样品温度变化会对模型预测结果产生影响, 为解决这个问题, 首先, 对同一样品不同温度下的光谱与同一样品相同温度的光谱进行了比较。 结果显示, 不同温度下的光谱差异较大。 然后研究了样品温度对玉米粗蛋白模型的预测结果的影响, 对随机选取的粗蛋白含量为6.04%的样品不同温度采集光谱, 对这些光谱进行预处理消除温度之外的因素对光谱的影响, 将预处理后的光谱代入已建立好的模型中进行预测, 结果显示, 预测结果与实测值之间的差别随着光谱温度与建模温度相之间差别的增大而增大, 最大的误差为1.12%。 为了解决温度对模型预测结果的影响, 进而分析了温度与不同温度下的光谱数据之间的关系, 发现在去除了光谱两端噪声较严重的区域后, 不同温度下, 同一波长点处的光谱数据之间存在一定的线性关系。 依据这一发现, 文中提出了温度修正理论, 以建模时的光谱为基准光谱, 然后根据温度与光谱之间的线性关系使用线性回归算法对不同波长点的光谱进行一元线性回归, 求出不同温度下的光谱与基准光谱之间的差, 最后将不同温度下的光谱校正为基准光谱, 通过该理论对光谱进行校正之后, 不同温度下的同一样品的光谱之间的差别和修正之前相比已经有了很大改善, 将修正后的光谱代入模型, 大部分预测结果得到了提高, 符合国家标准±0.5%以下的要求。 最后使用和建模无关的34个不同含量的样品对该温度修正理论进行验证, 光谱修正前后粗蛋白的模型预测值与标准理化值决定系数分别为0.910和0.982, 均方根误差分别为0.558和0.172, 平均相对误差分别为6.05%和1.75%。 该温度修正理论从近红外光谱分析的本质上进行了温度修正, 为其他样品的温度修正提供了参考, 有利于手持式近红外光谱仪使用的推广。

For the problem that the temperature change of the sample affects the prediction result of the model, firstly, the spectrum of the same sample at different temperatures is compared with the spectrum of the same sample at the same temperature. The results show that the spectral difference at different temperatures is large. Then the effect of sample temperature on the prediction of corn crude protein model was studied. Spectral collection of samples with a crude protein content of 6.04% was performed at different temperatures, and near-infrared spectra at different temperatures were pretreated in the same way as those used in modeling, so as to eliminate the influence of factors other than the temperature on the spectra. The pre-processed spectrum is substituted into the established model for prediction. The prediction results show that the difference between the predicted result and the measured value increases as the difference between the spectral temperature and the modeled temperature increases, and the maximum error is 1.12%. In order to solve the influence of temperature on the prediction results of the model, we further analyzed the relationship between temperature and spectral data at different temperatures, and found that after removing the areas with serious noise at both ends of the spectrum, there was a certain linear relationship between spectral data at the same wavelength point at different temperatures. According to this finding, a temperature correction theory is proposed. Taking the spectrum at the time of modeling as the reference spectrum, and then using the linear regression algorithm to perform linear regression on the spectra of different wavelength points according to the linear relationship between temperature and spectrum, the difference between the spectrum at different temperatures and the reference spectrum is obtained. Finally, the spectra at different temperatures are corrected to the reference spectrum. After the spectrum is corrected by the theory, the difference between the spectra has been greatly improved compared with before the correction. The corrected spectrum is brought into the model, and most of the prediction results are improved, which meets the requirements of ±0.5% of the national standard. Finally, the temperature correction theory was verified by using 34 different samples unrelated to the modeling. The model prediction values and standard physical and chemical value determination coefficients of the crude protein before and after the spectral correction were 0.910 and 0.982, respectively, and the root means square error was 0.558 and 0.172, and the average relative error was 6.05% and 1.75%, respectively. The temperature correction theory has been temperature-corrected from the nature of near-infrared spectroscopy, providing a reference for temperature correction of other samples, which is beneficial to the promotion of handheld near-infrared spectroscopy.