Huang Cai-jin, Liu Xian, Yang Zeng-ling, Han Lu-jia
College of Engineering, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 May;29(5):1264-7.
Two hundred and twenty-two straw samples, consisting of 170 rice straw samples and 50 wheat straw samples, were collected from 24 provinces of China. Near infrared spectroscopy (NIRS)was applied to build quantitative models for calorific value of straw combining the use of principal component regression (PCR), partial least square regression (PLS)and modified partial least square regression (MPLS). Different scatter correction methods and derivative treatments were adopted to help improve the accuracy of NIRS models. A total of 54 NIRS models were obtained and independent validations were conducted using the same validation set of samples. A statistical comparison of independent validation results was then introduced to evaluate whether the models perform significantly. Bias and bias corrected standard error of prediction (SEP(C)), which are the mean and the standard deviation of the prediction residuals respectively, were compared by the proposed statistical procedures. It was concluded that near infrared spectroscopy was able to predict the calorific value of straw samples rapidly and accurately, with resulting SEP(C)s between 134 and 178 J x g(-1); statistical comparison of biases and SEP(C)s was a reasonable and efficient way to compare spectral pre-processing methods, and select NIRS models predicting calorific value of straw.
从中国24个省份收集了222份秸秆样本，其中包括170份稻草样本和50份麦秸样本。应用近红外光谱（NIRS）结合主成分回归（PCR）、偏最小二乘回归（PLS）和改进的偏最小二乘回归（MPLS）建立秸秆热值的定量模型。采用不同的散射校正方法和导数处理来提高NIRS模型的准确性。共获得54个NIRS模型，并使用相同的验证样本集进行独立验证。然后引入独立验证结果的统计比较，以评估模型是否具有显著性能。通过所提出的统计程序比较了偏差和偏差校正预测标准误差（SEP(C)），它们分别是预测残差的均值和标准差。结果表明，近红外光谱能够快速、准确地预测秸秆样本的热值，所得SEP(C)在134至178 J x g(-1)之间；偏差和SEP(C)的统计比较是比较光谱预处理方法和选择预测秸秆热值的NIRS模型的合理有效方法。
