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Fish meal freshness detection by GBDT based on a portable electronic nose system and HS-SPME–GC–MS

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Abstract

The volatile compounds of super fresh, superior fresh, general fresh, corrupt and completely corrupt fish meal were studied by headspace solid-phase microextraction–gas chromatography–mass spectrometry. Principal component analysis was used to analyze the sensor and gas chromatography-mass spectrometry data, so as to study the resolution of sensor array. A total of 198 fish meal samples with different freshness were classified by the gradient boosting decision tree method, yielding a model between the sensor array data and the freshness index, such as the acid value and the volatile base nitrogen value. The gradient boosting decision tree model shows that the correlation between the predicted acid value by electronic nose and the measured acid value is 0.90 and the correlation between the predicted volatile base nitrogen value and the measured volatile base nitrogen is 0.97. The combination of an electronic nose and a pattern recognition method based on a gas sensor can predict the acid value and volatile base nitrogen of the freshness index and can detect the freshness of fish meal.

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References

  1. Olsen RL, Hasan MR (2012) A limited supply of fishmeal: impact on future increases in global aquaculture production. Trends Food Sci Technol 27:120–128

    Article  CAS  Google Scholar 

  2. Ai Q, Mai K, Tan B, Xu W, Duan Q, Ma H, Zhang L (2006) Replacement of fish meal by meat and bone meal in diets for large yellow croaker, Pseudosciaena crocea. Aquaculture 260:255–263

    Article  Google Scholar 

  3. Macan J, Turk R, Vukusic J, Kipcic D, Milkovic-Kraus S (2006) Long-term follow-up of histamine levels in a stored fish meal sample. Anim Feed Sci Technol 127:169–174

    Article  CAS  Google Scholar 

  4. Kose S, Quantick P, Hall G (2003) Changes in the levels of histamine during processing and storage of fish meal. Anim Feed Sci Technol 107:161–172

    Article  CAS  Google Scholar 

  5. Deng Y, Luo Y, Wang Y (2015) Effect of different drying methods on the myosin structure, amino acid composition, protein digestibility and volatile profile of squid fillets. Food Chem 171:168–176

    Article  CAS  PubMed  Google Scholar 

  6. Duan Y, Zheng F, Chen H (2015) Analysis of volatiles in Dezhou Braised Chicken by comprehensive two-dimensional gas chromatography/high resolution-time of flight mass spectrometry. LWT Food Sci Technol 60:1235–1242

    Article  CAS  Google Scholar 

  7. Aro T, Tahvonen R, Koskinen L (2003) Volatile compounds of Baltic herring analysed by dynamic headspace sampling–gas chromatography–mass spectrometry. Eur Food Res Technol 216:483–488

    Article  CAS  Google Scholar 

  8. Parlapani FF, Mallouchos A, Haroutounian SA, Boziaris LS (2017) Volatile organic compounds of microbial and non-microbial origin produced on model fish substrate un-inoculated and inoculated with gilt-head sea bream spoilage bacteria. LWT Food Sci Technol 78:54–62

    Article  CAS  Google Scholar 

  9. Odeyemi OA, Burke CM, Bolch CJS, Stanley R (2018) Evaluation of spoilage potential and volatile metabolites production by Shewanella baltica isolated from modified atmosphere packaged live mussels. Food Res Int 103:415–425

    Article  CAS  PubMed  Google Scholar 

  10. Cuevas FJ, Moreno-Rojas JM, Ruiz-Moreno MJ (2017) Assessing a traceability technique in fresh oranges (Citrus sinensis L. Osbeck) with an HS-SPME-GC-MS method. Towards a volatile characterisation of organic oranges. Food Chem 221:1930–1938

    Article  CAS  PubMed  Google Scholar 

  11. Ziółkowska A, Wąsowicz E, Jeleń HH (2016) Differentiation of wines according to grape variety and geographical origin based on volatiles profiling using SPME-MS and SPME-GC/MS methods. Food Chem 213:714–720

    Article  PubMed  CAS  Google Scholar 

  12. Huang L, Zhao J, Chen Q, Zhang Y (2014) Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem 145:228–236

    Article  CAS  PubMed  Google Scholar 

  13. Grassi S, Benedetti S, Opizzio M, Nardo E, Buratti S (2019) Meat and fish freshness assessment by a portable and simplified electronic nose system (Mastersense). Sensors 19:3225–3231

    Article  CAS  Google Scholar 

  14. Ramírez HL, Soriano A, Gómez S (2018) Evaluation of the food sniffer electronic nose for assessing the shelf life of fresh pork meat compared to physicochemical measurements of meat quality. Eur Food Res Technol 244:1047–1055

    Article  CAS  Google Scholar 

  15. Min W, Fan G, Qian W (2018) The real-time assessment of food freshness in refrigerator based on miniaturized electronic nose. Anal Methods 10:4741–4749

    Article  Google Scholar 

  16. Jiang J, Li J, Zheng F (2016) Rapid freshness analysis of mantis shrimps (Oratosquilla oratoria) by using electronic nose. J Food Meas Charact 10:48–55

    Article  Google Scholar 

  17. Ezhilan M, Nesakumar N, Babu KJ (2018) An electronic Nose for royal delicious apple quality assessment—a tri-layer approach. Food Res Int 109:44–51

    Article  CAS  PubMed  Google Scholar 

  18. Rutolo MF, Clarkson JP, Covington JA (2018) The use of an electronic nose to detect early signs of soft-rot infection in potatoes. Biosyst Eng 167:137–143

    Article  Google Scholar 

  19. Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M (2016) Application of MOS based electronic nose for the prediction of banana quality properties. Measurement 82:105–114

