Discrimination of Chicken Seasonings and Beef Seasonings Using Electronic Nose and Sensory Evaluation Huaixiang Tian, Fenghua Li, Lan Qin, Haiyan Yu, and Xia Ma
Abstract: This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes. Keywords: beef seasoning, chicken seasoning, electronic nose, multivariate statistical analysis, sensory evaluation
Aroma is the most significant attribute in selecting seasonings. It is traditionally evaluated by descriptive sensory analysis. This study used electronic nose combined with multivariate statistical analysis to discriminate the chicken seasonings and beef seasonings and to predict the sensory attributes. Electronic nose would be used as a rapid, useful, and objective technique for seasonings grading.
Practical Application:
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Introduction Seasoning is a kind of food ingredients which is used to improve food flavor. It is a necessity in people’s daily diet. The use of seasonings not only improves food aroma (V´azquez-Ara´ujo and others 2013; Yang and others 2011), but also increases energy intake (Best and Appleton 2011) and prolongs the shelf-life of product (Yin and Cheng 2003). Several essential elements required in the human diet, such as cobalt and manganese, are also detected in seasonings (Lemos and others 2010). Besides the technological properties, some seasonings are investigated as antimicrobial agents (Siripongvutikorn and others 2005; Gonz´alez and H¨anninen 2011). Chicken seasoning in particular, but also beef seasoning have been widely used by consumers of China. Aroma is the most significant attribute in selecting seasonings (V´azquez-Ara´ujo and others 2013). Usually, it is evaluated by descriptive sensory analysis which is the only reference method being normalized for odor assessment. It is performed by a group of trained tasters who use customized language and vocabulary to describe seasoning aroma attribute. The method is conventional and irreplaceable; however, it is time consuming, resource consuming, and easily influenced
MS 20140759 Submitted 5/4/2014, Accepted 8/26/2014. Authors Huaixiang Tian, Fenghua Li, Haiyan Yu, and Xia Ma are with Dept. of Food Science and Technology, Shanghai Inst. of Technology, 100 Haiquan Road, 201418, Shanghai, China. Author Lan Qin is with Nestl´e R&D Center Shanghai Ltd., Cao’an Road, 201812, Shanghai, China. Direct inquiries to author HaiYan Yu (E-mail: [email protected]).
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by mental and physical conditions of the trained tasters (Drake and others 2003). Electronic nose is becoming a reliable technology for assisting sensory evaluation to analyze food aroma (Wardencki and others 2013). Compared with sensory evaluation and gas chromatography-mass spectroscopy analysis, it is easy to use, objective, and cost effective (Bleibaum and others 2002). The chemical gas sensors of electronic nose may detect volatile compound, and then offer an aroma pattern of the volatiles to discriminate the samples. Nowadays, the electronic nose has a huge applications in food quality control, such as beverage (Bleibaum and others 2002; Ragazzo-Sanchez and others 2009; L´opez de Lerma and others 2013), meat product (Wang and others 2012), dairy product (Wang and others 2010; Cevoli and others 2011), fruit (Zhang and others 2008; Defilippi and others 2009), tomato (Berna and others 2004), red ginseng (Li and others 2012), tea (Yu and others 2008), rice plant (Zhou and Wang 2011), Mesona Blumes gum (Feng and others 2011), and Tuber magnatum Pico (Pennazza and others 2013). For the research of seasoning, what has been reported is the analysis of aroma attributes of pepper by sensory evaluation and electronic nose (Liu and others 2013). There are few reports about aroma analysis of chicken seasoning and beef seasoning using sensory evaluation and electronic nose. Therefore, the objectives of this study are to use sensory evaluation method to evaluate and classify different kinds of chicken seasoning and beef seasoning samples based on the difference of chicken/beef, gamey, garlic, spicy, soy sauce, onion, retention, and overall aroma intensity, and to discuss the feasibility of an electronic nose to differentiate and cluster these seasoning R C 2014 Institute of Food Technologists
doi: 10.1111/1750-3841.12675 Further reproduction without permission is prohibited
Discrimination of seasoning using E-nose . . . Table 1–Chicken and beef seasoning samples analyzed by elec- on their consensus, discriminability (for each attribute, and also tronic nose and sensory evaluation. for all samples), and replicability. Eight sensory attributes includAroma type
Information
C1 C2
Chicken seasoning Chicken seasoning
C3
Chicken seasoning
C4 CT1 CT2 CT3 CT4 B1 B2 B3 B4
Chicken seasoning Chicken seasoning Chicken seasoning Chicken seasoning Chicken seasoning Beef seasoning Beef seasoning Beef seasoning Beef seasoning
Shanghai Mrs. Le Food Co., Ltd Shanghai Crown Raw Garden Tianchu Condiment Co., Ltd Shanghai Vermouth Seasoning Food Co., Ltd Unilever Foods (China) Co., Ltd Top Note 1 Top Note 2 Top Note 3 Top Note 4 Top Note 1 Top Note 2 Top Note 3 Top Note 4
C1-C4 represents the chicken seasoning from different companies, respectively. CT1-CT4 represents the top note of aroma of chicken seasoning from different companies, respectively. B1-B4 represents the top note of aroma of beef seasoning from different companies, respectively.
ing chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were developed to describe the seasoning aroma. Retention aroma represented the aroma intensity after smelling the samples for 30 s. This is the continuously smelling, 30 s is the maximum time for determine the aroma intensity retention. Overall aroma intensity represented the aroma intensity of each sample, which depended on the chicken/beef, gamey, garlic, spicy, onion, and soy sauce aroma. Monadic sequential profile test was run on approximately a structured continuous 10-score scale. The sensory score standard of each sensory attribute was as follows: 0 to 2, very weak; 2 to 4, considerably weak; 4 to 6, neither weak nor strong; 6 to 8, considerably strong; 8 to 10, very strong. Twelve seasoning solution samples were prepared and the sensory evaluation experiment was conducted at the room temperature under red light. Plastic cups were labeled with a 3 number code. The panelists evaluated the samples and scored them randomly. Between 2 samples, panelists were required to have a break for 30 s to evaluate the next sample.
