, Vol.36, No.20

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1 , Vol.36, No.20 * PEN3Loadings W1C W5S W1S W1W W2S W2W W3S total volatile basic nitrogen TVB-N thiobarbituric acid reactive substances TBARS TVB-N TBARS 0 1 mg/l 5 min 5 mg/l 10 min Application of Electronic Nose in Freshness Evaluation of Tilapia Fillets as Affected by Ozone Treatment YAN Mingyue, LU Yuqin, CHEN Dewei* (Institute of Light Industry and Food Engineering, Guangxi University, Nanning , China) Abstract: In this study, an electronic nose (PEN3) was used to investigate the flavor of tilapia meat samples with different ozone water treatments. Correlation analysis and loading analysis were carried out on the W1C, W5S, W1S, W1W, W2S, W2W and W3S components constituting a new sensor array. The data were analyzed by principal component analysis (PCA) and total volatile basic nitrogen (TVB-N) and thiobarbituric acid reactive substances (TBARS) were used as indicators to evaluate the freshness of tilapia fillets. The results showed that the detection of electronic nose was basically consistent with the zero-grade reaction of TVB-N and TBARS analysis. The treatment with 5 mg/l ozone water for 10 min rather than with 1 mg/l ozone water for 5 min significantly delayed freshness deterioration of tilapia fillets during storage. Key words: electronic nose; ozone treatment; tilapia; freshness TS254.4 A doi: /spkx [1-2] total vola tile basic nitrogen TVB-N [3] GA yanmingyue@163.com *1975 chendw@gxu.edu.cn

2 2015, Vol.36, No [4-5] Korel [6] 0% 4% 8% Balasubramanian [7] 100% Du [8] [9] ph TVB-N [10] TVB-N [11] PEN3 principle component analysis PCA TVB-N thiobarbituric acid reactive substances TBARS g2 hgb/t [12] 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min TVB-N TBARS trichloroacetic acid TCAthiobarbituric acid TBA 1,1,3,3- PEN3 Airsense TVB-N SC/T [13] TVB-N TBARS Faustman [14] TBARS 5 g 25 ml 20% TCA 20 ml 1 h r/min 10 min 8 ml 50 ml 2 ml 0.01 mol/l TBA25 min 532 nm 3 1,1,3, Huang Lin [15] 5.00 g 25 ml 30 min 60 s 1 s 100 s 600 ml/min3 PEN3 W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S 10 1 [16] Table 1 1 PEN3 Response feature of the sensor array R1 W1C 10 ml/m 3 R2 W5S NO 2 1 ml/m 3 R3 W3C 10 ml/m 3 R4 W6S H ml/m 3 R5 W5C 1 ml/m 3 R6 W1S CH ml/m 3 R7 W1W H 2 S 1 ml/m 3 R8 W2S CO 100 ml/m 3 R9 W2W H 2 S 1 ml/m 3 R10 W3S CH 3 10 ml/m SPSSVersion 17.0 Excel Winmuster PCALoadings

3 , Vol.36, No TVB-N TVB-N TVB-N GB [17] TVB-N 20 mg/100 g 01 [18] TVB-N0 TVB-N 01 4 TVB-N TVB-N 5 mg/l 10 min TVB-N 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min R TVB-N / mg/100 g Fig mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min /d 1 TVB-N Changes in TVB-N value of tilapia during storage TVB-N20 mg/100 g 01 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min TBARS TBARS TBARS0 6 mg/kg 1 mg/kg [19] TBARS 0 TBARS 02 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min R TBARS6 mg/kg 0 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min TVB-N TBARS1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min TBARS / mg/kg mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min /d 2 TBARS Fig.2 Changes in TBARS value of tilapia during storage G/G s PEN3 G/G /s 1 10.R9 R7 R6 R2 R8 R10 R4 R5 R3 R1 Fig.3 3 Typical response curves of electronic nose for tilapia during storage [20-21]

