Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy

被引:33
|
作者
Viljoen, M
Hoffman, LC
Brand, TS
机构
[1] Elsenburg Agr Res Ctr, Dept Anim Sci, ZA-7607 Elsenburg, South Africa
[2] Univ Stellenbosch, Dept Anim Sci, ZA-7602 Matieland, South Africa
[3] Univ Pretoria, Dept Anat & Physiol, ZA-0110 Onderstepoort, South Africa
关键词
chemical composition; near infrared reflectance spectroscopy; ostrich meat;
D O I
10.1016/j.meatsci.2004.07.008
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Near infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of freeze-dried ostrich meat samples. Tenderloin (M. ambiens), big drum (M. iliofibularis) and fan fillet (M. gastrocnemius) samples (n = 160) were included in the study. Samples were minced, freeze-dried and analysed according to standard laboratory procedures for ash, dry matter (DM), crude protein (CP) and fat content. Samples were scanned (1100-2500 nm) and partial least-square regression (PLSR) was used to predict the chemical composition. Multiple correlation coefficients (r) and standard errors of calibration (SEC) for the chemical analysis of freeze-dried ostrich meat were: ash (0.72; 0.29%); DM (0.72; 1.01%); CP (0.98; 0.55%); and fat (0.99; 0.29%). The r values for the validation set and the standard error of performance (SEP) for the different constituents were: ash (0.71; 0.23%); DM (0.84; 0.72%); CP (0.97; 0.64%); and fat (0.99; 0.18%). Calibrations were accurate for CP and fat. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:255 / 261
页数:7
相关论文
共 50 条
  • [41] PREDICTION OF BOTANICAL COMPOSITION IN GRASSLAND HERBAGE SAMPLES BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY
    GARCIACRIADO, B
    GARCIACIUDAD, A
    PEREZCORONA, ME
    [J]. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 1991, 57 (04) : 507 - 515
  • [42] Prediction of composition and ruminal degradability characteristics of barley straw by near infrared reflectance spectroscopy
    Mathison, GW
    Hsu, H
    Soofi-Siawash, R
    Recinos-Diaz, G
    Okine, EK
    Helm, J
    Juskiw, P
    [J]. CANADIAN JOURNAL OF ANIMAL SCIENCE, 1999, 79 (04) : 519 - 523
  • [43] Prediction of chemical composition and geographical origin traceability of Chinese export tilapia fillets products by near infrared reflectance spectroscopy
    Liu, Yuan
    Ma, Dong-hong
    Wang, Xi-chang
    Liu, Li-ping
    Fan, Yu-xia
    Cao, Jin-xuan
    [J]. LWT-FOOD SCIENCE AND TECHNOLOGY, 2015, 60 (02) : 1214 - 1218
  • [44] Predicting the chemical composition of intact kernels in maize hybrids by near infrared reflectance spectroscopy
    Wei, LR
    Jiang, HY
    Li, JH
    Yan, YL
    Dai, JR
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25 (09) : 1404 - 1407
  • [45] DETERMINATION OF CHEMICAL-COMPOSITION OF CAROB PODS BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY
    ALBANELL, E
    PLAIXATS, J
    CAJA, G
    [J]. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 1993, 63 (03) : 309 - 312
  • [46] Prediction of chemical composition in sunflower whole plant and silage (Helianthus annus L.) by near infrared reflectance spectroscopy
    Fassio, A.
    Gimenez, A.
    Fernandez, E.
    Martins, D. Vaz
    Cozzolino, D.
    [J]. JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2007, 15 (03) : 201 - 207
  • [47] Prediction of silage digestibility by near infrared reflectance spectroscopy
    Liu, X.
    Han, L.
    Yang, Z.
    Xu, Ch.
    [J]. JOURNAL OF ANIMAL AND FEED SCIENCES, 2008, 17 (04): : 631 - 639
  • [48] Prediction of the intramuscular connective tissue components of fresh and freeze-dried samples by near infrared spectroscopy
    Andueza, D.
    Picard, F.
    Hocquette, J. F.
    Listrat, A.
    [J]. MEAT SCIENCE, 2021, 179
  • [49] Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review
    Prieto, N.
    Roehe, R.
    Lavin, P.
    Batten, G.
    Andres, S.
    [J]. MEAT SCIENCE, 2009, 83 (02) : 175 - 186
  • [50] Identification of animal meat muscles by visible and near infrared reflectance spectroscopy
    Cozzolino, D
    Murray, I
    [J]. LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY, 2004, 37 (04): : 447 - 452