samples by principal component analysis (PCA), discriminant facElectronic nose measurement tor analysis (DFA), and cluster analysis (CA) methods. The correThe same seasoning samples used for sensory evaluation were lation between electronic nose sensors data and sensory evaluation analyzed by electronic nose (Alpha MOS Toulouse, France). The data is to be established by partial least squares regression (PLSR) instrument included sampling apparatus, detector unit and patmethod. tern recognition software which were used to record and analyze data. The detector unit contains 18 metal oxide sensors Materials and Methods (MOS) which are located in 3 controlled temperature chambers Seasoning samples (Wang and others 2010). Sensor chamber CL: LY2/LG, LY2/G, Twelve commercial seasonings (8 chicken seasonings and 4 beef LY2/AA, LY2/GH, LY2/gCTl, LY2/gCT; Sensor chamber A: seasonings) were selected for the research. They were mainly T30/1, P10/1, P10/2, P40/1, T70/2, PA/2; Sensor chamber B: defined not only by the difference in chicken aroma and beef P30/1, P40/2, P30/2, T40/2, T40/1, TA/2. There were 3 types aroma, but also by the difference in aroma intensity of seasoning of sensors: LY, T, and P. LY sensors were chromium-titanium ox(Table 1). They were all offered by Nestl´e R&D center (Nestl´e ides (Cr2−x Tix O3−y ) and tungsten oxide (WO3 ) sensors. Types T R&D Center Shanghai Ltd, Shanghai, China) and stored in the and P were based on tin dioxide (SnO2 ) but had different sensor desiccators before the measurement. Twelve grams of seasoning geometries. For ensuring that the data captured by the 18 sensors samples were placed into the graduated glass with a lid, added were stable, it was needed to warm up and calibrate the electronic 1000 mL hot water, stirred until the granule dissolved. The sam- nose system using processed pure air before testing based on the ple solutions equilibrated 10 min under the temperature of 50 °C, automatic procedure of electronic nose system supplied by Alpha which were analyzed by sensory evaluation and electronic nose, MOS. respectively. After preliminary experiments on the influence of sample amount, equilibration time, and equilibration temperature on the Sensory evaluation electronic nose response signals, the optimized sampling condiTwenty-four panelists (12 male and 12 female, age from 20 to tions were determined. Three grams of the seasoning samples 30, mean age at 24) were trained for 1 h every day to evaluate were placed in 10 mL vials which were capped with Teflon/silicon and became familiar with the sensory attribute of seasonings un- septa. After 10 min equilibration at 50 °C under agitation with the der the supervision of expert. There were 2 sessions per day, and speed 500 r/min, the headspace gas was pumped into 3 controlled 12 panelists were trained per session. Each session was performed temperature chambers for 9 s at a rate of 150 mL/min. The acin triplicate. The training session was in accordance to the China quisition duration for the sensor was 120 s which can make sensor Natl. Inst. of Standardization (CNIS) GB/T 16291.2–2010, and response signs to reach the stable values. Before next sample, the the reference (Hong and others 2012). First, 24 panelists were sensors chambers were purged with the processed dry and pure trained to get to know the aroma character of the seasoning sam- air in order to re-establishment the base line of the instrument. ples. They wrote down sensory attribute terms, then discussed There was an adaption phase for the electronic nose system to and determined the sensory attributes. The redundant attributes detect every new sample. To ensure the accuracy of the data, each were excluded by the consensus of the panelists and the experts analysis was repeated 8 times. The last 4 measurements of each from Nestl´e R&D Center Shanghai Ltd. Second, the panelists sample were used for the further analysis. were trained not only on attributes identification, but also on the usage of sensory profiling scales. Last, the panelists were tested Statistical analysis to distinguish the different samples. After training about 1 mo Means and standard deviation of sensory evaluation data were (60 h), panelists had the ability to recognize and identify these calculated. The sensory data were analyzed by analysis of varisensory attributes well. Ten qualified panelists (4 male and 6 fe- ance (ANOVA) and Duncan’s multiple range tests were used for male, age from 20 to 30, mean age at 25) were selected based identifying statistical separation among the means according to the Vol. 79, Nr. 11, 2014 r Journal of Food Science S2347
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Product code
Discrimination of seasoning using E-nose . . . Table 2–Sensory evaluation results for the 8 chicken seasonings and 4 beef seasonings samples. Sample code
Sensory attribute Chicken aroma
C1 C2 C3 C4 CT1 CT2 CT3 CT4 B1 B2 B3 B4
5.90 ± 0.570c 5.00 ± 0.670d 5.10 ± 0.880d 5.70 ± 0.82cd 7.70 ± 0.670b 8.20 ± 0.790ab 8.60 ± 0.700a 8.60 ± 0.840a Beef aroma 5.20 ± 0.420b 7.90 ± 0.740a 4.60 ± 0.520c 4.40 ± 0.520c
Gamey aroma
Garlic aroma
Spicy aroma
Onion aroma
Soy sauce aroma
Retention aroma
Overall aroma intensity
6.20 6.60 7.50 6.40 6.20 6.80 8.00 7.10
± ± ± ± ± ± ± ±
0.420e 0.700cde 0.710ab 0.700de 0.630e 0.420cde 0.670a 0.570bc
3.50 2.30 1.90 2.40 2.20 2.60 2.20 2.10
± ± ± ± ± ± ± ±
0.850a 0.670c 0.880c 0.700c 0.630c 0.520bc 0.630c 0.740c
3.80 2.00 1.80 2.50 3.00 2.30 3.20 1.90
± ± ± ± ± ± ± ±
0.920ab 0.940ef 0.630f 0.710cdef 0.670cd 0.670def 0.920bc 0.880f
3.00 1.90 2.00 3.30 4.30 2.60 2.20 2.10
± ± ± ± ± ± ± ±
0.820b 0.570c 0.820c 0.670b 0.820a 0.700bc 0.630c 0.740c
4.70 2.50 3.30 4.30 4.90 4.60 5.20 3.60
± ± ± ± ± ± ± ±
0.670bc 0.710e 0.670d 0.480c 0.740bc 0.970bc 0.790b 0.520d
6.90 4.30 6.50 6.60 7.70 6.90 8.00 7.60
± ± ± ± ± ± ± ±
0.880cd 0.820f 0.710de 0.840de 0.670ab 0.880cd 0.670a 0.840abc
7.50 5.10 6.60 6.30 7.20 6.80 6.80 7.30
± ± ± ± ± ± ± ±
0.710a 0.740d 0.700bc 0.820c 0.920ab 0.790abc 0.920abc 0.820ab
6.90 6.40 5.10 3.90
± ± ± ±
0.880bcd 0.700de 0.0.880f 0.740g
2.60 2.40 3.10 2.20
± ± ± ±
0.520bc 0.520c 0.740ab 0.790c
2.30 2.30 4.40 2.70
± ± ± ±
0.480def 0.480def 0.700a 0.950cde
1.90 3.20 4.00 3.20
± ± ± ±
0.740c 0.790b 0.670a 0.630b
3.60 5.10 6.00 6.30
± ± ± ±
0.970d 0.740b 0.940a 0.480a
6.00 7.00 6.70 6.30
± ± ± ±
0.820e 0.820bcd 0.670de 0.670de
5.50 6.80 6.20 6.50
± ± ± ±
0.850d 0.790abc 0.790c 0.530bc
Data are represented as mean ± SD. Significant differences (P = 0.05) between aroma type and aroma intensity of seasoning samples. Mean separation with different letters in the same column (a to g) are significantly different by Duncan’s multiple range test at P < 0.05 except for the 1st column that divided the chicken aroma and beef aroma. Mean separation of each sensory attribute was carried out based on the comparison between all pairs of means by the GLM procedure of SAS software.