4 2015, Vol.36, No W1C W3C W3C W5C 0.01 W1C W5C W6S W5C 0.05 Table 2 2 Correlation coefficients of ten sensors W1C W5S W3C W6S W5C W1S W1W W2S W2W W3S W1C 1 W5S 74 1 W3C 0.995** W6S 0.939* * 1 W5C 0.997* 0.907* 0.999** 0.918* 1 W1S 0.918* * * 1 W1W W2S 86 89* * 1 W2W W3S *. α=0.05 **. α=0.01 Loadings [22-23] Loadings 4 W1S W1W W2W PC1 W1SW1W W2W W5S PC2 W1WW5C W6S 0 W5C W6S W5C W6S [24] W1W W2W [25] PEN3 18 Loadings W1S W2S W5S W1W W2W W3C W6S W5C W1C W5S W1S W1W W2S W2W W3S Fig.4 PC % 0.7 W1W W2W 0.3W5CW5S 0.1 W3C W1C W2S W6S W3S 0.3 W1S PC % 4 Loading Loading analysis for contribution rates of different sensors PCA Loadings W3C W6S W5C W1C W5S W1S W1W W2S W2W W3S PC2 4.78% PC2 8.59% PC2 8.38% PC % 1.4 A PC % 1.6 B PC % 1.8 C d PC % 2.0 D PC % A. B. 1 mg/l 5 min C. 2.5 mg/l 10 min D. 5 mg/l 10 min 5 PCA Fig.5 PCA analysis of original sensory array 5 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min PCA PC1 PC % 99.70% 99.73% 99.71% PC1 PC % 97.72% 98.54% 98.41% 95%2 PCA 5 6

5 , Vol.36, No.20 PC2 7.18% PC2 7.77% PC % 0 A B PC1 95% PC % C PC % D PC % Fig PC % 6 PCA PCA analysis of optimized sensory array PCA6 6A B 1 mg/l 5 min C D mg/l 10 min3 3 TVB-N TBARS 0 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min PCA TVB-N TBARS 0 PCA PCA 1 mg/l 5 min 5 mg/l 10 min TVB-N TBARS 0 3 TVB-N TBARS 0 1 mg/l 5 min 2.5 mg/l 10 min 5 mg/l 10 min Loadings W1C W5S W1S W1W W2S W2W W3S PCA TVB-N TBARS 0 1 mg/l 5 min 5 mg/l 10 min [1],,,. [J]., 2009, 30(18): [2]. [D]. :, [3] LIMBO S, SINELLI N, TORRI L, et al. Freshness decay and shelf life predictive modelling of European sea bass (Dicentrarchus labrax) applying chemical methods and electronic nose[j]. LWT- Food Science and Technology, 2009, 42(5): [4] PERIS M, ESCUDER-GILABERT L. A 21 st century technique for food control: electronic noses[j]. Analytica Chimica Acta, 2009, 638(1): [5] GÓMEZ A H, WANG J, HU G, et al. Electronic nose technique potential monitoring mandarin maturity[j]. Sensors and Actuators B: Chemical, 2006, 113(1):

6 2015, Vol.36, No [6] KOREL F, LUZURIAGA D A, BALABAN M. Objective quality assessment of raw tilapia (Oreochromis niloticus) fillets using electronic nose and machine vision[j]. Journal of Food Science, 2001, 66(7): [7] BALASUBRAMANIAN S, PANIGRAHI S, LOGUE C M, et al. Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification[j]. Journal of Food Engineering, 2009, 91(1): [8] DU W X, LIN C M, HUANG T, et al. Potential application of the electronic nose for quality assessment of salmon fillets under various storage conditions[j]. Journal of Food Science, 2002, 67(1): [9],,,. [J]., 2013, 26(10): [10],,,. [J]., 2014, 30(4): [11],,,. [J]., 2012, 33(20): [12]. GB/T [S]. :, [13]. SC/T [S]. :, [14] FAUSTMAN C, SPECHT S M, MALKUS L A, et al. Pigment oxidation in ground veal: influence of lipid oxidation, iron and zinc[j]. Meat Science, 1992, 31(3): [15] HUANG Lin, ZHAO Jiewen, CHEN Quansheng, et al. Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques[j]. Food Chemistry, 2014, 145: [16] HAI Z, WANG J. Electronic nose and data analysis for detection of maize oil adulteration in sesame oil[j]. Sensors and Actuators B: Chemical, 2006, 119(2): [17]. GB [S]. :, [18],,,. [J]., 2013, 29(1): [19]. [D]. :, [20]. [D]. :, [21],,,. [J]., 2009, 30(20): [22] BARBRI N E, AMARI A, VINAIAIXA M, et al. Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage[j]. Sensors and Actuators B: Chemical, 2007, 128(1): [23],. [J]., 2006, 22(12): [24],,,. [J]., 2013, 40(11): [25]. [D]. :, 2012.

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