针对近红外光谱数据的特点，分析了基于偏最小二乘法(PLS)回归系数的波长选择方法，指出了其存在的问题，提出了一种新的波长选择算法。将PLS回归系数归一化为对应波长被选择的概率，并进行蒙特卡罗(Monte-Carlo)计算，即对不同的随机波长组合建立一系列PLS模型，预测误差最小的模型所对应的波长组合将被选择。这个过程可以在前一次波长选择的基础上重复进行，从而形成迭代算法。采用三个近红外数据集对提出的算法进行了验证，同时与基于PLS的无信息波长剔除法(UVE-PLS)和遗传算法(GA)进行了比较分析。实验结果表明，该方法能有效地提高波长选择的准确性和稳定性，在选择的波长点个数、模型的复杂度与预测误差方面，达到甚至优于现有算法，具有实用价值。

Based on the feature of the (NIR) spectra, this paper analyses the method of wavelength selection using the partial least squaresc (PLS) regression coefficients and points out the existing problems, then proposes a new method for selecting wavelengths. It normalizes the PLS regression coefficients into the probability of the selected corresponding wavelengths, then a Monte-Carlo simulation based on the aforementioned probability is calculated. Some PLS models are constructed and evaluated using different random wavelengths combinations. The model with minimum predictive error is retained and the corresponding wavelength combinations are selected. This procedure can be iterated using the previous selected wavelengths to select fewer and fewer wavelengths. This method is tested on 3 NIR datasets and compared with the PLS-based uninformative variable elimination (UVE-PLS) and genetic algorithm (GA). Experimental results show that this method could select fewer wavelengths without sacrificing the complexity and predictive ability of the PLS model and could effectively improve the accuracy and stability of the wavelength selection.