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:: Volume 15, Issue 1 (Spring 2020) ::
Iranian J Nutr Sci Food Technol 2020, 15(1): 113-122 Back to browse issues page
Nondestructive Determination of the Total Volatile Basic Nitrogen (TVB-N) Content Using hyperspectral Imaging in Japanese Threadfin Bream (Nemipterusjaponicus) Fillet
S Khoshnoudi-Nia , M Moosavi-Nasab *
Shiraz university , marzieh.moosavi-nasab@mail.mcgill.ca
Abstract:   (3004 Views)
Background and Objectives: Considering the importance of safety evaluation of fish and seafood from capture to purchase, rapid and nondestructive methods are in urgent need for seafood industry. This study aimed to assess the application of hyperspectral imaging (HSI: 430-1010 nm) for prediction of total volatile basic nitrogen (TVB-N) in Japanese-threadfin bream (Nemipterusjaponicus) fillets, as a marine fish, during 8 days of cold storage (4±2°C).

 Materials & Methods: Hyperspectral imaging data and TVB-N value of the fillets were obtained in the laboratory. The basic prediction model was established based on Back-propagation artificial neural network (BP-ANN). To simplify the calibration models, 10 wavelengths were selected based on regression-coefficient (RC). Multiple-linear regression (MLR) and BP-ANN models were established based on the selected wavelengths.

Results: In full spectral range, the BP-ANN models exhibited relatively weak prediction performance (R2P=0.76 and RMSEP=4.45). After selecting 10 wavebands, the capability of the simplified models was better than that of the full-wavebands. The predictive power of simplified BP-ANN was better than that of MLR model (R2P(RC-BP-ANN)=0.820; RMSEPRC-BP-ANN=3.79 and R2P(RC-MLR)=0.794 and RMSEPRC-MLR=4.25). Therefore, r RC-BP-ANN model showed  more acceptable predictive performance (0.82 R2P 0.90).

Conclusion: Although the effectiveness of the developed simple multispectral imaging system based on BP-ANN model showed promising results to predict the TVB-N values of fillets, it did not show a strong prediction power of TVB-N values during storage. Therefore, further researches are required to enhance the prediction power and suitability of HSI method to evaluate TVB-N value in Japanese threadfin bream fish.
 
