The correlation coefficients (r) and root mean square error of prediction (RMSEP) were used as the model evaluation indices.
以预测集的预测相关系数(r)，预测标准偏差(RMSEP)作为模型评价指标。
The correlation and RMSEP of themodel using selected 11 wavelengths to reduce the calculation of modeling is 0.95and 0.68%, respectively.
为了减少建模计算工作量，利用蛋白质在近红外光谱区的11个特征吸收峰进行了波长优选，建模的相关系数为0.95，RMSEP为0.68%。
Result:The coefficients of correlation of inner cross validation and external validation are both above 0.90, and the RMSECV and RMSEP are both below 0.05.
结果：所建的5个模型对验证集样品水分含量的预测值与实测值的相关系数均在0.90以上，预测误差均方根（RMSEP）均在0.05以下。
The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model.
最终预测结果为两个模型预测值之和， 以模型的预测标准偏差（RMSEP）作为评价指标， 以便考察新方法的有效性。
The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model.
最终预测结果为两个模型预测值之和， 以模型的预测标准偏差（RMSEP）作为评价指标， 以便考察新方法的有效性。
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