MIV波长优选改善VIS/NIR光谱TVB-N模型性能研究

doi:10.3964/j.issn.1000-0593(2020)05-1413-07

Abstract
References
Related Citation (15)

Download: PDF (4125 KB)
Export: BibTeX | EndNote (RIS)

Abstract Volatile Basic Nitrogen (TVB-N) is an important physicochemical property for the detection of meat freshness. Using visible/near-infrared (VIS/NIR) spectroscopy to analyze TVB-N content is of great importance quantitatively-. The prediction model is the key factor for detection TVB-N content in visible or near infrared spectroscopy. Thus, an accurate and robust prediction model can improve the quantitative analysis results of TVB-N. Firstly, we collected 51 representative pork samples with different freshness, and determine the effective band from 450 to 900 nm after removing low signal-to-noise ratio band from 200 to 450 nm and from 900 to 1 000 nm. Then we use principal component analysis (PCA) to reduce spectral data in order to construct a back propagation neural network (BPNN) model. On this basis, we use the mean impact value (MIV) method to select characteristic wavelengths which strongly related to the content of Total Volatile Basic Nitrogen (TVB-N) in edible meat, and finally construct a MIV-PCA-BPNN prediction model based on 221 selected wavelengths. Experimental results show that the related coefficient of calibration (R_C), the related coefficient of prediction (R_P), the root means square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the robustness index of the PCA-BPNN model are 0.96, 0.93, 1.47 mg/100 g, 1.74 mg/100 g and 1.18, respectively. The PCA-BPNN nonlinear prediction model is better than the classical linear prediction model principal component regression and partial least squares regression prediction model, which proves that TVB-N has strong nonlinear effects. The R_C, R_P, RMSEC, RMSEP and the robustness index of the MIV-PCA-BPNN model are 0.98, 0.96, 1.12 mg/100 g, 1.21 mg/100 g and 1.08, respectively, it is RMSEC and RMSEP are the smallest, while R_C, R_P are the largest. Therefore, MIV-PCA-BPNN is the most accurate and robust model in all constructed prediction model. In addition, the characteristic wavelengths selected by the MIV method are concentrated near 7 peaks, which are distributed in the absorption regions of chemical composition in meat. The characteristic wavelengths are consistent with the absorption peaks of H Contained Groups in TVB-N, which provides a theoretical basis for selecting wavelengths by the MIV method. It is found that the MIV wavelength selection is effective to improve the performance of the prediction model, which offers new thought for using the neural network to eliminate irrelevant wavelength variables. The MIV-PCA-BPNN prediction model could be used for the quantitative analysis of TVB-N in meat.

Key words：Visible/near-infrared spectroscopy; Back propagation neural network (BPNN); Wavelength selection; Total volatile basic nitrogen (TVB-N)

Received: 2019-04-17 Accepted: 2019-08-28

ZTFLH:

O433.4

Corresponding Authors: YANG Chun-yong E-mail: cyyang@mail.scuec.edu.cn

Cite this article:

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)05-1413-07 OR http://www.gpxygpfx.com/EN/Y2020/V40/I05/1413

[1] Qiao L, Tang X, Dong J. Food Chemistry, 2017, 237: 1179.
[2] Qu F, Ren D, He Y, et al. Meat Science, 2018, 146: 59.
[3] Fan Y, Liao Y, Cheng F. International Journal of Food Properties, 2018, 21(1): 1180.
[4] SONG Xiang-zhong，TANG Guo，ZHANG Lu-da, et al(宋相中，唐果，张录达, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(4): 1048.
[5] Alamprese C, Casale M, Sinelli N, et al. LWT—Food Science and Technology, 2013, 53(1): 225.
[6] CAI Jian-rong，WAN Xin-min，CHEN Quan-sheng(蔡健荣，万新民，陈全胜). Acta Optica Sinica(光学学报), 2009, (10): 2808.
[7] Huang L, Zhao J, Chen Q, et al. Food Chemistry, 2014, 145: 228.
[8] Li H D, Liang Y Z, Long X X, et al. Chemometrics & Intelligent Laboratory Systems, 2013, 122(5): 23.
[9] YANG Mei-hua，ZHAO Xiao-min，FANG Qian, et al(杨梅花，赵小敏，方倩, 等). Scientia Agricultura Sinica(中国农业科学), 2014，(12): 2374.
[10] Dombi G W, Nandi P, Saxe J M, et al. Journal of Trauma and Acute Care Surgery, 1995, 39(5): 915.
[11] Tan X, Ji Z, Zhang Y. Technology & Health Care, 2018, 26(6): 1.
[12] Zhang Z, Yang J. International Journal of Oil, Gas and Coal Technology, 2016, 11(3): 279.
[13] Fan H, Xuan J, Du X, et al. RSC Advances, 2018, 8(69): 39602.
[14] WEI Wen-song，PENG Yan-kun，ZHENG Xiao-chun, et al(魏文松，彭彦昆，郑晓春, 等). Acta Optica Sinica(光学学报), 2017, 37(11): 382.
[15] YAN Yan-lu, CHEN Bin, ZHU Da-zhou, et al(严衍禄, 陈斌，朱大洲，等). Near Infrared Spectroscopy—Principles, Technologies and Applications(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2013. 123.
[16] Wu J, Peng Y, Li Y, et al. Journal of Food Engineering, 2012, 109(2): 267.
[17] Cheng J, Sun D, Qu J, et al. Journal of Food Engineering, 2016, 182: 9.
[18] ZOU Hao，TIAN Han-you，LIU Fei, et al(邹昊，田寒友，刘飞, 等). Food Science(食品科学), 2016, 37(22): 180.