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Combined application of electronic nose analysis and back-propagation neural network and random forest models for assessing yogurt flavor acceptability

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Abstract

Flavor acceptability is an important aspect of evaluating the quality of food products. Rapid flavor measurements and the detection of unsatisfactory products are thus necessary for the quality control of yogurt. To better evaluate the flavor acceptability of yogurt, this study used a method based on an electronic nose and nonlinear chemometric back-propagation neural network (BPNN) and random forest (RF) models. Initially, principal component analysis was applied to visualize the quality distribution of a set of yogurt samples, but it failed to distinguish between the satisfactory and unsatisfactory samples. However, the BPNN and RF models clearly discriminated between the two sample types, with accuracy values close to 100%. The RF model achieved better discrimination than the BPNN model, with an accuracy of 93.75% for three subsets of the samples with unsatisfactory flavor. In summary, the combination of an electronic nose and a nonlinear chemometric model is an effective system for the assessment of yogurt flavor acceptability.

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  • 29 April 2020

    The original version of the article unfortunately contained an error in one of the affiliations of the first author, Dr. Huaixiang Tian. The institution name was incorrectly published as ���State Key Laboratory of Technology��� and the corrected name is ���State Key Laboratory of Dairy Biotechnology���.

References

  1. Z. Wei, W. Zhang, Y. Wang, J. Wang, J. Food Eng. 203, 41–52 (2017). https://doi.org/10.1016/j.jfoodeng.2017.01.022

    Article  CAS  Google Scholar 

  2. C. Hefa, J. Crit. Rev. Food Sci. 50, 938–950 (2010). https://doi.org/10.1080/10408398.2013.814044

    Article  CAS  Google Scholar 

  3. S. Ozturkoglu-Budak, C. Akal, A. Yetisemiyen, J. Dairy Sci. (2016). https://doi.org/10.3168/jds.2016-11217

    Article  Google Scholar 

  4. S. Xu, T.D. Boylston, B.A. Glatz, J. Food Sci. 71, C275–C280 (2010). https://doi.org/10.1021/jf051030u

    Article  CAS  Google Scholar 

  5. W. Routray, H.N. Mishra, J. Compr. Rev. Food Sci. Food Saf. 10, 208–220 (2011). https://doi.org/10.1111/j.1541-4337.2011.00151.x

    Article  CAS  Google Scholar 

  6. A.Z. Berna, J. Lammertyn, S. Saevels, C.D. Natale, B.M. Nicolaı̈, J. Sens. Actuators B 97, 324–333 (2004). https://doi.org/10.1016/j.snb.2003.09.020

    Article  CAS  Google Scholar 

  7. S. Heather, C. Daniel, J. Chem. Rev. 113, 1429–1440 (2013). https://doi.org/10.1021/cr300076c

    Article  CAS  Google Scholar 

  8. Q. Shanshan, W. Jun, G. Liping, J. Agric. Food Chem. 62, 6426–6434 (2014). https://doi.org/10.1021/jf501468b

    Article  CAS  Google Scholar 

  9. R.N. Bleibaum, H. Stone, T. Tan, S. Labreche, E. Saint-Martin, S. Isz, J. Food Qual. Prefer. 13, 409–422 (2002). https://doi.org/10.1016/S0950-3293(02)00017-4

    Article  Google Scholar 

  10. B. Pradhan, S. Lee, J. Environ. Model. Softw. 7, 13–30 (2010). https://doi.org/10.1016/j.envsoft.2009.10.016

    Article  Google Scholar 

  11. O. Gursoy, P. Somervuo, T. Alatossava, J. Food Eng. 92, 202–207 (2009). https://doi.org/10.1016/j.jfoodeng.2008.11.002

    Article  Google Scholar 

  12. W. Hao, T. Yue, Z. Xu, Z. Chen, J. Anal. Methods UK 9, 921–928 (2017). https://doi.org/10.1039/C6AY02610A

    Article  CAS  Google Scholar 

  13. R. Karoui, J.D. Baerdemaeker, J. Food Chem. 102, 621–640 (2007). https://doi.org/10.1016/j.foodchem.2006.05.042

    Article  CAS  Google Scholar 

  14. M. Navrátil, A. Christian Cimander, C.F. Mandenius, J. Agric. Food Chem. 52, 415–420 (2004). https://doi.org/10.1021/jf0304876