reference (Olsen and others 2005). A P value of less than 0.05 was regarded as statistically significant. Besides, CA was performed to discriminate these seasonings based on the difference of sensory scores. They were conducted by the software SAS version 8.2 (SAS Inst. Inc., Cary, N.C., U.S.A.). For the data of electronic nose, PCA, DFA, and CA were used for discriminating and forming clusters among these seasoning samples based on the difference of aroma type and aroma intensity. PCA is an unsupervised technique which may calculate some principal components occupying the greatest variance according to reducing dimensionality in data sets and allow the
S: Sensory & Food Quality Figure 1–Cluster analysis plot from sensory evaluation data of seasonings.
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visualization of clusters (Alasalvar and others 2012). CA is an unsupervised classification technology which can cluster the samples according to their similarity (Alasalvar and others 2012). PCA was conducted with the Unscrambler version 9.7 (CAMO ASA, Oslo, Norway), DFA was performed using the software of Electronic Nose System, and CA was performed with the software SAS version 8.2. PLSR was a multivariate statistical method for relating the variation in 1 or several response variables to the variation of several predictors, which had been successfully applied to many researches to study the relationship between sensory attributes and
Discrimination of seasoning using E-nose . . . electronic nose (Tikk and others 2008; Song and others 2010; Qin and others 2013). PLSR was used to determine the correlation between seasoning aroma and the signal of electronic nose sensors. The data was statistically analyzed by calculating the PLS1 and PLS2 prediction models. Full cross-validation method was performed to validate the whole PLSR models. Multivariate analyses were conducted with the Unscrambler version 9.7 (CAMO ASA).
Results and Discussion Sensory evaluation results The sensory evaluation data was calculated using ANOVA with Duncan’s multiple range tests. Sensory evaluation results for the 8 chicken seasonings and 4 beef seasonings samples were shown in Table 2. The results showed significant differences for these sensory attributes which were used for describing seasonings aroma based
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Figure 2–Discrimination and testing result of seasoning samples by PCA based on electronic nose response data.
Figure 3–Discrimination and testing result of seasoning samples by DFA based on electronic nose response data. Vol. 79, Nr. 11, 2014 r Journal of Food Science S2349
Discrimination of seasoning using E-nose . . . on P value (P < 0.05). In general, “chicken,” “gamey,” “retention,” “overall” aroma intensity were the main sensory attributes for evaluating these seasonings, while “garlic,” “spicy,” “onion,” and “soy sauce” aroma were weaker but essential for these seasonings. Furthermore, the “chicken” aroma of samples CT1-CT4 were the strongest, samples C3, CT3, CT4, B1 had a stronger “gamey” aroma, and samples C1 and B3 had the strongest “garlic” and “spicy” aroma. The aroma of “onion” of samples CT1 and B3 was also stronger than the others, and “beef” aroma of sample 13 was stronger than sample 12, 14, and 15. Compared to chicken seasoning, beef seasoning had a stronger “soy sauce” aroma. Sensory evaluation data were analyzed using CA to discriminate these seasoning samples. The cluster method used in the paper was semipartial R-squared method and the cluster result was shown in Figure 1. Samples were divided into 3 groups. Samples, B1-B4, were located in the lowest side of the plot with long distance from chicken seasonings. The results indicated the discrimination
capacity of sensory evaluation to seasonings with different aroma types. Among the chicken seasonings, seasonings C1-C4 and CT1-CT4 were separated well each other. In combination with the results from Table 2, it was showed that seasoning samples CT1-CT4 had higher aroma intensity of “chicken” and “retention” aroma.
Electronic nose results PCA and DFA. An electronic nose analysis was carried out on all seasoning samples. PCA was conducted to identify the discrimination among all seasonings based on the pattern of relationship with individual composition variable. The result plot of the 1st 2 principal components of electronic nose was presented in Figure 2. The 1st 2 principal components showed 99% of the variance among these seasoning samples. The values of 92% data variance explained by the horizontal axis and 7% data variance
S: Sensory & Food Quality Figure 4–Cluster analysis of seasoning samples based on electronic nose response data.
Figure 5–PLS2 regression correlation loading plots of sensory attributes and electronic nose sensors of seasonings. Electronic nose measurements as X-matrix and sensory evaluation measurements as Y-matrix. Ellipses represent r = 0.5 (the inner ellipse) and 1.0 (the outer ellipse).
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Discrimination of seasoning using E-nose . . . could be explained by the fact that samples CT1-CT4 had higher intensity of “chicken,” “retention,” and “overall” aroma intensity than the other samples. It was found that seasonings with different aroma types and different aroma intensity could be discriminated based on different aroma using electronic nose combined with
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captured by the vertical axis, respectively. As shown in Figure 2, there was a clear cluster tends for the seasoning samples. Moving right to left along the 1st component, samples CT1-CT4 were separated from the other seasoning samples; while the 2nd component could distinguish the samples B1-B4 and C1-C4. This
Figure 6–Correlation coefficient, offset, slope, and root mean square error of prediction (RMSEP) are displayed as chicken aroma (A), beef aroma (B), gamey aroma (C), garlic aroma (D), spicy aroma (E), onion aroma (F), soy sauce aroma (G), retention aroma (H), and overall aroma intensity (I). Vol. 79, Nr. 11, 2014 r Journal of Food Science S2351
Discrimination of seasoning using E-nose . . .