    Article  Google Scholar 

  20. Hui G, Jin J, Deng S (2015) Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose. Food Chem 170:484–491

    Article  CAS  PubMed  Google Scholar 

  21. Radi CS, Litananda WS (2016) Electronic nose based on partition column integrated with gas sensor for fruit identification and classification. Comput Electron Agric 121:429–435

    Article  Google Scholar 

  22. Jiang S, Wang J, Wang Y (2017) A novel framework for analyzing MOS E-nose data based on voting theory: application to evaluate the internal quality of Chinese pecans. Sens Actuators B Chem 242:511–521

    Article  CAS  Google Scholar 

  23. Rodriguez SD, Barletta DA, Wilderjans TF, Bernik DL (2014) Fast and efficient food quality control using electronic noses: adulteration detection achieved by unfolded cluster analysis coupled with time-window selection. Food Anal Methods 7:2042–2050

    Article  Google Scholar 

  24. Timsorn K, Lorjaroenphon Y, Wongchoosuk C (2017) Identification of adulteration in uncooked jasmine rice by a portable low-cost artificial olfactory system. Measurement 108:67–76

    Article  Google Scholar 

  25. Xin R, Liu X, Wei C (2018) E-nose and GC-MS reveal a difference in the volatile profiles of white- and red-fleshed peach fruit. Sensors 18:765–776

    Article  CAS  Google Scholar 

  26. Tan J, Kerr WL (2018) Determining degree of roasting in cocoa beans by artificial neural network (ANN) based electronic nose system and gas chromatography/mass spectrometry (GC/MS). J Sci Food Agric 98:3851–3859

    Article  CAS  PubMed  Google Scholar 

  27. Pizzoni D, Compagnone D, Di Natale C (2015) Evaluation of aroma release of gummy candies added with strawberry flavours by gas-chromatography/mass-spectrometry and gas sensors arrays. J Food Eng 167:77–86

    Article  CAS  Google Scholar 

  28. Timsorn K, Thoopboochagorn T, Lertwattanasakul N, Wongchoosuk C (2016) Evaluation of bacterial population on chicken meats using a briefcase electronic nose. Biosyst Eng 151:116–125

    Article  Google Scholar 

  29. Asikin Y, Kusumiyati ST (2017) Volatile aroma components and MS-based electronic nose profiles of dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa). J Adv Res 9:79–85

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Capone S, Tufariello M, Francioso L (2013) Aroma analysis by GC/MS and electronic nose dedicated to Negroamaro and Primitivo typical Italian Apulian wines. Sens Actuators B Chem 179:259–269

    Article  CAS  Google Scholar 

  31. Lippolis V, Cervellieri S, Damascelli A (2018) Rapid prediction of deoxynivalenol contamination in wheat bran by MOS-based electronic nose and characterization of the relevant pattern of volatile compounds. J Sci Food Agric 98:4955–4962

    Article  CAS  PubMed  Google Scholar 

  32. GB/T19164–2003 Fish meal[S] (2003) Beijing: General Administration of quality supervision, inspection and Quarantine of the people's Republic of China

  33. GB/T5009.228-2016 (2016) National food safety standard—determination of volatile basic nitrogen in food[S]. National Health and Family Planning Commission of the people's Republic of China, Beijing

  34. Li P, Ren Z, Shao K, Tan H, Niu Z (2019) Research on distinguishing fish meal quality using different characteristic parameters based on electronic nose technology. Sensors 19:2146–2161

    Article  CAS  Google Scholar 

  35. Wang XR, Lizier JT, Berna AZ, Bravo FG, Trowell SC (2015) Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification. Sens Actuators B Chem 217:165–174

    Article  CAS  Google Scholar 

  36. Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:12–2831

    Article  Google Scholar 

  37. Papadopoulou OS, Panagou EZ, Mohareb FR (2013) Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis. Food Res Int 50:241–249

    Article  Google Scholar 

  38. Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M, Ahmadi H (2014) Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM). Czech J Food Sci 32:538–548

    Article  Google Scholar 

  39. Saidi T, Zaim O, Moufid M (2018) Exhaled breath analysis using electronic nose and gas chromatography–mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects. Sens Actuators B Chem 257:178–188

    Article  CAS  Google Scholar 

  40. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232

    Article  Google Scholar 

  41. Myles AJ, Feudale RN, Liu Y (2004) An introduction to decision tree modeling. J Chemom 18:275–285

    Article  CAS  Google Scholar 

  42. Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 521–529

  43. Yang Z, Xie J, Zhang L (2015) Aromatic effect of fat and oxidized fat on a meat-like model reaction system of cysteine and glucose. Flavour Fragrance J 30:320–329

    Article  CAS  Google Scholar 

  44. Lotfy SN, Fadel HHM, EI-Ghorab AH (2015) Stability of encapsulated beef-like flavourings prepared from enzymatically hydrolysed mushroom proteins with other precursors under conventional and microwave heating. Food Chem 187:7–13

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was funded by the Fundamental Research Funds for the Central Universities (2662018PY081).

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Authors and Affiliations

  1. College of Engineering, Huazhong Agricultural University, Wuhan, 430070, Hubei, China

    Pei Li, Jie Geng, Hongcheng Li & Zhiyou Niu

  2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan, 430070, Hubei, China

    Zhiyou Niu

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  1. Pei Li
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  2. Jie Geng
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Correspondence to Zhiyou Niu.

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Li, P., Geng, J., Li, H. et al. Fish meal freshness detection by GBDT based on a portable electronic nose system and HS-SPME–GC–MS. Eur Food Res Technol 246, 1129–1140 (2020). https://doi.org/10.1007/s00217-020-03462-7

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  • DOI: https://doi.org/10.1007/s00217-020-03462-7

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