green tea , unfermented and made from the leaves of the camellia sinensis plant , is one of the most popular beverages consumed across the world [ 13 ] . the property and chemical components of green teas are influenced by many factors , such as tea species , harvest season , climate , geographical locations , and processing . in china , among various factors , the geographical origin is recognized as an important aspect of tea . because of the similar tea species , cultivation and processing conditions in a specific tea - producing area , many teas are named after their geographical origins . longjing tea is a green tea produced in xihu and its surrounding areas ( hangzhou , china ) . as a famous green tea with protected designation of origin ( pdo ) , longjing is recognized as one of the top green teas for its special appearance ( flat and straight leaves ) , flavor , and taste . however , little information has been available on the feasibility of discriminating longjing from its three specific subproducing areas , namely , xihu , qiantang , and yuezhou . as the quality and prices of longjing tea from the above three producing areas are different , it is necessary to develop effective analysis methods for discrimination of longjing from different subproducing areas . because of the similarity ( processing , appearance , and taste ) among different subproducing areas , the specific geographical origins of longjing are usually distinguished by sensory analysis . however , because it is very expensive and may take years to train a tea taster , it would be more efficient to use some nonhuman techniques . recent years have witnessed increased applications of electronic tongue technology to analysis of wines , milk , tea , beer , juice , and so on [ 710 ] . in these applications , a very good time stability and sensitivity , a good correlation between human and electronic tongue judgment has been observed , which makes it a promising alternative to human sensory analysis of teas . this paper was focused on developing a rapid analysis method for discriminating specific subproducing areas of longjing tea by electronic tongue and chemometrics . robust principal component analysis ( robpca ) [ 11 , 12 ] was used to detect outliers in each class . partial least squares discriminant analysis ( plsda )   was used to develop the classification model . 155 longjing samples were collected from the local tea plantations in xihu ( 32 samples , class a ) , qiantang ( 59 samples , class b ) , and yuezhou ( 64 samples , class c ) . all the samples were preserved in a cool ( about 4c ) , dark , and dry place with integral packaging before preparation of tea extract . six gram of each sample was added with 250  ml boiling deionized water and infused for 10  min . the infusion was then filtered into a beaker and cooled to the room temperature ( 25c ) by water bath for electronic tongue analysis . the detection system consists of one reference electrode ( ag / agcl ) and 7 liquid cross selective sensors ( zz , ba , bb , ca , ga , ha , and jb ) . the cross - sensitivity and selectivity of the sensor array are listed in table 1 . the sensors array analyzed the solutions of tea samples with sampling interval of 1  s. each sample was measured for 150  s , which can ensure a stable response . all the data analysis was performed on matlab 7.0.1 ( mathworks , sherborn , ma , usa ) . the responses of the 7 sensors reported at 150  s were used for the subsequent data analysis . outliers in the data would degrade the classification models and prediction performance , so robust principal component analysis ( robpca ) was used to detect outliers in each class of samples . robpca can obtain robust projections of the original data points and avoid the masking effects caused by multiple outliers . with the computed robust score distance ( sd ) and orthogonal distance ( od ) , robpca can classify an object into one of the four groups : regular points ( with small sd and small od ) , good pca - leverage points ( with large sd and small od ) , orthogonal outliers ( with small sd and large od ) , and bad pca - leverage points ( with large sd and large od ) . with outliers deleted , the duplex algorithm   was used to divide the measured data into a training set and test set . duplex algorithm can obtain a test set of samples distributed uniformly in the range of training samples . for multiclass classification , plsda can be performed by regressing each column of a dummy response matrix y on the measured data x by pls . for the ith ( i = 1 , 2 , and 3 ) column in y , an element is set a value of 1 if the corresponding object is from class i ; otherwise , it is assigned a value of 0 . for prediction , a new sample is classified into class i when the ith element of its predicted response vector is nearest to 1 . monte carlo cross validation ( mccv )   was used to estimate the number of components in plsda by minimizing the mean percentage error of mccv ( mpemccv ) : 
 ( 1)mpemccv=i=1bnii=1bmi , 
 where b is the numbers of mccv data splitting , mi is the number of prediction objects , and ni is the number of wrongly predicted for the ith mccv data splitting . model sensitivity and specificity of prediction for each class were used to evaluate the performance of classification models : 
 ( 2)sensitivity = tptp+fn , specificity = tntn+fp , 
 where tp , fn , tn , and fp represent the numbers of true positives , false negatives , true negatives , and false positives , respectively . for classification , objects in each class were denoted as positives and the other two classes were denoted as negatives . the average electronic tongue features for each class of longjing tea are shown in figure 1 . seen from figure 1 , the features of the three classes have very similar response patterns . the features of class b and c are very similar and different from those of class a , especially in the responses of ga and ha . robpca was used for outlier detection with a significance level of 0.05 . because each class of tea has a different probability distribution , robpca models were performed separately on each class . figure 2 demonstrates the robpca plots for the three classes . for each class , a robpca model with three principal components ( pcs ) three pcs explained 89.7 , 91.5 and 92.1 percents of the total variances of each class , respectively . for outlier diagnosis , bad pca - leverage points , good pca - leverage points and orthogonal outliers were excluded to obtain a representative data set distributed in the entire range of measured samples . the numbers of objects with large sd and od were denoted . as a result , for class a , two orthogonal outliers ( objects 1 and 26 ) and two good pca - leverage points ( objects 15 and 31 ) were deleted ; for class b , four orthogonal outliers ( objects 1 , 19 , 24 , and 43 ) were deleted ; for class c , three orthogonal outliers ( objects 12 , 17 , and 29 ) and one bad pca - leverage point ( object 55 ) were deleted . the duplex algorithm was then performed to divide the remaining 143 tea samples into training and test objects . finally , 95 samples ( class a , 18 ; class b , 37 ; and class c , 40 ) were used for training and 48 samples ( class a , 10 ; class b , 18 ; and class c , 20 ) for prediction . plsda model was developed and mccv was used to select the number of latent variables ( lvs ) . for mccv , the original 95 training samples were randomly split into training ( 50% ) and test objects ( 50% ) for 100 times . the training and prediction results of a three - component plsda are demonstrated in figure 3 . the sensitivity / specificity of plsda for classes a , b , and c was 1.000(10/10)/1.000(38/38 ) , 1.000(18/18)/0.967(29/30 ) , and 0.950(19/20)/1.000(28/28 ) , respectively . the training accuracy was 1 and for prediction only one object from class c was wrongly assigned to class b , indicating the effectiveness of electronic tongue for classification of longjing tea samples . rapid and effective discrimination of longjing green tea from different subproducing areas was performed using electronic tongue and chemometrics . the sensitivity / specificity of plsda for classes a , b , and c was 1.000/1.000 , 1.000/0.967 , and 0.950/1.000 , respectively . electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of longjing . compared with human sensory analysis , this method is easier to perform and the more attractive economically . in the future studies , the comparison of chemical methods , for example , lc / uv / ms , to the electronic tongue analysis will be performed to investigate the statistical correlation between the chemistry and the tastes of longjing tea .
the feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a chinese green tea with protected designation of origin ( pdo ) . 
 155 longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors . to remove the influence of abnormal measurements and samples , robust principal component analysis ( robpca ) 
 was used to detect outliers in each class . 
 partial least squares discriminant analysis ( plsda ) was then used to develop a classification model . the prediction sensitivity / specificity of plsda was 1.000/1.000 , 1.000/0.967 , and 0.950/1.000 for longjing from xihu , qiantang , and yuezhou , respectively . electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of longjing .