CAJ | 학술논문

对60种植物类中药提取物的红外光谱药性特征标记及其模式识别模型进行评价筛选。利用傅里叶变换红外光谱结合（linear discriminant analysis ，LDA ），（logistic discriminant analysis ，Logistic-DA ），（prin-cipal component analysis-linear discriminant analysis ，PCA-LDA ），（partial least-squares discriminant analysis ， PLS-DA），（random forest ，RF），（support vector machine ，SVM）六种模式识别技术进行研究。水提取组采用加热回流提取1.5 h ，无水乙醇、氯仿、石油醚提取组采用室温超声提取45 min。首先分别建立六种模式识别模型，然后采用四种统计方法综合识别，包括60味中药组内回代、60味中药10次迭代5折交叉验证、48味中药训练集、12味中药测试集。选取组内回代识别正确率、交叉验证识别正确率、组外预测正确率同时很高，且理论图谱反映寒热中药原始图谱分布特征者为适宜模型。LDA 和SVM 是水提取物红外光谱的适宜模式识别模型，LDA是无水乙醇提取物红外光谱的适宜模式识别模型，SVM是氯仿提取物红外光谱的适宜模式识别模型，石油醚提取识别效果不佳。结论：根据适宜识别模型，通过红外光谱数据可识别表征中药寒热成分和寒热程度的特征参数，寒热成分特征参数为与红外光谱吸收位置波谱相对应的识别模型的识别系数，识别系数大于零为寒性标记，识别系数小于零为热性标记；寒热程度特征参数为识别模型的识别得分，得分越大（正值）则寒性越强，得分越小（负值）则热性越强。
대60충식물류중약제취물적홍외광보약성특정표기급기모식식별모형진행평개사선。이용부리협변환홍외광보결합（linear discriminant analysis ，LDA ），（logistic discriminant analysis ，Logistic-DA ），（prin-cipal component analysis-linear discriminant analysis ，PCA-LDA ），（partial least-squares discriminant analysis ， PLS-DA），（random forest ，RF），（support vector machine ，SVM）륙충모식식별기술진행연구。수제취조채용가열회류제취1.5 h ，무수을순、록방、석유미제취조채용실온초성제취45 min。수선분별건립륙충모식식별모형，연후채용사충통계방법종합식별，포괄60미중약조내회대、60미중약10차질대5절교차험증、48미중약훈련집、12미중약측시집。선취조내회대식별정학솔、교차험증식별정학솔、조외예측정학솔동시흔고，차이론도보반영한열중약원시도보분포특정자위괄의모형。LDA 화SVM 시수제취물홍외광보적괄의모식식별모형，LDA시무수을순제취물홍외광보적괄의모식식별모형，SVM시록방제취물홍외광보적괄의모식식별모형，석유미제취식별효과불가。결론：근거괄의식별모형，통과홍외광보수거가식별표정중약한열성분화한열정도적특정삼수，한열성분특정삼수위여홍외광보흡수위치파보상대응적식별모형적식별계수，식별계수대우령위한성표기，식별계수소우령위열성표기；한열정도특정삼수위식별모형적식별득분，득분월대（정치）칙한성월강，득분월소（부치）칙열성월강。
By using the Fourier transform infrared spectroscopy and linear discriminant analysis (LDA ) ,logistic discriminant a-nalysis(Logistic-DA) ,principal component analysis-linear discriminant analysis(PCA-LDA) ,partial least-squares discriminant analysis(PLS-DA) ,random forest(RF) ,support vector machine(SVM) ,infrared spectra of 60 kinds of plant extract of Chinese traditional medicine were analyzed and the identification and evaluation of characteristics of the regional markers associated with cold and heat nature were studied .Results indicated that LDA and SVM are suitable for the recognition model of water extract infrared spectral data ,LDA is suitable for the identification model of anhydrous ethanol extract infrared spectral data ,SVM is suitable for the identification model of chloroform extract infrared spectral data ,while petroleum ether extract group recognition effect is not ideal .According to the suitable characteristic parameters identification model ,data were analyzed by infrared spec-troscopy ,and parameters and resistance characteristics of the traditional Chinese drug composition can be obtained .Regional characteristics of these two parameters can be used to identify drug ingredients ,and can also be used to indicate different degrees of resistance characteristics of traditional Chinese medicine .Component parameter is model identification coefficient correspond-ing to the position of spectrum and infrared ,with a value greater than zero it is cold nature marker ,while with a value less than zero it is heat nature marker ;model identification score is a parameter reflecting the degree of cold and heat nature ,the greater the score (positive) ,the more it is cold ,while the smaller the score ,the more it is hot .a parameter reflecting the degree of cold and heat ,the greater the score (positive) is cold more strong ,the score is small (negative) heat stronger .