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Statistical comparison of dog and cat guard hairs using numerical morphology.

机译:使用数字形态对狗和猫护卫毛进行统计比较。

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摘要

Numerical features obtained from the guard hairs of dogs and cats (total 300 hairs per dog or cat) were statistically compared, in an attempt to discriminate between them. Using hairs from each of five mongrel dogs and cats, eight measurements (length (Len), maximum width (MaxWid), cross sectional maximum diameter, cross sectional minimum diameter, cuticular thickness of the cross section and three scale counts per 100 microm length (observed at three positions: distal third (disSC), middle (midSC) and the proximal third (proSC) portions) and five indexes (hair width index (HWI), medulla index (MI), hair index, cuticle index and the difference in scale counts between the distal and proximal parts (defSC)) were examined. The range for each numerical feature overlapped each other extensively, and none of the features permitted a discrimination between dog and cat hairs, based on the values obtained. However, 12 numerical features, except for the midSC, showed a statistically significant difference between dog and cat hairs, as evidenced by a t-test. For the purpose of comprehensively comparing numerical features and statistically discriminating between dog and cat hairs, a discriminant analysis between the two were carried out using a multiple regression analysis. Four types of discriminant functions produced by combining over five numerical features were examined. Dog and cat hairs could clearly be discriminated using any of the discriminant functions. Species discrimination using the discriminant function permitted the species of a dog or cat to be determined, based on the overall morphologies of various numerical features. When experimentally collected test samples were investigated using the discriminant function using Combination-2, consisting of eight numerical features (Len, MaxWid, MI, HWI, disSC, midSC, proSC and defSC), all 10 cat hairs were correctly determined to be cat hair and 22 of 23 dog hairs were correctly identified. This discriminant function produced good results for species discrimination between dog and cat hairs.
机译:从狗和猫的保护毛获得的数值特征(每只狗或猫总共300根毛发)进行了统计比较,以试图区分它们。使用五只杂种猫和猫中每只的毛发,进行八次测量(长度(Len),最大宽度(MaxWid),横截面最大直径,横截面最小直径,横截面表皮厚度和每100微米长度的三个刻度计数(在三个位置进行观察:远端的第三(disSC),中间的(midSC)和近端的第三(proSC))和五个指数(头发宽度指数(HWI),延髓指数(MI),头发指数,角质层指数和检查了远端部分和近端部分之间的比例计数(defSC),每个数字特征的范围都相互重叠,并且根据获得的值,没有一个特征允许区分狗和猫毛。通过t检验,除midSC以外的其他特征在狗和猫毛之间显示出统计学上的显着差异,目的是全面比较数字特征并在统计学上区分狗对于猫毛和猫毛,使用多元回归分析对两者进行判别分析。检查了通过组合五个以上的数字特征而产生的四种判别函数。可以使用任何判别函数明显区分狗和猫的毛发。使用判别函数进行物种区分可以根据各种数字特征的总体形态来确定狗或猫的物种。当使用判别函数使用组合2对实验收集的样本进行调查时,该函数由8个数字特征(Len,MaxWid,MI,HWI,disSC,midSC,proSC和defSC)组成,所有10只猫毛都正确地确定为猫毛在23条狗毛中有22条被正确识别。这种区分功能为狗和猫毛之间的物种区分产生了良好的结果。

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