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PCA method. To support the PCA results, DFA was used to discriminate these seasonings. The result plot of the 1st 2 principal components of electronic nose based on DFA method was presented in Figure 3. The 1st 2 principal components showed 97.291% of the variance among these seasoning samples. The values of 90.594% data variance explained by the horizontal axis and 6.697% data variance captured by the vertical axis, respectively. The cluster tend based on DFA was in accordance with the result of PCA. Cluster analysis. The same data from electronic nose were analyzed by CA which could be an assistant tool to identify subclass correlation among aroma (Liu and others 2012). The cluster method used in the paper was minimum distance method and the cluster result was shown in Figure 4. It was divided into 2 big groups for all samples. The 1st group was seasonings CT1-CT4 and the 2nd group included seasonings C1-C4 and seasonings B1-B4. In the 1st subclass, sample CT1 and sample CT2 were much similar due to similar intensity of “chicken,” “gamey,” “garlic,” and “soy sauce” aromas. Sample CT3 and sample CT4 were also similar as a result of their similar intensity of “chicken,” “garlic,” “onion,” “retention,” and “overall” aroma. In the 2nd subclass, sample C3 was close to sample C4 based on the similarity of “chicken,” “garlic,” “retention,” and “overall” aroma intensity. Sample B3 was close to sample B4 because of the similarity of “beef,” “soy sauce,” “retention,” and “overall” aroma intensity. The result was in accordance with that obtained by PCA. However, compared with the sensory evaluation, the difference between sample C1-C4 and B1-B4 were slight according to the cluster result This could be explained by the fact that the classification for sensory evaluation data was mainly based on the difference in seasoning aroma type, while the classification for the data of electronic nose sensors response signal was influenced by the difference of seasoning aroma intensity. The results were in accordance with the results of PCA and DFA.
Correlation between sensory attribute and electronic nose sensors The correlation between the sensory attributes and electronic nose measurements were established using PLSR. Figure 5 revealed the PLSR regression correlation loading plots of sensory attributes and electronic nose sensors of all the seasonings. The Y-matrix was specified as sensory evaluation data; the X-matrix was specified as electronic nose data. As shown in Figure 5, 50% of the explained variances showed in the inner ellipse and 100% of the explained variances showed in the outer ellipse. The model explained 83% of the cross validated variance of X-matrix and 69% the cross validated variance of Y-matrix. As shown in Figure 5, L1 showed the radial connected the origin of coordinates and the dot represented chicken aroma. L2 was the vertical line of L1. The sensors—LY2/LG, P10/2, P10/1, P40/1, T70/2, PA/2, P30/2, T40/2, T30/1, P40/2 located under the L1. It indicated that these sensors were negatively correlated to “chicken” aroma. It could be noted that the sensory attributes “beef” aroma and “soy sauce” were positively correlated to the sensor T40/1, LY2/gCTl, LY2/G, LY2/AA, LY2/gCT, LY2/GH and negatively associated with the sensors TA/2, P30/1, which was similar to “onion” aroma except that the sensor T40/1 was negatively associated with “onion” aroma. For the “spicy” aroma and “garlic” aroma, they were negatively correlated to the sensors which were mentioned above. Besides, “gamey” aroma, “retention” aroma, and “overall” aroma intensity were positively associated with the sensors LY2/LG and negatively correlated to the sensors P10/2, S2352 Journal of Food Science r Vol. 79, Nr. 11, 2014
P10/1, P40/1, T70/2, PA/2, P30/2, T40/2, T30/1, P40/2. “Chicken” aroma was negatively correlated to the sensors which were mentioned above. For supporting the experiment result of PLS2 and getting more information to predict the single sensory variable of 18n sensors. Regression analysis of PLS1 was carried out for prediction of these sensory attributes in chicken seasonings and beef seasonings. Test samples were analyzed by cross-validated model derived from PLSR. The Y-matrix was specified as sensory evaluation data; the X-matrix was specified as electronic nose data. Figure 6A-I showed the validation model by means of the cross-validation method and the correlation coefficients, slope, offset, and root mean square error of prediction (RMSEP) were calculated. It was found that the results had high correlation coefficient for the prediction about chicken aroma, beef aroma, gamey aroma, garlic aroma, spicy aroma, onion aroma, soy sauce aroma, retention aroma, and overall aroma intensity, indicating that electronic nose had good relationship with all the sensory attributes (r = 0.980, 0.995, 0.980, 0.973, 0.833, 0.704, 0.847, 0.872, and 0.976, respectively). The result were expected by electronic nose data (Figure 6A-I) as shown in previous study (Song and others 2010). All these slopes were close to 1 (0.980, 1.038, 1.010, 0.908, 0.896, 0.851, 0.850, 0.971, 1.082, respectively). The offset values (0.0663, −0.0117, −0.0618, 0.174, 0.0915, 0.287, 0.585, 0.190, −0.560, respectively) and RMSEP values (0.683, 0.314, 0.208, 0.112, 0.504, 0.498, 0.597, 0.502, 0.172, respectively) were low. Therefore, chicken and beef seasonings with electronic nose could be clearly used to predict sensory attributes. According to the present results obtained in the paper, it indicated that electronic nose was a useful tool to discriminate qualitatively the different seasonings and analyze quantitatively the sensory attributes.
Conclusion This is the 1st study to discriminate the chicken and beef seasonings using electronic nose and sensory evaluation. For the sensory evaluation results, the classification was mainly based on the difference in aroma types of seasonings according to ANOVA and PCA. For the results of electronic nose, samples C1-C4, CT1CT4, B1-B4 could be classified because of the difference of the aroma intensity of seasonings according to PCA, DFA, and CA. In addition, the correlation between the sensory attributes and the electronic nose sensors signal were revealed according to the PLSR loading plot. The results showed that electronic nose was a rapid and objective technique for differentiating of the chicken and beef seasonings.
Acknowledgments The authors acknowledge the research collaboration of Nestl´e R&D Center Shanghai Ltd.
Author Contributions Huaixiang Tian drafted the manuscript. Fenghua Li executed the statistical analysis and interpreted results. Lan Qin executed the sensory evaluation analysis. Haiyan Yu designed the research. Xia Ma helped in electronic nose analysis methods.
