Kong Qing-ming, Su Zhong-bin, Shen Wei-zheng, Zhang Bing-fang, Wang Jian-bo, Ji Nan, Ge Hui-fang
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 May;35(5):1233-8.
The whole spectrum usually contains a lot of redundant information in the near-infrared spectroscopy model, the presence of redundant information will increase the model resolution time and increase the difficulty of parsing model, Therefore, how to select the characteristic wavelength quickly and effectly is very crucial. In this paper, we combined the algorithm based on SPA (successive projections algorithm ) with IPLS (interval partial least squares ) to selec the characteristic wavelength in the fermentation of wheat straw microbial biomass, A total of 85 samples prepared by measuring microbial biomass using glucosamine method, 68 samples are chosen as calibration set and 17 simples are chosen as verification set. First, the whole spectral region 520 points are segmented modeling according to the interval wavelength point size 10, 20, 30, 40 and 4 4504 925 cm-1, 9 1949 993 cm-1 two-band range are selected as the characteristic wavelength band, then pick out the new feature wavelength points by Successive Projections Algorithm band and Genetic Algorithm (GA), comprehensive analysis and comparison the result of model. The experimental results show that the using of IPLS-SPA algorithm to select the combination band 4 4504 925 cm-1 & 9 1949 993 cm-1 has the best modeling effect, compared with the modeling of whole spectrum, the wavelength points decrease from 520 to 10, the correction coefficient of determination R2 rised from 0. 884 9 to 0. 945 28, root mean square error (RMSE) dropped from 11. 104 9 to 8. 203 3, although the genetic algorithm model achieved the better accuracy, but the results are instable and have a strong randomness , while IPLS combined SPA method can select characteristic wavelength information stability and accurately, which can improve the model calculation speed and reduce the fitting difficulty of the model, it can be used as a new reference method for band selection. The results show that using near infrared spectroscopy method for straw biomass rapid detection is feasible.
在近红外光谱模型中，整个光谱通常包含大量冗余信息，冗余信息的存在会增加模型解析时间并加大模型解析难度，因此，如何快速有效地选择特征波长至关重要。本文将基于SPA（连续投影算法）的算法与IPLS（区间偏最小二乘法）相结合，用于选择小麦秸秆微生物生物量发酵中的特征波长。使用氨基葡萄糖法测量微生物生物量共制备了85个样本，其中68个样本作为校正集，17个样本作为验证集。首先，根据区间波长点数10、20、30、40，将整个光谱区域520个点进行分段建模，选取44504925cm-1、91949993cm-1两个波段范围作为特征波长带，然后通过连续投影算法带和遗传算法（GA）挑选出新的特征波长点，综合分析并比较模型结果。实验结果表明，使用IPLS-SPA算法选择44504925cm-1和91949993cm-1的组合波段具有最佳建模效果，与全光谱建模相比，波长点数从520减少到10，决定系数校正系数R2从0.8849提高到0.94528，均方根误差（RMSE）从11.1049降至8.2033，虽然遗传算法模型精度更高，但结果不稳定且随机性强，而IPLS结合SPA方法能够稳定、准确地选择特征波长信息，可提高模型计算速度并降低模型拟合难度，可作为波段选择的一种新的参考方法。结果表明，利用近红外光谱法对秸秆生物量进行快速检测是可行的。
