Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose and tongue

FRIN-04903; No of Pages 7 Food Research International xxx (2013) xxx–xxx

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Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose and tongue Xuezhen Hong, Jun Wang ⁎, Shanshan Qiu Department of Biosystems Engineering, Zhejiang University, 886 Yuhangtang Road, Hangzhou 300058, China

a r t i c l e

i n f o

Article history: Received 4 April 2013 Received in revised form 30 September 2013 Accepted 23 October 2013 Available online xxxx Keywords: Electronic nose Electronic tongue Cherry tomato juice Adulteration Data standardization Data fusion

a b s t r a c t The study presented six approaches (two e-nose measurements, an e-tongue measurement and three fusion approaches using both of the instruments) for recognition and quantitative analysis of four tomato juice groups: unadulterated and three adulterated tomato juices with different adulteration levels. Recognition of the juices was performed by principle component analysis (PCA) and cluster analysis (CA). Quantitative calibration with respect to pH and soluble solids content (SSC) was performed using four regression methods (principle components regression (PCR) based on stepwise selection, multiple linear regression (MLR) based on raw feature vector, forward selection and stepwise selection features). CA based on different data standardization and distance calculation methods were compared, and precision-recall measure was applied to quantify clustering outcomes. The result implies that it is important to explore the optimum standardization and distance calculation methods for every dataset studied prior to CA. Humidity effect was also explored and the result showed that employing desiccant for e-nose measurement presented no improvement. The fusion dataset that consists of variables selected by analysis of variance (ANOVA) presented the best authentication ability, and the quality indices highly correlated to this dataset. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction It has been reported that in the European Union, the sales of freshly squeezed fruit juices labeled as 100% fruit represent approximately twothirds or 7.0 billion liters of total EU juice sales in 2011 (Faria, Magalhães, Nunes, & Oliveira, 2013). Fruits are relatively easy to authenticate by their morphological characteristics when they are intact and fresh but the act of processing them into juice gives rise to the possibility of adulteration. Substitution of material with cheaper alternatives, i.e. addition of water, sugar, pulpwash, senescent fruits or economical substitutes is a known topic for fruit juice issues (Reinhard, Sager, & Zoller, 2008). Traditionally, chromatographic methods such as gas chromatography with mass spectrometry (GC–MS), high performance liquid chromatography (HPLC) and ion chromatography, combined with appropriate sample preparation techniques, have been used for the quality control of fruit juices (Obón, Díaz-García, & Castellar, 2011). These analytical techniques, however, are timeconsuming and expensive, and they require skilled personnel to operate the equipment and interpret the analytical results (Baldwin et al., 1998). Electronic nose (e-nose) and electronic tongue (e-tongue) have proven to be a good alternative for traditional techniques in the analysis ⁎ Corresponding author. Tel.: +86 571 88982178; fax: +86 571 88982191. E-mail address: [email protected] (J. Wang).

of food (Hong, Wang, & Hai, 2012; Rudnitskaya, Schmidtke, Delgadillo, Legin, & Scollary, 2009; Tian, Deng, & Chen, 2007; Yu, Wang, Xiao, & Liu, 2009; Zhang et al., 2006). E-nose is a simulation of human nose to identify some simple or complex odor (Gardner & Bartlett, 1994). A typical e-nose system contains a non-selective chemical sensor array based on conducting polymers, metal oxides, surface acoustic wave devices, quartz crystal microbalances, or combination of these devices, a signal processing subsystem and a pattern recognition subsystem. Similarly, e-tongue is a sensor array combined with pattern recognition system for liquid analysis using both several non-specific, low-selective, chemical sensors with high stability and cross-sensitivity and ion-selective sensors (Dias et al., 2009). Instead of detecting one or two components of the substances, the e-nose and e-tongue systems give the whole information for identification. In the area of fruit and fruit juice detection, e-nose and e-tongue have also been reported to characterize odor and taste, respectively. The e-nose has been applied for early detection of Alicyclobacillus spp. in peach, orange and apple juices (Gobbi et al., 2010), verification of citrus juices according to fruits types (Reinhard et al., 2008), recognition of orange juices with different treatments (Shaw et al., 2000) and classification of white grape musts (grape juices before fermentation) in variety categories (Roussel, Bellon-Maurel, Roger, & Grenier, 2003). The e-tongue has been applied for classification of apple-based juices (Bleibaum et al., 2002), determination of orange juice percentage in juice beverages (Gallardo, Alegret, & del Valle, 2005) and simulation of juice aging process