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Abstract: This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes. Keywords: beef seasoning, chicken seasoning, electronic nose, multivariate statistical analysis, sensory evaluation
Aroma is the most significant attribute in selecting seasonings. It is traditionally evaluated by descriptive sensory analysis. This study used electronic nose combined with multivariate statistical analysis to discriminate the chicken seasonings and beef seasonings and to predict the sensory attributes. Electronic nose would be used as a rapid, useful, and objective technique for seasonings grading.
Practical Application:
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Introduction Seasoning is a kind of food ingredients which is used to improve food flavor. It is a necessity in people’s daily diet. The use of seasonings not only improves food aroma (V´azquez-Ara´ujo and others 2013; Yang and others 2011), but also increases energy intake (Best and Appleton 2011) and prolongs the shelf-life of product (Yin and Cheng 2003). Several essential elements required in the human diet, such as cobalt and manganese, are also detected in seasonings (Lemos and others 2010). Besides the technological properties, some seasonings are investigated as antimicrobial agents (Siripongvutikorn and others 2005; Gonz´alez and H¨anninen 2011). Chicken seasoning in particular, but also beef seasoning have been widely used by consumers of China. Aroma is the most significant attribute in selecting seasonings (V´azquez-Ara´ujo and others 2013). Usually, it is evaluated by descriptive sensory analysis which is the only reference method being normalized for odor assessment. It is performed by a group of trained tasters who use customized language and vocabulary to describe seasoning aroma attribute. The method is conventional and irreplaceable; however, it is time consuming, resource consuming, and easily influenced
MS 20140759 Submitted 5/4/2014, Accepted 8/26/2014. Authors Huaixiang Tian, Fenghua Li, Haiyan Yu, and Xia Ma are with Dept. of Food Science and Technology, Shanghai Inst. of Technology, 100 Haiquan Road, 201418, Shanghai, China. Author Lan Qin is with Nestl´e R&D Center Shanghai Ltd., Cao’an Road, 201812, Shanghai, China. Direct inquiries to author HaiYan Yu (E-mail: [email protected]).
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by mental and physical conditions of the trained tasters (Drake and others 2003). Electronic nose is becoming a reliable technology for assisting sensory evaluation to analyze food aroma (Wardencki and others 2013). Compared with sensory evaluation and gas chromatography-mass spectroscopy analysis, it is easy to use, objective, and cost effective (Bleibaum and others 2002). The chemical gas sensors of electronic nose may detect volatile compound, and then offer an aroma pattern of the volatiles to discriminate the samples. Nowadays, the electronic nose has a huge applications in food quality control, such as beverage (Bleibaum and others 2002; Ragazzo-Sanchez and others 2009; L´opez de Lerma and others 2013), meat product (Wang and others 2012), dairy product (Wang and others 2010; Cevoli and others 2011), fruit (Zhang and others 2008; Defilippi and others 2009), tomato (Berna and others 2004), red ginseng (Li and others 2012), tea (Yu and others 2008), rice plant (Zhou and Wang 2011), Mesona Blumes gum (Feng and others 2011), and Tuber magnatum Pico (Pennazza and others 2013). For the research of seasoning, what has been reported is the analysis of aroma attributes of pepper by sensory evaluation and electronic nose (Liu and others 2013). There are few reports about aroma analysis of chicken seasoning and beef seasoning using sensory evaluation and electronic nose. Therefore, the objectives of this study are to use sensory evaluation method to evaluate and classify different kinds of chicken seasoning and beef seasoning samples based on the difference of chicken/beef, gamey, garlic, spicy, soy sauce, onion, retention, and overall aroma intensity, and to discuss the feasibility of an electronic nose to differentiate and cluster these seasoning R C 2014 Institute of Food Technologists
doi: 10.1111/1750-3841.12675 Further reproduction without permission is prohibited
Discrimination of seasoning using E-nose . . . Table 1–Chicken and beef seasoning samples analyzed by elec- on their consensus, discriminability (for each attribute, and also tronic nose and sensory evaluation. for all samples), and replicability. Eight sensory attributes includAroma type
Information
C1 C2
Chicken seasoning Chicken seasoning
C3
Chicken seasoning
C4 CT1 CT2 CT3 CT4 B1 B2 B3 B4
Chicken seasoning Chicken seasoning Chicken seasoning Chicken seasoning Chicken seasoning Beef seasoning Beef seasoning Beef seasoning Beef seasoning
Shanghai Mrs. Le Food Co., Ltd Shanghai Crown Raw Garden Tianchu Condiment Co., Ltd Shanghai Vermouth Seasoning Food Co., Ltd Unilever Foods (China) Co., Ltd Top Note 1 Top Note 2 Top Note 3 Top Note 4 Top Note 1 Top Note 2 Top Note 3 Top Note 4
C1-C4 represents the chicken seasoning from different companies, respectively. CT1-CT4 represents the top note of aroma of chicken seasoning from different companies, respectively. B1-B4 represents the top note of aroma of beef seasoning from different companies, respectively.
ing chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were developed to describe the seasoning aroma. Retention aroma represented the aroma intensity after smelling the samples for 30 s. This is the continuously smelling, 30 s is the maximum time for determine the aroma intensity retention. Overall aroma intensity represented the aroma intensity of each sample, which depended on the chicken/beef, gamey, garlic, spicy, onion, and soy sauce aroma. Monadic sequential profile test was run on approximately a structured continuous 10-score scale. The sensory score standard of each sensory attribute was as follows: 0 to 2, very weak; 2 to 4, considerably weak; 4 to 6, neither weak nor strong; 6 to 8, considerably strong; 8 to 10, very strong. Twelve seasoning solution samples were prepared and the sensory evaluation experiment was conducted at the room temperature under red light. Plastic cups were labeled with a 3 number code. The panelists evaluated the samples and scored them randomly. Between 2 samples, panelists were required to have a break for 30 s to evaluate the next sample.