    Article  CAS  PubMed  Google Scholar 

  15. H. Tian, Y. Shen, H. Yu, Y. He, C. Chen, J. Food Sci. 82(7), 1693–1701 (2017). https://doi.org/10.1111/1750-3841.13779

    Article  CAS  PubMed  Google Scholar 

  16. Radi, S. Ciptohadijoyo, W.S. Litananda, M. Rivai, M.H. Purnomo, J. Comput. Electron. Agric. 121, 429–435 (2016). https://doi.org/10.1016/j.compag.2015.11.013

    Article  Google Scholar 

  17. A. Gliszczyńska-Świgło, J. Chmielewski, J. Food Anal. Method 10(6), 1800–1816 (2016). https://doi.org/10.1007/s12161-016-0739-4

    Article  Google Scholar 

  18. H.Y. Yu, X.Y. Niu, H.J. Lin, Y.B. Ying, B.B. Li, X.X. Pan, J. Food Chem. 113, 291–296 (2015). https://doi.org/10.1016/j.foodchem.2008.06.083

    Article  CAS  Google Scholar 

  19. A. Sofu, F.Y. Ekinci, J. Dairy Sci. 90, 3118–3125 (2007). https://doi.org/10.3168/jds.2006-591

    Article  CAS  PubMed  Google Scholar 

  20. A.K. Bhatt, D. Pant, J. AI Soc. 30, 45–56 (2013). https://doi.org/10.1007/s00146-013-0516-5

    Article  Google Scholar 

  21. J.A. Hernández, J. Food Control 20, 435–438 (2009). https://doi.org/10.1016/j.foodcont.2008.07.005

    Article  Google Scholar 

  22. M.S. Zenoozian, S. Devahastin, J. Food Eng. 90, 219–227 (2009). https://doi.org/10.1016/j.jfoodeng.2008.06.033

    Article  Google Scholar 

  23. T.K. Ho, International Conference on Document Analysis and Recognition, 1995. http://doi.org/10.1109/ICDAR.1995.598994

  24. C. Zhang, Y. Ma, Ensemble Mach. Learn. (2012). https://doi.org/10.1007/978-1-4419-9326-7

    Article  Google Scholar 

  25. L. Breiman, Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  26. N. Wang, J. Li, Y. Liu, Z. Zhu, J.X. Su, C. Peng, Adv. Hybrid Inf. Process. (2017). https://doi.org/10.1007/978-3-319-73317-3_19

    Article  Google Scholar 

  27. J. Broséus, M. Vallat, P. Esseiva, J. Chemom. Intell. Lab. 107, 343–350 (2011). https://doi.org/10.1016/j.chemolab.2011.05.004

    Article  CAS  Google Scholar 

  28. M. Pardo, G. Sberveglieri, J. Sens. Actuators B 131, 93–99 (2008). https://doi.org/10.1016/j.snb.2006.09.041

    Article  CAS  Google Scholar 

  29. S. Qiu, J. Wang, T. Chen, D. Du, J. Food Eng. 166, 193–203 (2015). https://doi.org/10.1016/j.jfoodeng.2015.06.007

    Article  Google Scholar 

  30. F.F. Ai, J. Bin, Z.M. Zhang, J.H. Huang, J.B. Wang, Y.Z. Liang, L. Yu, Z.Y. Yang, J. Food Chem. 143, 472–478 (2014). https://doi.org/10.1016/j.foodchem.2013.08.013

    Article  CAS  Google Scholar 

  31. X. Shao, H. Li, N. Wang, Q. Zhang, J. Sens. Basel 15, 26726–26742 (2015). https://doi.org/10.3390/s151026726

    Article  CAS  Google Scholar 

  32. L. Miao, M. Wang, J. Wang, D. Li, J. Sens. Actuators B 177, 970–980 (2013). https://doi.org/10.1016/j.snb.2012.11.071

    Article  CAS  Google Scholar 

  33. M.W. Ahmad, M. Mourshed, Y. Rezgui, J. Energy Build. (2017). https://doi.org/10.1016/j.enbuild.2017.04.038

    Article  Google Scholar 

  34. ISO 22935-1, Milk and Milk Products—Sensory Analysis—Part 1: General Guidance for the Recruitment, Selection, Training and Monitoring of Assessors (International Organization for Standardization, ISO, Geneva, 2013)

