在近红外光谱定量分析中, 由近红外光谱数据的高维特点引起的复共线性, 是利用化学计量方法建模经常遇到的问题, 可利用一种新的逆回归方法处理这一问题。 该方法首先利用自变量X和因变量Y的相关性质降维, 找到真正与回归函数有关的X的线性组合, 再利用新找到的低维变量估计回归函数。 文中以103个玉米样品为实验材料, 随机选择其中70个玉米样品中蛋白质含量的近红外光谱建立定量分析模型, 随后利用该模型预测剩余33个样品的蛋白质含量, 预测值和化学测量值的相关系数(R)为0.986, 平均相对误差为2.1%, 而偏最小二乘回归建模得到的对应结果分别为0.978和2.5%, 结果表明新方法比偏最小二乘有更好的表现。 为进一步考察该方法建模的稳定性, 对样品进行了6次随机分组, 并分别用逆回归方法和偏最小二乘法建模、 预测并计算预测值与化学测量值的相关系数和相对误差, 结果显示六次预测的结果表现出较好的稳定性, 并且都优于或至少与偏最小二乘方法相近。 逆回归方法利用自变量X和因变量Y的相关性质寻找与回归函数真正有关的自变量组合, 为建立近红外光谱定量分析校正模型提供了一种新的思路。

In the present paper, an inverse regression method is used in near infrared (NIR) spectroscopy analysis to reduce dimension of predictor at first, then estimate linear regression function using the new derived low dimensional data. A real data set of 103 corn samples was used for analysis with this new inverse regression method. Taking 103 corn samples as experiment materials, seventy samples were chosen randomly to establish predicting model, the remaining thirty-three corn samples were viewed as prediction set. The new derived model is used to the prediction set. The coefficient is 0.986 and the average relative error is 2.1% between the model predication results and Kjeldahl’s value for the protein content, and the results of using partial least square regression are 0.978 and 2.5%, respectively. The results demonstrate that the inverse regression method is feasible and has good property in near-infrared spectroscopy quantitative analysis, and also provides a new idea for chemometrics quantitative analysis.