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(Legin et al., 1997). In most of the juice detection researches, the sensor arrays used in e-noses were metal-oxide semiconductors (MOS). However, MOS sensors are water sensitive, so it is not possible to rule out the fact that the temporal changes observed in their sensor reading may be significantly affected by the fact that the sensors perceive increasing proportions of humidity due to increase in water vapor. Meanwhile, it is noticeable that the two sensor systems do not look at the same features when applied to the same liquid sample. The e-tongue electrodes are immersed in the sample while the e-nose sensors are in contact with its headspace. Sole usage of e-nose or e-tongue may not be sufficient, while simultaneous application of both instruments may increase the amount of information extracted from a sample when compared with the information from a single sensory organ (Cole, Covington, & Gardner, 2011; Di Natale et al., 2000; Tudu, Shaw, Jana, Bhattacharyya, & Bandyopadhyay, 2012). In this paper, we describe the use of an e-nose, an e-tongue and three fusion approaches using both of the e-nose and e-tongue instruments to discriminate adulteration of cherry tomato juices, as well as to predict the pH and soluble solids content (SSC) of the juice samples. In addition to direct e-nose measurement, the effect of humidity on the sensor responses was also explored by employing anhydrous sodium carbonate as desiccant. Different data standardization and distance calculation methods were discussed. Meanwhile, four regression methods were compared based on their prediction performances of pH and SSC. The main objective of this research is to explore the best approach for juice authentication. 2. Materials and methods 2.1. Sample preparation Chinese variety, youbei cherry tomatoes were picked twice for selfmade tomato juices at the experimental orchard in Department of Horticulture, Zhejiang University, Hangzhou, China. All the cherry tomatoes were picked at roughly red ripeness stage (more than 90% of the surface, in the aggregate, shows red color) (Agriculture, 1997). Upon arrival at the laboratory, cherry tomatoes were selected according to approximate uniform size and weight and non-damaged and not attacked by worm. The first batch of tomatoes was stored for 4 days at ambient atmosphere, 25 ± 1 °C and 80 ± 5% relative humidity until they became overripe with flesh softening. Juices of these cherry tomatoes were used as filler juices. The second batch was squeezed for fresh juices. During the juicing process, cherry tomatoes were placed in a fruit squeezer and juiced for 30 s. Fresh tomato juices were then blended with overripe tomato juices at three levels of adulteration from 10% to 30% (w/w) in steps of 10%, which can be of great practical interest. A group of unadulterated fresh juice was also prepared as the control group. Thus, there are in total four groups of juice samples: unadulterated fresh juice and 3 adulterated juices (10%, 20% and 30%). The juices were then filtered using medical gauze that was folded into eight layers; the filter liquor was collected for e-nose and e-tongue measurements. For each group, 25 replicates were prepared for direct e-nose measurement, e-nose measurement with a pretreatment and e-tongue measurement, respectively. 2.2. E-nose and e-tongue Headspace analysis was performed with a PEN 2 e-nose (Airsense Analytics, GmBH, Schwerin, Germany). The sensor array of this analytical instrument is composed of ten different MOS positioned in a small chamber. A description of the ten MOS has been given in our previous work (Hong et al., 2012). Since the object of this research is juice and MOS sensors are water sensitive, in contrast to direct e-nose measurement, a pretreatment of employing anhydrous sodium carbonate as desiccant prior to e-nose measurement was also conducted. The aim

