Key Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, & Hubei University of Chinese Medicine, Wuhan 430065, China.
Key Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, & Hubei University of Chinese Medicine, Wuhan 430065, China; Nanzhang People's Hospital, Xiangyang, Hubei 441500, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Mar 15;193:133-140. doi: 10.1016/j.saa.2017.12.019. Epub 2017 Dec 5.
Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4cm), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9cm). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of determination (R) of 95.75% in internal cross and relative mean square error of prediction (RMSEP) of 1.98%, R of 97.94% and ratio of performance to deviation (RPD) of 6.11 in external validation.
近红外（NIR）光谱分析技术在中药分析领域得到了广泛应用。该技术具有分析速度快、样品无损、无污染等优点。本研究旨在建立一种矿物药炉甘石中氧化锌（ZnO）含量的近红外光谱快速定量分析模型。采集了 57 批炉甘石样品的近红外光谱，采用一阶导数（FD）方法对光谱进行预处理。采用乙二胺四乙酸（EDTA）滴定法测定炉甘石样品中 ZnO 的含量，并将其作为近红外光谱的参考值。采用 Kennard-Stone（K-S）算法将 57 批炉甘石样品分为校正集和预测集。首先，在校正集中，计算每个波数处对应样品的吸光度值与 ZnO 含量的相关系数（r）。然后，根据平方相关系数（r）值，得到前 50 个波数组成特征光谱带（4081.8-4096.3、4188.9-4274.7、4335.4、4763.6、4794.4-4802.1、4809.9、4817.6-4875.4cm），采用反向传播人工神经网络（BP-ANN）算法建立 ZnO 含量的定量模型。然后，对 50 个波数进行均值影响值（MIV）算法操作，选择 MIV 绝对值大于或等于 25 的波数，得到最佳特征光谱带（4875.4-4836.9、4223.6-4080.9cm）。然后，采用内部交叉验证和外部验证筛选 BP-ANN 的隐藏层节点数。最终，选择隐藏层节点数 4 作为最佳。最后，发现 BP-ANN 模型对 42.05-69.98%范围内的炉甘石样品 ZnO 含量分析具有较高的准确性和较强的预测能力，内部交叉验证的相对均方根误差（RMSECV）为 1.66%，决定系数（R）为 95.75%；外部预测的相对均方根误差（RMSEP）为 1.98%，R 为 97.94%，表现与偏差比（RPD）为 6.11。
