为了简化近红外光谱模型，提高对草莓可溶性固形物含量的预测精度，将反向偏最小二乘法(BiPLS)与模拟退火算法(Simulated annealing algorithm，SAA)相结合优选特征波长，建立了多元线性回归可溶性固形物光谱模型.原始光谱经过预处理后，用反向偏最小二乘法优选出4个特征子区间(分别为第8、13、16、17)；对所选的特征子区间，进一步用模拟退火算法选择可溶性固形物的特征波长.在SAA选择出的 7565 cm-1、7706 cm-1、8289 cm-1、8489 cm-1、8499 cm-1、8724 cm-1、8807 cm-1 7个特征波数点的基础上建立了预测模型.模型的预测均方根误差为0.428，优于偏最小二乘法、向后区间偏最小二乘法建模结果.研究结果表明： 反向偏最小二乘法结合模拟退火算法可以有效选择近红外光谱特征波长.

To improve and simplify the NIR prediction model of the soluble solid content (SSC) of strawberry, backward interval partial least squares (BiPLS) and simulated annealing algorithm (SAA) were combined to select the efficient wavelengths. The strawberry spectra were divided into 21 intervals, among which 4 subsets, i.e. No.8, 13, 16 and 17 were selected by BiPLS. Then SAA was used to select variables in these informative regions, which were used for regression variables of MLR model. Finally, 7565 cm-1, 7706 cm-1, 8289 cm-1, 8489 cm-1, 8499 cm-1, 8724 cm-1 and 8807 cm-1 were used to build a MLR model. The MLR model performs well with root mean standard error of prediction (RMSEP) of 0.428 for SSC, which out performs models using PLS and BiPLS. This work proved that the BiPLS-SAA could determine optimal variables in NIR spectra and improve the accuracy of model.