samples by principal component analysis (PCA), discriminant facElectronic nose measurement tor analysis (DFA), and cluster analysis (CA) methods. The correThe same seasoning samples used for sensory evaluation were lation between electronic nose sensors data and sensory evaluation analyzed by electronic nose (Alpha MOS Toulouse, France). The data is to be established by partial least squares regression (PLSR) instrument included sampling apparatus, detector unit and patmethod. tern recognition software which were used to record and analyze data. The detector unit contains 18 metal oxide sensors Materials and Methods (MOS) which are located in 3 controlled temperature chambers Seasoning samples (Wang and others 2010). Sensor chamber CL: LY2/LG, LY2/G, Twelve commercial seasonings (8 chicken seasonings and 4 beef LY2/AA, LY2/GH, LY2/gCTl, LY2/gCT; Sensor chamber A: seasonings) were selected for the research. They were mainly T30/1, P10/1, P10/2, P40/1, T70/2, PA/2; Sensor chamber B: defined not only by the difference in chicken aroma and beef P30/1, P40/2, P30/2, T40/2, T40/1, TA/2. There were 3 types aroma, but also by the difference in aroma intensity of seasoning of sensors: LY, T, and P. LY sensors were chromium-titanium ox(Table 1). They were all offered by Nestl´e R&D center (Nestl´e ides (Cr2−x Tix O3−y ) and tungsten oxide (WO3 ) sensors. Types T R&D Center Shanghai Ltd, Shanghai, China) and stored in the and P were based on tin dioxide (SnO2 ) but had different sensor desiccators before the measurement. Twelve grams of seasoning geometries. For ensuring that the data captured by the 18 sensors samples were placed into the graduated glass with a lid, added were stable, it was needed to warm up and calibrate the electronic 1000 mL hot water, stirred until the granule dissolved. The sam- nose system using processed pure air before testing based on the ple solutions equilibrated 10 min under the temperature of 50 °C, automatic procedure of electronic nose system supplied by Alpha which were analyzed by sensory evaluation and electronic nose, MOS. respectively. After preliminary experiments on the influence of sample amount, equilibration time, and equilibration temperature on the Sensory evaluation electronic nose response signals, the optimized sampling condiTwenty-four panelists (12 male and 12 female, age from 20 to tions were determined. Three grams of the seasoning samples 30, mean age at 24) were trained for 1 h every day to evaluate were placed in 10 mL vials which were capped with Teflon/silicon and became familiar with the sensory attribute of seasonings un- septa. After 10 min equilibration at 50 °C under agitation with the der the supervision of expert. There were 2 sessions per day, and speed 500 r/min, the headspace gas was pumped into 3 controlled 12 panelists were trained per session. Each session was performed temperature chambers for 9 s at a rate of 150 mL/min. The acin triplicate. The training session was in accordance to the China quisition duration for the sensor was 120 s which can make sensor Natl. Inst. of Standardization (CNIS) GB/T 16291.2–2010, and response signs to reach the stable values. Before next sample, the the reference (Hong and others 2012). First, 24 panelists were sensors chambers were purged with the processed dry and pure trained to get to know the aroma character of the seasoning sam- air in order to re-establishment the base line of the instrument. ples. They wrote down sensory attribute terms, then discussed There was an adaption phase for the electronic nose system to and determined the sensory attributes. The redundant attributes detect every new sample. To ensure the accuracy of the data, each were excluded by the consensus of the panelists and the experts analysis was repeated 8 times. The last 4 measurements of each from Nestl´e R&D Center Shanghai Ltd. Second, the panelists sample were used for the further analysis. were trained not only on attributes identification, but also on the usage of sensory profiling scales. Last, the panelists were tested Statistical analysis to distinguish the different samples. After training about 1 mo Means and standard deviation of sensory evaluation data were (60 h), panelists had the ability to recognize and identify these calculated. The sensory data were analyzed by analysis of varisensory attributes well. Ten qualified panelists (4 male and 6 fe- ance (ANOVA) and Duncan’s multiple range tests were used for male, age from 20 to 30, mean age at 25) were selected based identifying statistical separation among the means according to the Vol. 79, Nr. 11, 2014 r Journal of Food Science S2347
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Product code
Discrimination of seasoning using E-nose . . . Table 2–Sensory evaluation results for the 8 chicken seasonings and 4 beef seasonings samples. Sample code
Sensory attribute Chicken aroma
C1 C2 C3 C4 CT1 CT2 CT3 CT4 B1 B2 B3 B4
5.90 ± 0.570c 5.00 ± 0.670d 5.10 ± 0.880d 5.70 ± 0.82cd 7.70 ± 0.670b 8.20 ± 0.790ab 8.60 ± 0.700a 8.60 ± 0.840a Beef aroma 5.20 ± 0.420b 7.90 ± 0.740a 4.60 ± 0.520c 4.40 ± 0.520c
Gamey aroma
Garlic aroma
Spicy aroma
Onion aroma
Soy sauce aroma
Retention aroma
Overall aroma intensity
6.20 6.60 7.50 6.40 6.20 6.80 8.00 7.10
± ± ± ± ± ± ± ±
0.420e 0.700cde 0.710ab 0.700de 0.630e 0.420cde 0.670a 0.570bc
3.50 2.30 1.90 2.40 2.20 2.60 2.20 2.10
± ± ± ± ± ± ± ±
0.850a 0.670c 0.880c 0.700c 0.630c 0.520bc 0.630c 0.740c
3.80 2.00 1.80 2.50 3.00 2.30 3.20 1.90
± ± ± ± ± ± ± ±
0.920ab 0.940ef 0.630f 0.710cdef 0.670cd 0.670def 0.920bc 0.880f
3.00 1.90 2.00 3.30 4.30 2.60 2.20 2.10
± ± ± ± ± ± ± ±
0.820b 0.570c 0.820c 0.670b 0.820a 0.700bc 0.630c 0.740c
4.70 2.50 3.30 4.30 4.90 4.60 5.20 3.60
± ± ± ± ± ± ± ±
0.670bc 0.710e 0.670d 0.480c 0.740bc 0.970bc 0.790b 0.520d
6.90 4.30 6.50 6.60 7.70 6.90 8.00 7.60
± ± ± ± ± ± ± ±
0.880cd 0.820f 0.710de 0.840de 0.670ab 0.880cd 0.670a 0.840abc
7.50 5.10 6.60 6.30 7.20 6.80 6.80 7.30
± ± ± ± ± ± ± ±
0.710a 0.740d 0.700bc 0.820c 0.920ab 0.790abc 0.920abc 0.820ab
6.90 6.40 5.10 3.90
± ± ± ±
0.880bcd 0.700de 0.0.880f 0.740g
2.60 2.40 3.10 2.20
± ± ± ±
0.520bc 0.520c 0.740ab 0.790c
2.30 2.30 4.40 2.70
± ± ± ±
0.480def 0.480def 0.700a 0.950cde
1.90 3.20 4.00 3.20
± ± ± ±
0.740c 0.790b 0.670a 0.630b
3.60 5.10 6.00 6.30
± ± ± ±
0.970d 0.740b 0.940a 0.480a
6.00 7.00 6.70 6.30
± ± ± ±
0.820e 0.820bcd 0.670de 0.670de
5.50 6.80 6.20 6.50
± ± ± ±
0.850d 0.790abc 0.790c 0.530bc
Data are represented as mean ± SD. Significant differences (P = 0.05) between aroma type and aroma intensity of seasoning samples. Mean separation with different letters in the same column (a to g) are significantly different by Duncan’s multiple range test at P < 0.05 except for the 1st column that divided the chicken aroma and beef aroma. Mean separation of each sensory attribute was carried out based on the comparison between all pairs of means by the GLM procedure of SAS software.
