将近红外光谱技术和化学计量学相结合分析慈竹纤维素结晶度.通过区间偏最小二乘法(iPLS)、联合区间偏最小二乘法(siPLS)和反向区间偏最小二乘法(biPLS)优化建模区域,建立经多元散射校正后光谱的结晶度分析模型,并与全光谱范围350～2 500 nm建立的偏最小二乘(PLS)模型进行比较.结果表明,三种改进偏最小二乘法建立的结晶度模型预测效果均优于 PLS 模型,并且当采用联合区间偏最小二乘法将全光谱进行30个子区间划分,选择三个子区间[8 12 19]组合时,建立的 siPLS 模型预测效果最好,相关系数(r)达到0.88.预测标准差(RMSEP)为0.0117.因此,采用联合区间偏最小二乘法可以有效选择建模光谱区域,提高模型预测能力,实现慈竹纤维素结晶度的快速预测.%Near infrared spectroscopy technique combined with chemometrics methods was applied to predict crystallinity of Neosinocalamus affinins. Three improved partial least squares (PIS) methods, including interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS), were used to find the most informative ranges and build models with better predictive quality based on multiplicative scatter correction spectra. And then the models were compared with PLS model which was developed on the whole wavelength range 350～2 500 nm. The results showed that the models built by the three improved PLS methods had higher predictive ability than that of PIS model, and the optimal model was obtained by siPLS method that separated the whole spectra into 30 intervals and combined three intervals. The siPLS model had correlation coefficient (R) of 0. 88 and root mean standard error of prediction (RMSEP) of 0. 011 7. Therefore,through selecting the effective wavelength range, siPLS method could accurately and rapidly predict crystallinity in Neosinocalataus affinins.
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