was to observe if reducing water vapor would improve authentication performance of the e-nose. Taste analysis was performed with an α-Astree e-tongue (Alpha MOS company, France). This taste sensor consists of an array of seven liquid cross-sensitive electrodes or sensors (ZZ, BA, BB, CA, GA, HA and JB), a 16-position auto-sampler and associated interface electronic module. Specific description of the electrodes has also been given in our previous work (Wei, Wang, & Liao, 2009). 2.3. Experimental procedures 2.3.1. E-nose sampling procedure One hundred samples (25 replicates × 4 juice groups) were prepared for direct e-nose measurement and e-nose measurement with a pretreatment, respectively. For direct e-nose measurement, each sample (10 mL of tomato juice) was placed in a 500 mL airtight glass vial that was sealed with plastic wrap. The glass vial was closed for 10 min (headspace-generation time) while the headspace collected the volatiles from the samples. For e-nose measurement with a pretreatment, an addition of 5 g of anhydrous sodium carbonate was placed on a filter paper that was placed 4 cm above the bottom of the glass vial. The procedure for the rest was the same as that of the direct e-nose measurement. During the measurement process, the headspace gaseous compounds were pumped into the sensor arrays through Teflon tubing connected to a needle in the plastic wrap, causing the ratio of conductance of each sensor to change. The measurement phase lasted for 70 s, which was long enough for the sensors to reach stable signal values. The signal data from the sensors were collected by the computer once per second during the measurements. When the measurement process was complete, the acquired data were stored for later analysis. After each measurement, zero gas (air filtered by active carbon) was pumped into the sample gas path from the other port of the instrument for 60 s (flush time). In case of sensor pollution which could cause sensor drift, after all the measurements were done, nitrogen gas was pumped into the sample gas path to clear the sensor array. All the enose measurement procedures were carried out at a temperature of 25 ± 1 °C (controlled by air-conditioning). 2.3.2. E-tongue sampling procedure One hundred samples (25 replicates × 4 groups) were prepared for e-tongue detection. During the experiment, 80 mL of each sample was injected into a 120 mL beaker for e-tongue detection. The measuring time was set to 120 s for each sample, and the sensors were rinsed for 10 s using ultra-pure water to reach stable potential readings before detecting the next sample. Six replicated measurements were run on each sample. The first three measurement cycles were discarded due to instability, and the rest three stable sensor responses were obtained and averaged. The mean value of the three replicated measurements was considered as the original data of the samples. 2.3.3. Soluble solids content (SSC) and pH evaluation SSC of juice was measured by a temperature compensating refractometer in °Brix (Digital refractometer 2WA-J 0–32% Shanghai, China), and pH was measured by a titrimeter (Ti-Touch-916, Metrohm, Switzerland). For both experiments, three duplicates were carried out for each sample, and the mean values of SSC and pH were expressed as the original values. All the experiments and measurements were carried out at a temperature of 25 ± 1 °C. 2.4. Statistical analysis Data obtained from the sensor array of e-nose and e-tongue were analyzed using SAS 8.2 software (SAS Institute Inc., Gary, USA). Feature selection and construction for fusion datasets were performed by analysis of variance (ANOVA) and stepwise selection.

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ANOVA was used to explore if the changes in the independent variable (4 groups of tomato juices) have significant effects on each of the dependent variables (sensor signals), and Duncan multiple comparison was used to compare the means. Stepwise selection is usually employed to select a subset of the quantitative variables for use in discriminating among the classes. Stepwise selection begins with no variables in the model. At each step, the model is examined. If the variable in the model that contributes least to the discriminatory power of the model as measured by Wilks' lambda fails to meet the criterion to stay, then that variable is removed. Otherwise, the variable not in the model that contributes most to the discriminatory power of the model is entered. When all variables in the model meet the criterion to stay and none of the other variables meet the criterion to enter, the stepwise selection process stops. Principal component analysis (PCA) was applied to visualize data structure of the four juice classes in a reduced dimension (2D). Cluster analysis (CA) was also applied. CA is an unsupervised clustering procedure based on the similarity or distances among observations (Huang, Guo, Qiu, & Chen, 2007). The various clustering methods (11 methods provided by SAS) differ in how the distance between two observations is analyzed and computed. Before performing a cluster analysis, it is necessary to consider scaling or transforming the variables since variables with large variances tend to have a larger effect on the resulting clusters than variables with small variances do. In this paper, four methods — standard deviation normalization (STD), min–max normalization (RANGE), mean normalization (MEAN) and sum normalization (SUM) — were applied to standardize or transform the data prior to the cluster analysis based on Euclidean distances and Manhattan distances. For specific descriptions of the four standardization approaches and two distances calculation methods, refer to the works of Scott et al. (Scott, James, & Ali, 2006) and the SAS user's guide (Institute Inc., 2008). Meanwhile, precision-recall measure (PR) was applied to quantitatively evaluate clustering outcomes (Falasconi, Pardo, Vezzoli, & Sberveglieri, 2007; Jain, Murty, & Flynn, 1999; Rokach & Maimon, 2005). The PR is expressed as the number of correct matches M divided by number of instances n. Calculation equation of PR is given as follows: P ¼ M=n ¼

