Projects / Programmes source: ARIS

Predictive ability of NIR spectroscopy for pig meat quality evaluation

Research activity

Code Science Field Subfield
4.02.00  Biotechnical sciences  Animal production   

Code Science Field
B400  Biomedical sciences  Zootechny, animal husbandry, breeding 
T430  Technological sciences  Food and drink technology 
NIR spectroscopy, meat quality, pig, intramuscular fat; ryr1 gene
Evaluation (rules)
source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  05658  PhD Drago Babnik  Animal production  Researcher  2004 - 2007  369 
2.  11233  PhD Marjeta Čandek Potokar  Animal production  Head  2004 - 2007  791 
3.  01364  PhD Dejan Škorjanc  Biotechnical sciences  Researcher  2004 - 2007  435 
4.  14548  PhD Špela Velikonja Bolta  Chemistry  Researcher  2004 - 2007  445 
5.  22606  PhD Tomaž Žnidaršič  Animal production  Researcher  2004 - 2007  195 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0401  Agricultural institute of Slovenia  Ljubljana  5055431  20,185 
2.  0482  University of Maribor, Faculty of Agriculture and Life Sciences  Hoče  5089638004  9,966 
Inspite of intensive research on fast methods for meat quality evaluation, the practical use of such methods under industrial conditions in meat sector remains scarce. NIR spectroscopy is considered as being one of the most promising techniques. The objective of the present study is to evaluate the predictive value of NIR spectroscopy for pig meat quality evaluation, namely to make and evaluate calibrations for intramuscular fat content determination and technological quality of meat. Meat sampling will be adapted to achieve the necessary variation range for each parameter under study and selection based on the principal factors affecting the trait. Thus, for the intramuscular fat, the selection will be based on variation due to the breed (duroc genes), subcutaneus fat thickness, muscle type, whereas for the technological quality of meat, pigs of three genotypes will be selected (NN, Nn, nn) according to ryr1 gene mutation (alelle n) which is responsible for meat of low technological quality. Measurements of some meat quality traits (pH, water holding capacity, colour) and standard chemical analysis of intramuscular fat content will be performed. The reflectance spectra of all samples will be recorded in visual (408-1092 nm) and near-infrared (1108-2492 nm) spectra using NIRSystem 6500 (FOSS). Prediction ability of NIR spectroscopy for determination of intramuscular fat content will be performed on 140 samples (2 muscles, 70 pigs) and for technological quality of meat on 105 samples (105 pigs, 35 pigs per genotype, 1 muscle). Predictions based on spectral information will be made using WinISI statistical package; for linear data global calibration equations with cross-validation will be used. To discriminate between the genotypes NN, Nn, nn, the artificial neural network classifier will be used, a method available in WinISI statistical package.
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