  35. ISO 22935-3, Milk and Milk Products—Sensory Analysis—Part 3: Guidance on a Method for Evaluation of Compliance with Product Specifications for Sensory Properties by Scoring (International Organization for Standardization, ISO, 2013)

  36. M.A. Drake, K.R. Cadwallader, M.E.C. Whetstine, J. ACS Symp. Ser. 971, 51–77 (2007). https://doi.org/10.1021/bk-2007-0971.ch003

    Article  CAS  Google Scholar 

  37. B. Wang, S. Xu, D.W. Sun, J. Food Res. Int. 43, 255–262 (2010). https://doi.org/10.1016/j.foodres.2009.09.018

    Article  CAS  Google Scholar 

  38. H. Tian, F. Li, L. Qin, H. Yu, X. Ma, J. Food Anal. Method 8, 1522–1534 (2015). https://doi.org/10.1007/s12161-014-0031-4

    Article  Google Scholar 

  39. D.C. Duro, S.E. Franklin, M.G. Dubé, J. Int. J. Remote Sens. 33, 4502–4526 (2012). https://doi.org/10.1080/01431161.2011.649864

    Article  Google Scholar 

  40. A. Palczewska, J. Palczewski, R.M. Robinson, D. Neagu, IEEE 14th International Conference on Information Reuse and Integration (IRI), 2013. http://doi.org/10.1007/978-3-319-04717-1_9

  41. B. Gregorutti, B. Michel, P. Saint-Pierre, J. Stat. Comput. 27, 659–678 (2013). https://doi.org/10.1007/s11222-016-9646-1

    Article  Google Scholar 

  42. G. Biau, E. Scornet, J. Test. 25, 1–31 (2016). https://doi.org/10.1007/s11749-016-0481-7

    Article  Google Scholar 

  43. L. Breiman, J. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1007/s11749-016-0481-7

    Article  Google Scholar 

  44. J. Wang, D. Gao, Z. Wang, J. Meas. Sci. Technol. 26, 085005 (2015). https://doi.org/10.1088/0957-0233/26/8/085005

    Article  CAS  Google Scholar 

  45. H. Cheng, J. Crit. Rev. Food Sci. 50, 938–950 (2010). https://doi.org/10.1080/10408390903044081

    Article  CAS  Google Scholar 

  46. J. Cheng, W. Xin, T. Si, Z. Fan, Z. Wang, J. Zhou, K. Cen, J. Fuel 172, 170–177 (2016). https://doi.org/10.1016/j.fuel.2016.01.035

    Article  CAS  Google Scholar 

  47. S. Wei, Y. Xu, J. Clean. Prod. 112, 1282–1291 (2016). https://doi.org/10.1016/j.jclepro.2015.04.097

    Article  CAS  Google Scholar 

  48. W. Liu, J. Yu, Z. Sun, Y. Song, X. Wang, H. Wang, T. Wuren, M. Zha, B. Menghe, H. Zhang, J. Dairy Sci. 99, 89–103 (2016). https://doi.org/10.3168/jds.2015-10209

    Article  CAS  Google Scholar 

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Acknowledgements

This work was sponsored by the Open Project Program of State Key Laboratory of Dairy Biotechnology (No. SKLDB2017-001), “Shu Guang” Project (No. 16SG50) supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation and Shanghai Rising-Star Program (No. 17QB1404200).

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Authors and Affiliations

  1. State Key Laboratory of Technology, Shanghai Engineering Research Center of Dairy Biotechnology, Dairy Research Institute, Bright Dairy & Food Co., Ltd, Shanghai, China

    Huaixiang Tian

  2. Department of Food Science and Technology, Shanghai Institute of Technology, 100 Haiquan Road, Shanghai, 201418, China

    Huaixiang Tian, Han Liu, Yujie He, Bin Chen, Haiyan Yu & Chen Chen

  3. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, 201418, China

    Lizhong Xiao, Yugang Fei & Guangzhong Wang

  4. Shanghai Research Institute of Fragrance & Food Flavor, Shanghai, 200232, China

    Chen Chen

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  1. Huaixiang Tian
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Tian, H., Liu, H., He, Y. et al. Combined application of electronic nose analysis and back-propagation neural network and random forest models for assessing yogurt flavor acceptability. Food Measure 14, 573–583 (2020). https://doi.org/10.1007/s11694-019-00335-w

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  • DOI: https://doi.org/10.1007/s11694-019-00335-w

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