Xk h¼1

maxl jfxi jxi ∈ch ; xi ∈C l gj=n

ð1Þ

where maxl|{xi|xi ∈ ch, xi ∈ Cl}| means that we match the clustering result ch to actual clusters Cl by majority voting: if the majority of instances in ch belongs to Cl then we define ch is actually part of Cl . The PR ranges from 0 to 1, where 0 represents all the instances that are mismatched while 1 represents all the instances that are correctly matched. PR can be viewed as the classification accuracy index of classification techniques. In this paper, PR was used to judge which data standardization method or distances calculation method was better. Meanwhile, PR was also used to confirm PCA observations. Generally speaking, if a

3

dataset obtains a high PR value, then the data points from different juice classes of this dataset were discriminated from each other in a 2D PCA plot. Quantitative calibration with respect to pH and SSC was performed using four regression methods (principle components regression (PCR) based on stepwise selection, multiple linear regression (MLR) (Zhang, Chang, Wang, & Ye, 2008) based on raw feature vector, forward selection and stepwise selection features). MLR is a common method used in quantitative analysis. Different selections choices for independent variables would lead to different regression models. For example, PCR is actually an employment of PCA on raw independent variables prior to MLR.

3. Results and discussion 3.1. Response curves of e-nose and e-tongue Fig. 1a and b are typical responses of direct e-nose measurement (Fig. 1a) and e-nose measurement employing anhydrous sodium carbonate as desiccant (Fig. 1b) during measurement of a fresh cherry tomato juice sample. The x-axis represents time, and the y-axis represents sensors' ratio of conductance of the e-nose (G/G0, where G and G0 are the conductivities of the sensor when exposed to the sample gas and the zero gas, respectively). Each curve represents the change of a sensor's ratio of conductance during measurement. As shown in Fig. 1, except S4 and S10, the conductivity of the rest eight sensors gradually changed (gradually increased or decreased) and finally reached stable equilibrium. This is because the S4 and S10 sensors are sensitive to hydrogen, methane and aliphatic; yet these substances were not detected from the juice sample. It is noticeable that the change trends of sensors in both of the figures are the same: the G/G0 value of sensors S9, S6, S8 and S7 reached maximum at the 70th second, while that of sensors S1, S3, S5 and S2 reached minimum. Compared with Fig. 1a, the G/G0 value of S9 and S2 in Fig. 1b is relatively lower and higher, respectively. In general, Fig. 1b is like another version of Fig. 1a that has smaller extents of changes. This may be explained as follows: for e-nose measurement with a pretreatment, an addition of 5 g of anhydrous sodium carbonate was placed on a filter paper that was placed 4 cm above the bottom of the glass vial; thus, its headspace is smaller. In this paper, the response values of the 70th second of each sensor were extracted and analyzed. Fig. 1c is the typical response of e-tongue sensors detecting the same sample. The x-axis represents time, and the y-axis represents the potentiometric difference between each individually coated sensor and the Ag/AgCl reference electrode. Each curve represents the corresponding potentiometric difference value of a sensor against time (s). In the first 30 s, the response intensity of sensor BA and ZZ changed rapidly (rapidly increased and decreased, respectively), while that of sensor CA, JB, BB,

Fig. 1. Typical responses of a juice sample obtained by (a) direct e-nose measurement, (b) e-nose detection employing anhydrous sodium carbonate as measurement and (c) e-tongue measurement. S1–S10 are the ten e-nose sensors, and CA, BB, JB, GA, ZZ, BA and HA are the seven e-tongue electrodes.

