Diagnosis of Chronic Enteritis of Alpine Musk Deer (Moschus Chrysogaster) Based on Visible-Near Infrared Reflectance Spectra of Feces
LIANG Liang1, 2, LIU Zhi-xiao1, 2*, PAN Shi-cheng3, ZHANG Xue-yan3, BAI Zhen-qing3, WANG Cheng-hua2, YANG Min-hua1
1. School of Info-Physics and Geomatics Engineering, Central South University, Changsha 410083, China 2. College of Biology and Environmental Sciences, Jishou University, Jishou 416000, China 3. Administration of Xinglong Mountain National Nature Reserve, Yuzhong 730117, China
Abstract：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 ℃-22 ℃, 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 the method 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.
