目的 建立近红外快速预测片剂硬度的方法。方法 采用硬度仪获得片剂真实硬度,运用偏最小二乘回归法(PLSR)和反向人工神经网络(BP-ANN)法建立近红外光谱与硬度之间的校正模型。结果 偏最小二乘回归模型的相关系数r=0.977 8,内部交叉验证均方根误差(RMSECV)为0.742 kg,预测均方根误差(RMSEP)为0.815 kg;反向人工神经网络训练集、监控集和测试集的相关系数r分别为0.987 3、0.985 6、0.986 8,各数据集的相对标准偏差(RSE%)分别为6.83%、8.77%、6.69%。结论 反向人工神经网络非线性模型预测准确度要优于偏最小二乘回归线性模型。
Abstract
OBJECTIVE To establish a method for predicting tablet hardness by near infrared diffuse reflection spectroscopy. METHODS Tablet hardness value was obtained by hardness meter. Calibration model between NIR spectra and the hardness was establish by partial least squares regression (PLSR) method and BP-ANN method. RESULTS Correlation coefficients (r), root mean squares error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) obtained by PLSR model were 0.977 8, 0.742 and 0.815 kg respectively. And the correlation coefficients of training set, monitor set and testing set by BP-ANN were 0.987 3, 0.985 6, and 0.986 8, with RSE% of 6.83%, 8.77%, and 6.69%, respectively. CONCLUSION The prediction accuracy of BP-ANN nonlinear model is superior to the PLSR model
关键词
近红外漫反射光谱 /
硬度 /
反向人工神经网络 /
偏最小二乘回归法
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Key words
near infrared diffuse reflection spectrum /
hardness /
back-propagation artificial neural networks /
PLSR
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中图分类号：
R944
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参考文献
[1] LU W Z. Modern Near Infrared Spectroscopy Analytical Technology. 2nd Ed(现代近红外光谱分析技术. 第二版)[M]. Beijing:China Petrochemical Press,2007.
[2] MORISSEAU K M, RHODES C T. Near-infrared spectroscopy as a nondestructive alternative to conventional tablet hardness testing[J]. Pharm Res,1997,14(1):108-111.
[3] CHEN Y, THOSAR S S, FORBESS R A, et al. Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy[J]. Drug Dev Ind Pharm,2001,27(7):623-631.
[4] TANABE H, OTSUKA K, OTSUKA M. Theoretical analysis of tablet hardness prediction using chemoinformetric near-infrared spectroscopy[J]. Anal Sci,2007,23(7):857-862.
[5] LIU P, LIANG Y Z,ZHANG L,et al. Artificial neural networks applied in the analysis of chemical data(I)—approximation for trend and over-fitting [J]. Chem J Chin Univ(高等学校化学学报),1996,17(6):861-865.
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脚注
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基金
“十二五”重大新药创制专项(2012ZX09102201011);江西省科技厅科技计划专项(20151BBG70031);江西中医药大学科技计划(2013ZR0076)
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