Keywords: Chemometric analysis, Total volatile basic nitrogen, Hyperspectral imaging, Japanese threadfin bream
Full-Text [PDF 791 kb]   (1552 Downloads)    
Article type: Research | Subject: Food Science
Received: 2019/04/12 | Accepted: 2019/10/16 | Published: 2020/03/25
References
1. Cheng J-H, Sun D-W. Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: current research and potential applications. Trends Food Sci Tech. 2014;37(2):78-91. [DOI:10.1016/j.tifs.2014.03.006]
2. Folkestad A, Wold JP, Rørvik K-A, Tschudi J, Haugholt KH, Kolstad K, et al. Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquac. 2008;280(1-4):129-35. [DOI:10.1016/j.aquaculture.2008.04.037]
3. Pena EA, Ridley LM, Murphy WR, Sowa JR, Bentivegna CS. Detection of polycyclic aromatic hydrocarbons (PAHs) in raw menhaden fish oil using fluorescence spectroscopy: Method development. Environ Toxicol Chem. 2015;34(9):1946-58. [DOI:10.1002/etc.3015]
4. Hu J, Li D, Duan Q, Han Y, Chen G, Si X. Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput Electron Agric. 2012;88:133-40. [DOI:10.1016/j.compag.2012.07.008]
5. Shi C, Qian J, Han S, Fan B, Yang X, Wu X. Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia (Oreochromis niloticus) pupil and gill color during storage at 4° C. Food Chem. 2018;243:134-40. [DOI:10.1016/j.foodchem.2017.09.047]
6. Dai Q, Cheng J-H, Sun D-W, Zeng X-A. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review. Crit Rev Food Sci Nutr. 2015;55(10):1368-82. [DOI:10.1080/10408398.2013.871692]
7. Cheng J-H, Sun D-W, Qu J-H, Pu H-B, Zhang X-C, Song Z, et al. Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. J Food Eng. 2016;182:9-17. [DOI:10.1016/j.jfoodeng.2016.02.004]
8. Cheng J-H, Sun D-W. Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT-food Sci Technol. 2015;62(2):1060-8. [DOI:10.1016/j.lwt.2015.01.021]
9. Cheng J-H, Sun D-W. Data fusion and hyperspectral imaging in tandem with least squares-support vector machine for prediction of sensory quality index scores of fish fillet. LWT-Food Sci Technol. 2015;63(2):892-8. [DOI:10.1016/j.lwt.2015.04.039]
10. Khojastehnazhand M, Khoshtaghaza MH, Mojaradi B, Rezaei M, Goodarzi M, Saeys W. Comparison of Visible-Near infrared and short wave infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Res Int. 2014;56:25-34. [DOI:10.1016/j.foodres.2013.12.018]
11. Cheng J-H, Sun D-W, Zeng X-A, Pu H-B. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innov Food Sci Emerg Technol. 2014;21:179-87. [DOI:10.1016/j.ifset.2013.10.013]
12. Dai Q, Cheng J-H, Sun D-W, Zhu Z, Pu H. Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chem. 2016;197:257-65. [DOI:10.1016/j.foodchem.2015.10.073]
13. Russell BC. A review of the threadfin breams of the genusNemipterus (Nemipteridae) from Japan and Taiwan, with description of a new species. JPN J ICHTHYOL. 1993;39(4):295-310. [DOI:10.1007/BF02905130]
14. Dantas Filho HA, Galvao RKH, Araújo MCU, da Silva EC, Saldanha TCB, José GE, et al. A strategy for selecting calibration samples for multivariate modelling. Chemometr Intell Lab Syst. 2004;72(1):83-91. [DOI:10.1016/j.chemolab.2004.02.008]
15. Khoshnoudi-Nia S, Moosavi-Nasab M, Nassiri SM, Azimifar Z. Determination of Total Viable Count in Rainbow-Trout Fish Fillets Based on Hyperspectral Imaging System and Different Variable Selection and Extraction of Reference Data Methods. Food Anal Method. 2018;11(12):3481-94. [DOI:10.1007/s12161-018-1320-0]
16. Fan W, Chi Y, Zhang S. The use of a tea polyphenol dip to extend the shelf life of silver carp (Hypophthalmicthys molitrix) during storage in ice. Food Chem. 2008;108(1):148-53. [DOI:10.1016/j.foodchem.2007.10.057]
17. Wu D, Sun D-W. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review-Part I: Fundamentals. Innov Food Sci Emerg Technol. 2013;19:1-14. [DOI:10.1016/j.ifset.2013.04.014]
18. Yang Q, Sun D-W, Cheng W. Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process. J Food Eng. 2017;192:53-60. [DOI:10.1016/j.jfoodeng.2016.07.015]
19. Xiong Z, Sun D-W, Xie A, Han Z, Wang L. Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem. 2015;175:417-22. [DOI:10.1016/j.foodchem.2014.11.161]
20. Sun D-W. editor. Hyperspectral imaging for food quality analysis and control. Amsterdam : Elsevier; 2010. p 496
21. Lakshmanan P. editor. Fish spoilage and quality assessment. India: The Central Institute of Fisheries Technology (CIFT). 2000. p. 26-40.
22. Zhu F, Zhang D, He Y, Liu F, Sun D-W. Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen-thawed fish fillets. Food Bioprocess Tech. 2013;6(10):2931-7. [DOI:10.1007/s11947-012-0825-6]
23. Abtahi S, Aminlari M. Effect of sodium sulfite, sodium bisulfite, cysteine, and pH on protein solubility and sodium dodecyl sulfate− polyacrylamide gel electrophoresis of soybean milk base. J Agric Food Chem. 1997;45(12):4768-72. [DOI:10.1021/jf970035r]
24. Iqbal A, Sun D-W, Allen P. Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. J Food Eng. 2013;117(1):42-51. [DOI:10.1016/j.jfoodeng.2013.02.001]
25. Klaypradit W, Kerdpiboon S, Singh RK. Application of artificial neural networks to predict the oxidation of menhaden fish oil obtained from Fourier transform infrared spectroscopy method. Food Bioprocess Tech. 2011;4(3):475-80. [DOI:10.1007/s11947-010-0386-5]
26. Jouki M, Yazdi FT, Mortazavi SA, Koocheki A, Khazaei N. Effect of quince seed mucilage edible films incorporated with oregano or thyme essential oil on shelf life extension of refrigerated rainbow trout fillets. Int J Food Microbiol. 2014;174:88-97. [DOI:10.1016/j.ijfoodmicro.2014.01.001]
27. Cheng J-H, Sun D-W, Wei Q. Enhancing visible and near-infrared hyperspectral imaging prediction of TVB-N level for fish fillet freshness evaluation by filtering optimal variables. Food Anal method. 2017;10(6):1888-98 [DOI:10.1007/s12161-016-0742-9]
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Khoshnoudi-Nia S, Moosavi-Nasab M. Nondestructive Determination of the Total Volatile Basic Nitrogen (TVB-N) Content Using hyperspectral Imaging in Japanese Threadfin Bream (Nemipterusjaponicus) Fillet. Iranian J Nutr Sci Food Technol 2020; 15 (1) :113-122
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Volume 15, Issue 1 (Spring 2020) Back to browse issues page
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