正模型。 2、最后的PLS模型的性能评价主要在于根的交叉验证均方误差（RMSECV）的交叉验证和根平均平方预测误差（RMSEP）的试验验证和决定系数（R2）的测量。 3、残余（RES）是真正的拟合值之间的差异。因此，误差平方和（SSE）是这些值的平方的总和（1）。 4、均方根误差估计（RMSEE）是按如下这个方法计算：“N”为样本的数量和“R”的等级（水利）。 5、决定系数R2（式），给出了在真正的元件值存在差异的百分比，这是在回归重现（不太清楚后半句意思）。 6、，当剩余大于真实值的方差（一），R2可以是负的低等级。在交叉验证的情况下，预测计算Eq.4。 |
2楼2013-11-29 08:51:18