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GA and HA decreased slowly. From 30 s to 80 s, except slowly increased response intensity in sensor BA, the response intensity of the other six sensors hardly changed. All the sensors' responses became stable afterwards and finally reached a dynamic equilibrium. In this paper, the response values of the 120th second of each sensor were extracted and analyzed. 3.2. Feature selection by ANOVA and stepwise selection for construction of sensor fusion datasets Each sample data contains 10 e-nose sensors/variables (the 70th second data point) and 7 e-tongue sensors/variables (the 120th second data point). Thus, when e-nose and e-tongue are combined (simple concatenation), there are 17 original variables in total for each sample, that is, a 17 × 100 (25 replications × 4 groups) data matrix (marked as fusion dataset 1). To explore the correlation between these 17 variables as well as to avoid curse of dimensionality, ANOVA and stepwise selection were employed for data reduction. To compare the ability of particular sensors to distinguish between different groups of tomato juices, one-way ANOVA was employed. The result showed that the significance level p for each sensor is b 0.0001, representing the significant difference that appeared among the mean values of the tomato juice groups as detected by any of the 17 sensors. For each sensor, Duncan multiple comparison method was then applied to compare the mean values between any two of the four groups (Table 1). The results are as follows: (1) for sensor S1, S3, S5 and BB, the unadulterated and the 10% groups are discriminable, but there is no significant difference between the 20% and 30% groups; (2) for sensor S2, the unadulterated and 20% groups are discriminable, but there is no significant difference between the 10% and 30% groups; (3) for sensor S4, the unadulterated and the 30% groups are discriminable, but there is no significant difference between the 10% and 20% groups; (4) for sensor ZZ, the 20% and 30% groups are discriminable, but there is no significant difference between the unadulterated and the 10% groups; (5) for sensors S6–S10, CA, GA, JB, BA and HA, all the four groups are discriminable from each other. Thus, sensors S6–S10, CA, GA, JB, BA and HA were obtained to construct fusion dataset 2, that is, a 10 × 100 (25 replications × 4 groups) data matrix. Stepwise selection starts with the largest classification weight (the fisher weight). The procedure was repeated with the unselected

Table 1 Duncan multiple comparison between the means of four cherry tomato juices (unadulterated and three adulterated groups: 10%, 20% and 30%). Sensors a

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 CA BB JB GA ZZ BA HA

Means of groups b Unadulterated

10%

20%

30%

0.783 A 7.512 C 0.788 A 1.046 C 0.802 A 1.340 D 1.263 D 1.266 D 2.013 D 1.049 B 2151.3 A 1912.1 A 2117.9 A 1473.9 A 709.1 B 687.9 A 1339.7 A

0.553 B 15.448 B 0.574 B 1.074 A 0.608 B 1.970 C 1.487 C 1.759 C 2.779 C 1.053 A 2147.7 B 1885.3 B 2001.3 B 1365.0 B 708.4 B 480.8 B 1164.1 B

0.450 C 15.575 A 0.478 C 1.073 A 0.524 C 2.620 B 1.569 B 2.243 B 3.097 B 1.047 C 2082.1 D 1829.7 C 1900.7 C 1326.1 D 704.4 C 382.1 C 1049.5 C

0.459 C 17.106 B 0.477 C 1.060 B 0.519 C 2.818 A 1.636 A 2.337 A 3.178 A 1.027 D 2088.9 C 1828.8 C 1852.8 D 1334.8 C 711.1 A 343.0 D 978.5 D

a S1–S10 are the ten e-nose sensors; CA, BB, JB, GA, ZZ, BA and HA are the seven etongue electrodes. b Means with the same letter are not significantly different at the 99.99% confidence level.

