Liang Liang, Liu Zhi-Xiao, Pan Shi-Cheng, Zhang Xue-Yan, Bai Zhen-Qing, Wang Cheng-Hua, Yang Min-Hua
School of Info-Physics and Geomatics Engineering, Central South University, Changsha 410083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Jul;29(7):1772-6.
A new method was put forward to diagnose chronic enteritis of alpine musk deer (Moschus chrysogaster) by visible-near infrared reflectance spectra of feces. A total of 125 feces samples, including 70 samples from healthy individuals (healthy samples) and 55 samples from chronic enteritis sufferers (diseased samples), were collected in Xinglongshan musk deer farm, Gansu province. The spectral scan was carried out in the darkroom (temperature 18 degrees C-22 degrees C, humidity 22%-25% and halogen lamp as a sole light source) with an ASD FieldSpec 3 spectrometer. All the samples were divided randomly into two groups, one with 95 samples as the calibration set, and another with 30 samples as the validation set. The samples data were pretreated by the methods of S. Golay smoothing and first derivative. The pretreated spectra were analyzed by principal component analysis (PCA), and the top 6 principal components, which were computed by PCA and accounted for 95.16% variation of the original spectral information, were used for modeling as the new variables. The data of the calibration set were used to build models for diagnosing the chronic enteritis of alpine musk deer by means of back-propagation artificial neural network (ANN-BP), fuzzy pattern recognition, Fisher linear discriminant and Bayes stepwise discriminant, respectively. The predicted outcomes of the 30 unknown samples in validation set showed that the accuracy was 86.7% by themethod of Fisher linear discriminant, 90% by fuzzy pattern recognition and ANN-BP model, and 93.3% by stepwise discrimination. Further analysis found that all misdiagnosed samples were derived from the healthy samples, which were treated as disease samples, and the detection rates of diseased samples were 100% by the four different methods. The results indicated that it was feasible to diagnose the chronic enteritis of alpine musk deer by visible-near infrared reflectance spectra of feces as a rapid and non-contact way, and the PCA combined with Bayes stepwise discriminant was a preferred method.
提出了一种利用粪便的可见 - 近红外反射光谱诊断马麝（Moschus chrysogaster）慢性肠炎的新方法。在甘肃省兴隆山麝鹿养殖场收集了125份粪便样本，其中包括70份健康个体的样本（健康样本）和55份慢性肠炎患者的样本（患病样本）。在暗室（温度18摄氏度 - 22摄氏度，湿度22% - 25%，唯一光源为卤素灯）中使用ASD FieldSpec 3光谱仪进行光谱扫描。所有样本随机分为两组，一组95个样本作为校正集，另一组30个样本作为验证集。样本数据采用Savitzky - Golay平滑和一阶导数方法进行预处理。对预处理后的光谱进行主成分分析（PCA），取PCA计算得到的前6个主成分，其占原始光谱信息变异的95.16%，作为新变量用于建模。分别利用反向传播人工神经网络（ANN - BP）、模糊模式识别、Fisher线性判别和贝叶斯逐步判别方法，以校正集数据建立诊断马麝慢性肠炎的模型。验证集中30个未知样本的预测结果表明，Fisher线性判别法的准确率为86.7%，模糊模式识别和ANN - BP模型的准确率为90%，逐步判别法的准确率为93.3%。进一步分析发现，所有误诊样本均来自被误判为患病样本的健康样本，四种不同方法对患病样本的检出率均为100%。结果表明，利用粪便的可见 - 近红外反射光谱作为一种快速、非接触的方式诊断马麝慢性肠炎是可行的，且PCA结合贝叶斯逐步判别是一种首选方法。
