Application of successive projections algorithm to nondestructive determination of pork pH value

被引：0
|
作者：
Liao Y. [1 ]
Fan Y. [1 ]
Cheng F. [1 ]
Wu X. [1 ]
机构：
[1] College of Biosystems Engineering and Food Science, Zhejiang University
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2010年 / 26卷 / SUPPL. 1期
关键词：
Feature selection; Meats; Near infrared spectroscopy; Nondestructive exzamination; pH of pork; Spectroscopy; Successive projections algorithm;
D O I：
10.3969/j.issn.1002-6819.2010.z1.067
中图分类号：
学科分类号：
摘要：
Using a few variables from the fresh pork spectra to construct the model of pH prediction is capable of decreasing of calculated amount, which has a vital significance to determine and monitor pork pH values. In this study, successive projection algorithm (SPA) was proposed to select feature wavelength from pork to determine the pH values. The performance of the model which constructed by variables selected of SPA was compared with various models including the model of partial least squares regression (PLSR) based on full spectrum (5000-10440 cm-1), the model constructed by variables selected of stepwise multiple linear regression (SMLR) and the model constructed by variables selected of genetic algorithm (GA). A total of 37 variables, only 2.6 percent in the full spectrum, selected by SPA were employed to construct the model with 0.870 as the correlation coefficient and 0.094 as the root of mean square error of calibration set and 0.892 as the correlation coefficient and 0.085 as the root of mean square error of validation set. While it was nearly to the PLSR model with preprocess of multiplicative signal correction, the SPA-MLR model is more accurate than SMLR model and GA model. The results confirm that SPA can be applied to select few variables from huge information space of NIR spectroscopy to build simple model to determine fresh pork pH values.
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页码：379 / 383
页数：4
相关论文
共 15 条
  • [1] Brewer M.S., Zhu L.G., Bidner B., Et al., Measuring pork color: Effects of bloom time, muscle, pH and relationship to instrumental parameters, Meat Science, 57, 2, pp. 169-176, (2001)
  • [2] Brewer M.S., Novakofski J., Freise K., Instrumental evaluation of pH effects on ability of pork chops to bloom, Meat Science, 72, 4, pp. 596-602, (2006)
  • [3] Holmer S.F., McKeith R.O., Boler D.D., Et al., The effect of pH on shelf-life of pork during aging and simulated retail display, Meat Science, 82, 1, pp. 86-93, (2009)
  • [4] Knox B.L., van Laack R.L.J.M., Davidson P.M., Relationships between ultimate pH and microbial, chemical, and physical characteristics of vacuum-packaged pork loins, Journal of Food Science, 73, 3, pp. 104-110, (2008)
  • [5] Prieto N., Roehe R., Lavin P., Et al., Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review, Meat Science, 83, 2, pp. 175-186, (2009)
  • [6] Savenije B., Geesink G.H., van der Palen J.G.P., Et al., Prediction of pork quality using visible/near-infrared reflectance spectroscopy, Meat Science, 73, 1, pp. 181-184, (2006)
  • [7] Anderson J.R., Borggaard C., Rasmussen A.J., Et al., Optical measurements of pH in meat, Meat Science, 53, 2, pp. 135-141, (1999)
  • [8] Chan D.E., Walker P.N., Mills E.W., Prediction of pork quality characteristics using visible and near-infrared spectroscopy, Transactions of the Asae, 45, 5, pp. 1519-1527, (2002)
  • [9] Chu X., Yuan H., Lu W., Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique, Progress in Chemistry, 16, 4, pp. 528-542, (2004)
  • [10] Pontes M.J.C., Galvao R.K.H., Araujo M.C.U., Et al., The successive projections algorithm for spectral variable selection in classification problems, Chemometrics and Intelligent Laboratory Systems, 78, 1-2, pp. 11-18, (2005)
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