variables and ends when the fisher weight of the best unselected variable was b 0.05. As a result, 9 variables were selected to construct fusion dataset 3. The sensors are S6, S10, BA, CA, JB, GA, HA, BB and ZZ. So dataset 3 is a 9 × 100 (25 replications × 4 groups) data matrix. 3.3. PCA analysis of the six datasets (two e-nose datasets, an e-tongue dataset and three fusion datasets) PCA was applied to analyze the six datasets. PCA description of data structure of the four tomato juice groups based on direct e-nose measurement (Fig. 2a), e-nose employing anhydrous sodium carbonate as desiccant (Fig. 2b), e-tongue measurement (Fig. 2c), fusion dataset 1 (Fig. 2d), fusion dataset 2 (Fig. 2e) and fusion dataset 3 (Fig. 2f) are shown in Fig. 2, where the unadulterated and the three adulterated tomatoes juices are marked as control, 10%, 20% and 30%, respectively. The total contribution variance of PC1 and PC2 in each figure is higher than 90%, meaning the first two PCs are sufficient enough to explain the total variance of the dataset. For each dataset, the data points of the four juice groups were discriminable from each other as observed in Fig. 2. However, the 20% and 30% groups based on e-tongue dataset are close to each other, and the three adulterated groups are close to each other in Fig. 2b. This structure of data distribution may result in incorrect clustering and classification results. It is interesting to note that the relative position of the four groups based on two e-nose measurements (Fig. 2a and b) is the same along the PC1 axis (the unadulterated group has the lowest PC1 value while the 30% group has the highest PC1 value); however, relative positions of the four groups based on e-tongue and fusion datasets (Fig. 2c–f) are different with the e-nose case. It is also noticeable that the 20% and 30% groups based on fusion dataset 3, which is actually a combination of e-tongue dataset and two completely discriminant sensors (sensor S6 and S10), are more discriminable than in the case of e-tongue dataset. This implies that addition of sensors with good discriminant ability to a technique may increase classification ability of the technique. 3.4. Comparison of CA methods for the six datasets CA was applied to cluster the six datasets and PR was used to evaluate the clustering results. Clustering outcomes based on using of nonstandardization approach as well as four approaches (STD, RANGE, MEAN and SUM) combined with two distance calculation methods (Euclidean distance and Manhattan distance) to standardize or transform the data prior to CA are listed in Table 2. For direct e-nose measurement based on non-standardized data, PR is as poor as 0.46 with 54 samples being mismatched. However, after employing any of the four standardization approaches, PR raises to 1. For e-nose measurement with pretreatment, PR ranges from 0.46 (54 of mismatched samples) to 0.77 (23 of mismatched samples), with non-standardization presents the worst clustering precision while Euclidean distance based STD pproach presents the highest clustering precision. For the e-tongue measurement, besides the CA method that is based on STD approach with Euclidean distance as distance calculation method has 11 samples mismatched, PR for all the other cluster methods are 1. For fusion approach based on simply concatenation (dataset 1), PR of SUM and MEAN approaches are relative low. However, PR reaches 1 for STD and RANGE approaches no matter which distance calculation method is applied. For fusion approach based on stepwise selection (dataset 2), except PR of non-standardization as well as RANGE approach based on Manhattan distance which are 1, other standardization has low PR values. For fusion approach based on ANOVA selected variables (dataset 3), no matter which standardization or distance calculation method is used, PR is 1. The result of this analysis demonstrates that when variables are standardized by different methods including STD, RANGE, MEAN and SUM based on different distance calculation methods, the performance differs. In this paper, pretreatment of employing anhydrous sodium

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Fig. 2. PCA description of four tomato juice groups (unadulterated and three adulterated groups: 10%, 20% and 30%) based on (a) direct e-nose measurement, (b) e-nose employing anhydrous sodium carbonate as desiccant, (c) e-tongue measurement, (d) simple sensor concatenation of e-nose and e-tongue, (e) ANOVA selected fusion dataset and (f) stepwise selected fusion dataset.

carbonate as desiccant did not improve clustering performance of e-nose, and the fusion approach based on ANOVA selected variables achieved the best clustering performance (no matter what data pretreatment is, this dataset kept 100% accuracy, meaning the four groups of this dataset are completely separable). However, it is noticeable that except e-nose with pretreatment, all the other 5 datasets could get 100% clustering precision (PR = 1) as long as appropriate standardization approaches and distance calculation methods were taken (PR of the five datasets based on RANGE standardization and Manhattan distance reached 1). There is no guarantee which distance calculation and standardization methods would always be a better choice; it is the property of the original data that actually matters. In consideration of the PCA and CA results, the fusion approach based on ANOVA selected variables presented the best authentication ability and was thus chosen for further regression analysis.

3.5. Prediction of pH and SSC based on ANOVA selected dataset 3.5.1. Quality indices (pH and SSC) of four tomato juices As presented in Table 3, when the adulteration level increased from non-adulteration to 30%, pH and SSC of the cherry tomato juices declined slightly from 4.37 to 4.32, and 5.7 to 5.4 °Brix, respectively. The changes in these two taste indices are slight, e.g., the average pH values for the 10% and 20% group are 4.35 and 4.34, respectively; yet, during repeated measuring of the 10% group (or 20% group), the pH value may range from 4.34 to 4.36 (4.33 to 4.35 for the 20% group). In addition, some researchers (Harker et al., 2002) found that two samples needed to differ in °Brix by more than 1 (i.e. 1 g sucrose in 100 g aqueous solution) before evoking a response in perceived sweet taste for median panelist. Thus, it is not easy for human taste to perceive the difference. However, after ANOVA, we found that there are significant differences