reference (Olsen and others 2005). A P value of less than 0.05 was regarded as statistically significant. Besides, CA was performed to discriminate these seasonings based on the difference of sensory scores. They were conducted by the software SAS version 8.2 (SAS Inst. Inc., Cary, N.C., U.S.A.). For the data of electronic nose, PCA, DFA, and CA were used for discriminating and forming clusters among these seasoning samples based on the difference of aroma type and aroma intensity. PCA is an unsupervised technique which may calculate some principal components occupying the greatest variance according to reducing dimensionality in data sets and allow the
S: Sensory & Food Quality Figure 1–Cluster analysis plot from sensory evaluation data of seasonings.
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visualization of clusters (Alasalvar and others 2012). CA is an unsupervised classification technology which can cluster the samples according to their similarity (Alasalvar and others 2012). PCA was conducted with the Unscrambler version 9.7 (CAMO ASA, Oslo, Norway), DFA was performed using the software of Electronic Nose System, and CA was performed with the software SAS version 8.2. PLSR was a multivariate statistical method for relating the variation in 1 or several response variables to the variation of several predictors, which had been successfully applied to many researches to study the relationship between sensory attributes and
Discrimination of seasoning using E-nose . . . electronic nose (Tikk and others 2008; Song and others 2010; Qin and others 2013). PLSR was used to determine the correlation between seasoning aroma and the signal of electronic nose sensors. The data was statistically analyzed by calculating the PLS1 and PLS2 prediction models. Full cross-validation method was performed to validate the whole PLSR models. Multivariate analyses were conducted with the Unscrambler version 9.7 (CAMO ASA).
Results and Discussion Sensory evaluation results The sensory evaluation data was calculated using ANOVA with Duncan’s multiple range tests. Sensory evaluation results for the 8 chicken seasonings and 4 beef seasonings samples were shown in Table 2. The results showed significant differences for these sensory attributes which were used for describing seasonings aroma based
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Figure 2–Discrimination and testing result of seasoning samples by PCA based on electronic nose response data.
Figure 3–Discrimination and testing result of seasoning samples by DFA based on electronic nose response data. Vol. 79, Nr. 11, 2014 r Journal of Food Science S2349
Discrimination of seasoning using E-nose . . . on P value (P < 0.05). In general, “chicken,” “gamey,” “retention,” “overall” aroma intensity were the main sensory attributes for evaluating these seasonings, while “garlic,” “spicy,” “onion,” and “soy sauce” aroma were weaker but essential for these seasonings. Furthermore, the “chicken” aroma of samples CT1-CT4 were the strongest, samples C3, CT3, CT4, B1 had a stronger “gamey” aroma, and samples C1 and B3 had the strongest “garlic” and “spicy” aroma. The aroma of “onion” of samples CT1 and B3 was also stronger than the others, and “beef” aroma of sample 13 was stronger than sample 12, 14, and 15. Compared to chicken seasoning, beef seasoning had a stronger “soy sauce” aroma. Sensory evaluation data were analyzed using CA to discriminate these seasoning samples. The cluster method used in the paper was semipartial R-squared method and the cluster result was shown in Figure 1. Samples were divided into 3 groups. Samples, B1-B4, were located in the lowest side of the plot with long distance from chicken seasonings. The results indicated the discrimination
capacity of sensory evaluation to seasonings with different aroma types. Among the chicken seasonings, seasonings C1-C4 and CT1-CT4 were separated well each other. In combination with the results from Table 2, it was showed that seasoning samples CT1-CT4 had higher aroma intensity of “chicken” and “retention” aroma.
Electronic nose results PCA and DFA. An electronic nose analysis was carried out on all seasoning samples. PCA was conducted to identify the discrimination among all seasonings based on the pattern of relationship with individual composition variable. The result plot of the 1st 2 principal components of electronic nose was presented in Figure 2. The 1st 2 principal components showed 99% of the variance among these seasoning samples. The values of 92% data variance explained by the horizontal axis and 7% data variance
S: Sensory & Food Quality Figure 4–Cluster analysis of seasoning samples based on electronic nose response data.
Figure 5–PLS2 regression correlation loading plots of sensory attributes and electronic nose sensors of seasonings. Electronic nose measurements as X-matrix and sensory evaluation measurements as Y-matrix. Ellipses represent r = 0.5 (the inner ellipse) and 1.0 (the outer ellipse).
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Discrimination of seasoning using E-nose . . . could be explained by the fact that samples CT1-CT4 had higher intensity of “chicken,” “retention,” and “overall” aroma intensity than the other samples. It was found that seasonings with different aroma types and different aroma intensity could be discriminated based on different aroma using electronic nose combined with
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captured by the vertical axis, respectively. As shown in Figure 2, there was a clear cluster tends for the seasoning samples. Moving right to left along the 1st component, samples CT1-CT4 were separated from the other seasoning samples; while the 2nd component could distinguish the samples B1-B4 and C1-C4. This
Figure 6–Correlation coefficient, offset, slope, and root mean square error of prediction (RMSEP) are displayed as chicken aroma (A), beef aroma (B), gamey aroma (C), garlic aroma (D), spicy aroma (E), onion aroma (F), soy sauce aroma (G), retention aroma (H), and overall aroma intensity (I). Vol. 79, Nr. 11, 2014 r Journal of Food Science S2351
Discrimination of seasoning using E-nose . . .
