目的 采用在线近红外光谱（NIRS）技术，建立桂枝茯苓胶囊流化床干燥过程水分实时监测模型。方法 通过NIRS漫反射探头采集16个生产批次共176个样本进行建模，优选移动窗口平滑法进行光谱预处理，采用间隔偏最小二乘法（siPLS）结合移动窗口偏最小二乘法（mwPLS）筛选特征变量为4 759.45~5 338.00 cm^-1、5 503.84~6 101.67 cm^-1、8 512.25~8 809.24 cm^-1，采用偏最小二乘（PLS）法建立水分含量多变量校正模型。结果 水分预测的交叉验证均方根误差（RMSECV）为0.243%，性能偏差比（RPD）值为13.384，预测相对偏差（RSEP）为0.270%。以8个生产批次对在线监控方法的可靠性进行持续验证，结果40个样本的相对预测误差均小于4.7%。干燥过程水分实时监测趋势图显示可准确判断干燥终点，终点样本水分含量位于控制限内。结论 在线NIRS结合PLS建立的定量模型，可应用于生产规模桂枝茯苓胶囊流化床干燥过程水分含量在线监控且预测性能稳健、准确。

Objective To establish a real-time moisture monitoring model for the fluidized bed drying process of Guizhi Fuling Capsules (GFC) by using online near-infrared spectroscopy (NIRS). Methods A total of 176 samples from 16 production batches were collected by NIRS diffuse reflection probe for modeling. The moving window average smoothing method was used for spectral preprocessing. The characteristic variables were 4 759.45-5 338.00 cm^-1, 5 503.84-6 101.67 cm^-1, and 8 512.25-8 809.24 cm^-1, which were screened by the interval partial least squares method (siPLS) combined with the moving window partial least squares (mwPLS). The partial variable least squares (PLS) method was used to build a multivariate correction model for moisture. Results The root mean square error of cross-validation (RMSECV) of predicted moisture was 0.243%, the ratio of predicton to deviation (RPD) was 13.384, and the relative standard error of prediction (RSEP) was 0.270%. The reliability of the online monitoring method was continuously verified by eight production batches. The relative error of 40 samples was less than 4.7%, indicating that the PLS quantitative model prediction performance was robust and accurate. The real-time monitoring trend chart of the moisture in the drying process can accurately determine the drying end point, and the moisture content of the end sample was within the control limit. Conclusion The quantitative model established by online NIRS combined with PLS can be applied to the on-line monitoring of moisture content in the fluidized bed drying process of production scale GFC and the prediction performance was robust and accurate.

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