农业工程学报
農業工程學報
농업공정학보
2013年
3期
258-264
,共7页
特征提取%相关法%识别%电子鼻检测%食醋
特徵提取%相關法%識彆%電子鼻檢測%食醋
특정제취%상관법%식별%전자비검측%식작
feature extraction%correlation methods%identification%electronic nose testing%vinegar
电子鼻检测中常用的特征鉴别能力评价方法有2种,一是对判别结果的直观分析,二是对判别正确率的统计计算.但是,当判别正确率相同时,对于不同特征间鉴别能力的差异,2种方法都不能进行准确的定量评价.为实现特征鉴别能力的准确度量,以不同种类食醋为检测对象,对检测信号提取面积斜率比、方差、积分、平均微分值、相对稳态平均值、小波能量等6种特征参量,并将特征参量与类别间的相关系数作为特征鉴别能力的度量指标.计算结果可知:面积斜率比特征参量的相关系数绝对值最小,为0.1027,积分特征参量的相关系数绝对值最大,为0.6455.表明面积斜率比特征参量的鉴别能力最低,积分特征参量的鉴别能力最高.Fisher 判别结果也证明了特征参量的鉴别能力越高,其分类效果越好.因此,用特征参量与类别间的相关系数作为特征鉴别能力的度量是合适的、也是有效的.
電子鼻檢測中常用的特徵鑒彆能力評價方法有2種,一是對判彆結果的直觀分析,二是對判彆正確率的統計計算.但是,噹判彆正確率相同時,對于不同特徵間鑒彆能力的差異,2種方法都不能進行準確的定量評價.為實現特徵鑒彆能力的準確度量,以不同種類食醋為檢測對象,對檢測信號提取麵積斜率比、方差、積分、平均微分值、相對穩態平均值、小波能量等6種特徵參量,併將特徵參量與類彆間的相關繫數作為特徵鑒彆能力的度量指標.計算結果可知:麵積斜率比特徵參量的相關繫數絕對值最小,為0.1027,積分特徵參量的相關繫數絕對值最大,為0.6455.錶明麵積斜率比特徵參量的鑒彆能力最低,積分特徵參量的鑒彆能力最高.Fisher 判彆結果也證明瞭特徵參量的鑒彆能力越高,其分類效果越好.因此,用特徵參量與類彆間的相關繫數作為特徵鑒彆能力的度量是閤適的、也是有效的.
전자비검측중상용적특정감별능력평개방법유2충,일시대판별결과적직관분석,이시대판별정학솔적통계계산.단시,당판별정학솔상동시,대우불동특정간감별능력적차이,2충방법도불능진행준학적정량평개.위실현특정감별능력적준학도량,이불동충류식작위검측대상,대검측신호제취면적사솔비、방차、적분、평균미분치、상대은태평균치、소파능량등6충특정삼량,병장특정삼량여유별간적상관계수작위특정감별능력적도량지표.계산결과가지:면적사솔비특정삼량적상관계수절대치최소,위0.1027,적분특정삼량적상관계수절대치최대,위0.6455.표명면적사솔비특정삼량적감별능력최저,적분특정삼량적감별능력최고.Fisher 판별결과야증명료특정삼량적감별능력월고,기분류효과월호.인차,용특정삼량여유별간적상관계수작위특정감별능력적도량시합괄적、야시유효적.
In a test of the electronic nose (E-nose), two evaluation methods of the feature vector identification ability are commonly used: 1) visual analysis for the discrimination result, 2) statistical computation for the correct rate of the discrimination result. However, when the correct rates of the discrimination result are same, for the different feature vectors, the identification ability can not be evaluated accurately and quantitatively by the above two methods. In order to achieve the precise evaluation of the feature vector identification ability, different kinds of vinegar were taken as the study object and tested by the E-nose. Six kinds of feature vectors including Variance (Var), Integral value (Inv), Average value in relative steady-state (Avrs), Value of area divided by the slope (Vads), Average differential value (Adv), and Wavelet energy value (Wev) were extracted from the acquisition data after removing the background signal. The correlation coefficient between feature vectors and categories was used as an evaluation index of the feature vector’s identification ability, the evaluation and comparison for the feature vectors is achieved by this index. Absolute values of the correlation coefficient between feature vectors and categories are respectively 0.2936, 0.6455, 0.6182, 0.1027, 0.6176 and 0.6189. Among them the absolute value of correlation coefficient was least between the ‘Vads’feature vector and the categories, and the absolute value of correlation coefficient was greatest between the ‘Inv’feature vector and the categories. These results show that the identification ability of the ‘Vads’feature vector is the lowest, and the identification ability of the ‘Inv’feature vector is the highest. The correct rate of the Fisher discrimination result was calculated, and the classification effect graph of the Fisher Discrimination was analyzed for every feature vector. The correct rate of the ‘Vads’feature vector was the lowest (39.2%). Its corresponding classification effect was the worst; all kinds of vinegar samples mixed, and the group centroids of three categories almost overlapped. The correct rates of the discrimination result were all 100%for the other feature vectors, so the comparison of feature vector identification ability can not be carried out only by the correct rates of discrimination. But the classification effect graphs of Fisher Discrimination for these feature vectors show that the clustering degree within and between the groups for the three categories of vinegar samples were very different, which indicates that there may still be differences among the feature vectors’ identification ability, even though their discrimination correct rates were all 100%. Specifically, the classification effect of feature vector ‘Inv’was the best, the clustering degree of samples within the groups was the highest, and the boundaries between groups were the most distinct. The discriminating results of Fisher Discrimination prove that the higher the feature vector identification ability is, the better the classification result is. This result is in accord with that of the absolute values of the correlation coefficient. Therefore, it is right and effective that the correlation coefficient between feature vectors and categories be used as an evaluation index of the feature vector identification ability. The proposed method will provide a new train of thought for studies of quantitative evaluation in the E-nose system.