On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy

被引：44
作者：
Pullanagari, Reddy R. [1 ]
Yule, Ian J. [1 ]
Agnew, M. [2 ]
机构：
[1] Massey Univ, IAE, Dept Soil & Earth Sci, Palmerston North 11222, New Zealand
[2] AgResearch, Hamilton 3123, New Zealand
来源：
MEAT SCIENCE | 2015年 / 100卷
关键词：
Vis-NIRS; Lamb; Fatty acid; Chemometrics; CHEMICAL-COMPOSITION; GENETIC ALGORITHMS; FEATURE-SELECTION; PLS-REGRESSION; MEAT; QUALITY; PROFILE; BEEF; WEIGHT; CATTLE;
D O I：
10.1016/j.meatsci.2014.10.008
中图分类号：
TS2 [食品工业];
学科分类号：
0832 ;
摘要：
This study investigated the potential of visible near infrared spectroscopy (Vis-NIRS) to quantify the fatty acid (FA) composition of lamb meat under commercial abattoir conditions. Genetic algorithm based partial least squares (PLS) were used to develop regression models for predicting individual FA and FA groups such as saturated FA (SFA), monounsaturated FA (MUFA) and polyunsaturated FA (PUFA). Overall, the majority of the FA (C14:0, C16:0, C16:1, C17:0, C18:1 c9, C18:1 c11, C18:2 n - 6, C18:2 c9 t11 and C18:1 tin intramuscular fat (IMF) and all FA groups were predicted with an R-CV(2), the squared correlation between observed and cross validated predicted values, which ranged between 0.60 and 0.74 and ratio prediction to deviation (RPD) values between 1.60 and 2.24. However the results for the remaining FA (C17:1, C18:0, C18:3 n-3, C20:4, C20:5, C22:5, C22:6) were unsatisfactory (R-2 = 0.35-0.57, RPD = 0.76-1.49). This indicates that Vis-NIRS could be used as an on-line tool to predict a number of FA. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码：156 / 163
页数：8
相关论文
共 38 条
  • [31] Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy
    Sierra, V.
    Aldai, N.
    Castro, P.
    Osoro, K.
    Coto-Montes, A.
    Olivan, M.
    [J]. MEAT SCIENCE, 2008, 78 (03) : 248 - 255
  • [32] Williams P., 2001, Near-infrared technology: in the agricultural and food industries, V2nd
  • [33] Windham WR, 1998, JOURNAL OF NEAR INFRARED SPECTROSCOPY, VOL 6 1998, P229
  • [34] PLS-regression:: a basic tool of chemometrics
    Wold, S
    Sjöström, M
    Eriksson, L
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2001, 58 (02) : 109 - 130
  • [35] Fat deposition, fatty acid composition and meat quality: A review
    Wood, J. D.
    Enser, M.
    Fisher, A. V.
    Nute, G. R.
    Sheard, P. R.
    Richardson, R. I.
    Hughes, S. I.
    Whittington, F. M.
    [J]. MEAT SCIENCE, 2008, 78 (04) : 343 - 358
  • [36] Effects of fatty acids on meat quality: a review
    Wood, JD
    Richardson, RI
    Nute, GR
    Fisher, AV
    Campo, MM
    Kasapidou, E
    Sheard, PR
    Enser, M
    [J]. MEAT SCIENCE, 2004, 66 (01) : 21 - 32
  • [37] Determination of fatty acids in broiler breast meat by near-infrared reflectance spectroscopy
    Zhou, L. J.
    Wu, H.
    Li, J. T.
    Wang, Z. Y.
    Zhang, L. Y.
    [J]. MEAT SCIENCE, 2012, 90 (03) : 658 - 664
  • [38] Application of NIRS for predicting fatty acids in intramuscular fat of rabbit
    Zomeno, C.
    Juste, V.
    Hernandez, P.
    [J]. MEAT SCIENCE, 2012, 91 (02) : 155 - 159
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