Table 2 Utilization of different standardization methods prior to clustering of the four juice groups based on six datasets. Datasets

Direct e-nose E-nose with desiccant E-tongue Fusion dataset 1f Fusion dataset 2g Fusion dataset 3h

Clustering outcomes

Number of mismatch PRe Number of mismatch PR Number of mismatch PR Number of mismatch PR Number of mismatch PR Number of mismatch PR

None

54 0.46 54 0.46 0 1 0 1 0 1 0 1

Based on Euclidean distance

Based on Manhattan distance

STDa

RANGEb

SUMc

MEANd

STD

RANGE

SUM

MEAN

0 1 47 0.53 11 0.89 0 1 26 0.74 0 1

0 1 24 0.76 0 1 0 1 26 0.74 0 1

0 1 24 0.76 0 1 49 0.51 49 0.51 0 1

0 1 50 0.50 0 1 49 0.51 49 0.51 0 1

0 1 23 0.77 0 1 0 1 45 0.55 0 1

0 1 24 0.76 0 1 0 1 0 1 0 1

0 1 24 0.76 0 1 20 0.80 49 0.51 0 1

0 1 24 0.76 0 1 20 0.80 49 0.51 0 1

a STD: standard deviation normalization. b RANGE: min–max normalization. c SUM: sum normalization. d MEAN: mean normalization. e PR: precision recall measure.f Fusion dataset 1: based on simply concatenation of original e-nose and e-tongue sensors. g Fusion dataset 2: based on stepwise selection of fusion dataset 1. h Fusion dataset 3: based on ANOVA and Duncan selection of fusion dataset 1.

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Table 3 pH and soluble solid content (SSC) values of the four cherry tomato juices (unadulterated and three adulterated groups: 10%, 20% and 30%). Groups

pH

Unadulterated 10% 20% 30%

4.37 4.35 4.34 4.32

SSC (°Brix) ± ± ± ±

0.0058 0.0053 0.0049 0.0051

5.7 5.6 5.5 5.4

± ± ± ±

0.058 0.038 0.048 0.079

among SSC and pHs of the four juice classes (significance level p b 0.001 in both cases). This helps to explain the fact that the sensor signals of the four juice groups are discriminable. 3.5.2. Prediction of pH and SSC by four regression methods Four regression methods (PCR based on stepwise selection, MLR based on raw feature vector, forward and stepwise selection features) were performed on the fusion dataset based on ANOVA selected variables (a 9 variables × 100 samples data matrix) to predict pH and SSC values. For each method, leave-one-out inner cross-validation (LOOCV) and 5-fold external cross-validation (5-fold CV) were performed on the dataset. In an external CV process, the overall 100 samples (25 replications × 4 groups) were randomly divided into calibrating and testing subsets: 68 samples (randomly chose 17 samples from each group) for the training set and 32 samples (the rest of the 8 samples of each group) for the testing set. This process of dividing training and testing subsets repeated five times. During each external CV process, inner LOO-CV was applied to verify the training model. In the LOO-CV, 67 samples were used for training and 1 was left for validation. The process was repeated 67 times until all of the samples in the training set have been used for validation once. The accuracy of the prediction model was estimated using the parameters obtained from the fitted equation: squared correlation coefficient (R2) and root mean square error (RMSE) between predicted and experimental values. Generally, the larger the R2 and the lower the RMSE are, the better the model is. The results of the four regression methods are listed in Table 4, where performances based on the four regression methods are equally good. Although it may seem surprising to see that the two nonvolatile quality indexes (pH and SSC) could be sensed by gas sensors of e-nose, such results are meaningful since adulteration of tomato juices involves changes in the composition of both soluble and volatile compounds. These changes correlated with the changes in the two quality indexes thus allowing prediction of the latter using e-nose. Generally, the e-nose or e-tongue does not measure the quality indexes directly; it actually measures volatiles or other soluble compounds that are well correlated with the quality indexes. No matter which of the regression model was used, the correlation coefficient between actual values and predicted values was very high, with R2 N 0.99 for both of the training

and prediction sets, indicating both of the quality indices (pH and SSC) are highly related to the responses of the sensors. This may be explained as follows: there are significant differences among SSC and pHs of the four groups; meanwhile, the sensors in the ANOVA based dataset also have significant different values for the four group. Thus, the dataset is well correlated with the quality indices. The regression expression of the four regression methods (PCR based on stepwise selection, MLR based on raw feature vector, forward and stepwise selection features) were also given and described. For the PCR model based on stepwise selection, the regression expressions are described as follows: pH ¼ 4:3330 þ 0:0304Z1–0:0027Z2 þ 0:0468Z3–0:0162Z4 þ 0:0303Z5–0:0186Z6 þ 0:0328Z7–0:0420Z9