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PCA method. To support the PCA results, DFA was used to discriminate these seasonings. The result plot of the 1st 2 principal components of electronic nose based on DFA method was presented in Figure 3. The 1st 2 principal components showed 97.291% of the variance among these seasoning samples. The values of 90.594% data variance explained by the horizontal axis and 6.697% data variance captured by the vertical axis, respectively. The cluster tend based on DFA was in accordance with the result of PCA. Cluster analysis. The same data from electronic nose were analyzed by CA which could be an assistant tool to identify subclass correlation among aroma (Liu and others 2012). The cluster method used in the paper was minimum distance method and the cluster result was shown in Figure 4. It was divided into 2 big groups for all samples. The 1st group was seasonings CT1-CT4 and the 2nd group included seasonings C1-C4 and seasonings B1-B4. In the 1st subclass, sample CT1 and sample CT2 were much similar due to similar intensity of “chicken,” “gamey,” “garlic,” and “soy sauce” aromas. Sample CT3 and sample CT4 were also similar as a result of their similar intensity of “chicken,” “garlic,” “onion,” “retention,” and “overall” aroma. In the 2nd subclass, sample C3 was close to sample C4 based on the similarity of “chicken,” “garlic,” “retention,” and “overall” aroma intensity. Sample B3 was close to sample B4 because of the similarity of “beef,” “soy sauce,” “retention,” and “overall” aroma intensity. The result was in accordance with that obtained by PCA. However, compared with the sensory evaluation, the difference between sample C1-C4 and B1-B4 were slight according to the cluster result This could be explained by the fact that the classification for sensory evaluation data was mainly based on the difference in seasoning aroma type, while the classification for the data of electronic nose sensors response signal was influenced by the difference of seasoning aroma intensity. The results were in accordance with the results of PCA and DFA.
Correlation between sensory attribute and electronic nose sensors The correlation between the sensory attributes and electronic nose measurements were established using PLSR. Figure 5 revealed the PLSR regression correlation loading plots of sensory attributes and electronic nose sensors of all the seasonings. The Y-matrix was specified as sensory evaluation data; the X-matrix was specified as electronic nose data. As shown in Figure 5, 50% of the explained variances showed in the inner ellipse and 100% of the explained variances showed in the outer ellipse. The model explained 83% of the cross validated variance of X-matrix and 69% the cross validated variance of Y-matrix. As shown in Figure 5, L1 showed the radial connected the origin of coordinates and the dot represented chicken aroma. L2 was the vertical line of L1. The sensors—LY2/LG, P10/2, P10/1, P40/1, T70/2, PA/2, P30/2, T40/2, T30/1, P40/2 located under the L1. It indicated that these sensors were negatively correlated to “chicken” aroma. It could be noted that the sensory attributes “beef” aroma and “soy sauce” were positively correlated to the sensor T40/1, LY2/gCTl, LY2/G, LY2/AA, LY2/gCT, LY2/GH and negatively associated with the sensors TA/2, P30/1, which was similar to “onion” aroma except that the sensor T40/1 was negatively associated with “onion” aroma. For the “spicy” aroma and “garlic” aroma, they were negatively correlated to the sensors which were mentioned above. Besides, “gamey” aroma, “retention” aroma, and “overall” aroma intensity were positively associated with the sensors LY2/LG and negatively correlated to the sensors P10/2, S2352 Journal of Food Science r Vol. 79, Nr. 11, 2014
P10/1, P40/1, T70/2, PA/2, P30/2, T40/2, T30/1, P40/2. “Chicken” aroma was negatively correlated to the sensors which were mentioned above. For supporting the experiment result of PLS2 and getting more information to predict the single sensory variable of 18n sensors. Regression analysis of PLS1 was carried out for prediction of these sensory attributes in chicken seasonings and beef seasonings. Test samples were analyzed by cross-validated model derived from PLSR. The Y-matrix was specified as sensory evaluation data; the X-matrix was specified as electronic nose data. Figure 6A-I showed the validation model by means of the cross-validation method and the correlation coefficients, slope, offset, and root mean square error of prediction (RMSEP) were calculated. It was found that the results had high correlation coefficient for the prediction about chicken aroma, beef aroma, gamey aroma, garlic aroma, spicy aroma, onion aroma, soy sauce aroma, retention aroma, and overall aroma intensity, indicating that electronic nose had good relationship with all the sensory attributes (r = 0.980, 0.995, 0.980, 0.973, 0.833, 0.704, 0.847, 0.872, and 0.976, respectively). The result were expected by electronic nose data (Figure 6A-I) as shown in previous study (Song and others 2010). All these slopes were close to 1 (0.980, 1.038, 1.010, 0.908, 0.896, 0.851, 0.850, 0.971, 1.082, respectively). The offset values (0.0663, −0.0117, −0.0618, 0.174, 0.0915, 0.287, 0.585, 0.190, −0.560, respectively) and RMSEP values (0.683, 0.314, 0.208, 0.112, 0.504, 0.498, 0.597, 0.502, 0.172, respectively) were low. Therefore, chicken and beef seasonings with electronic nose could be clearly used to predict sensory attributes. According to the present results obtained in the paper, it indicated that electronic nose was a useful tool to discriminate qualitatively the different seasonings and analyze quantitatively the sensory attributes.
Conclusion This is the 1st study to discriminate the chicken and beef seasonings using electronic nose and sensory evaluation. For the sensory evaluation results, the classification was mainly based on the difference in aroma types of seasonings according to ANOVA and PCA. For the results of electronic nose, samples C1-C4, CT1CT4, B1-B4 could be classified because of the difference of the aroma intensity of seasonings according to PCA, DFA, and CA. In addition, the correlation between the sensory attributes and the electronic nose sensors signal were revealed according to the PLSR loading plot. The results showed that electronic nose was a rapid and objective technique for differentiating of the chicken and beef seasonings.
Acknowledgments The authors acknowledge the research collaboration of Nestl´e R&D Center Shanghai Ltd.
Author Contributions Huaixiang Tian drafted the manuscript. Fenghua Li executed the statistical analysis and interpreted results. Lan Qin executed the sensory evaluation analysis. Haiyan Yu designed the research. Xia Ma helped in electronic nose analysis methods.
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