ð1Þ

SSC ¼ 2:654–0:419Z1–0:437Z2 þ 0:089Z3–0:200Z4 þ 0:491Z5–0:802Z6 þ 0:270Z8–0:372Z9

ð2Þ

where Z1–Z9 represent the scores of PC1–PC9. For the MLR model based on raw feature vector, the regression expressions are described as follows: pH ¼ 4:3331–0:0120S6–0:0013S7 þ 0:0079S8–0:0027S9–0:0018S10 þ 0:0608CA þ 0:0363JB þ 0:0311GA þ 0:0055BA–0:0425HA

ð3Þ

SSC ¼ 2:654 þ 0:357S6 þ 0:024S7 þ 0:030S8–0:184S9–0:239S10 þ 0:193CA–0:903JB þ 0:427GA þ 0:052BA–0:594HA

ð4Þ

where S6–S10 represent the signals of e-nose sensors, and CA, GA, JB, BA and HA represent the signals of e-tongue sensors. For the MLR model based on forward selection features, the regression expressions are described as follows: pH ¼ 4:3330–0:0057S9–0:0018S10 þ 0:0606CA þ 0:0431JB þ 0:0312GA–0:0414HA

ð5Þ

SSC ¼ 2:654 þ 0:361S6–0:141S9–0:243S10 þ 0:183CA–0:880JB þ 0:449GA–0:581HA

ð6Þ

where S6, S9 and S10 represent the signals of e-nose sensors, and GA, JB, GA and HA represent the signals of e-tongue sensors. For the MLR model based on stepwise selection features, the regression expressions are described as follows: pH ¼ 4:3327 þ 0:0645CA þ 0:0340GA

ð7Þ

SSC ¼ 2:654 þ 0:361S6–0:141S9–0:243S10 þ 0:183CA–0:880JB þ 0:449GA–0:581HA

ð8Þ

where S6, S9 and S10 represent the signals of e-nose sensors, and CA, GA, JB and HA represent the signals of e-tongue sensors. Table 4 Results of calibration and prediction for quality indices of juices on the base of ANOVA optimized e-nose and e-tongue fusion dataset. Quality indices

pH

SSC c

a b c

Model

a

PCR MLR b based on raw feature MLR based on forward selection MLR based on stepwise selection PCR MLR based on raw feature MLR based on forward selection MLR based on stepwise selection

PCR: principle component regression. MLR: multiple linear regression. SSC: soluble solid content.

Calibration

Prediction

R2

RMSE

R2

RMSE

0.996 0.996 0.996 0.994 0.998 0.998 0.998 0.998

0.0061 0.0062 0.0060 0.0072 0.0696 0.0690 0.0680 0.0680

0.992 0.991 0.994 0.991 0.997 0.997 0.997 0.997

0.0099 0.0095 0.0086 0.0097 0.0848 0.0877 0.0819 0.0819

4. Conclusions This paper presented different approaches and data analysis methods for the recognition and quantitative analysis of four cherry tomato juices (unadulterated and three adulterated juices with different levels of adulteration: 10% to 30%). Six approaches (two e-nose measurements, an e-tongue measurement and three fusion approaches using both of the instruments) were considered to explore the optimal approach for juice authentication. The results obtained are as follows: (1) Authentication ability based on direct e-nose measurement is better than e-nose measurement with a pretreatment of employing anhydrous sodium carbonate as desiccant. (2) The fusion approach based on ANOVA selected variables presented the best authentication performance.

Please cite this article as: Hong, X., et al., Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose an..., Food Research International (2013), http://dx.doi.org/10.1016/j.foodres.2013.10.039

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Please cite this article as: Hong, X., et al., Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose an..., Food Research International (2013), http://dx.doi.org/10.1016/j.foodres.2013.10.039