Fresh Pork Quality Assessment by NIRS and NMR: Predicting Eating Quality and Elucidating Relationships with Key Chemical Components

The Australian pork industry has been seeking a rapid and non-destructive way to predict pork chemical components and eating quality. In this study, near-infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR) were applied to fresh pork <i>Longissimus thoracis et lumborum</i> (LTL) and <i>Semimembranosus</i> (SM) with the aim to build prediction models for intramuscular fat (IMF) content, collagen content and solubility, pH, and sensory attributes, namely tenderness, juiciness, liking of flavor and overall liking as well as investigate the effects of chemical components on pork eating quality. Results showed that the NIRS output, which was a predicted IMF content calibrated for the IMF of lamb, correlated with the chemically analyzed IMF content across both muscles. In LTL, NMR parameter <i>p<sub>2f</sub></i> was weakly correlated with IMF and pH. For the LTL, NMR parameters <i>p<sub>21</sub></i> and <i>p<sub>22</sub></i> were related to sensory tenderness, while <i>T<sub>22</sub></i> was correlated with the liking of flavor. In both muscles, the collagen content and pH were related to all sensory attributes, and IMF was related to the liking of flavor. The chemical properties of SM were weakly correlated with those of LTL. The NIRS and NMR weakly predicted the pork chemical components and sensory properties, but more studies are required to improve the accuracy.

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Publication Details

Journal
Animals
Published
2025-10-14
DOI
https://doi.org/10.3390/ani15202973
Citations
1
Primary Topic
Meat and Animal Product Quality
Type
article
Field-Weighted Citation Impact
1.19
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article

Fresh Pork Quality Assessment by NIRS and NMR: Predicting Eating Quality and Elucidating Relationships with Key Chemical Components

Cameron C. Steel, Robyn Dorothy Warner, Melindee Hastie, M.T. Corlett et al.
1 citations
Animals
Meat and Animal Product Quality
1.19
article

Fresh Pork Quality Assessment by NIRS and NMR: Predicting Eating Quality and Elucidating Relationships with Key Chemical Components

Cameron C. Steel, Robyn Dorothy Warner, Melindee Hastie, M.T. Corlett, Frank Rowland Dunshea, Xiying Li, P. McGilchrist, Evan R. McCarney, Minh Ha, R.J.E. Hewitt, D.W. Pethick, Sarah Stewart, D.N. D’Souza
article en
1 citations

Abstract

The Australian pork industry has been seeking a rapid and non-destructive way to predict pork chemical components and eating quality. In this study, near-infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR) were applied to fresh pork <i>Longissimus thoracis et lumborum</i> (LTL) and <i>Semimembranosus</i> (SM) with the aim to build prediction models for intramuscular fat (IMF) content, collagen content and solubility, pH, and sensory attributes, namely tenderness, juiciness, liking of flavor and overall liking as well as investigate the effects of chemical components on pork eating quality. Results showed that the NIRS output, which was a predicted IMF content calibrated for the IMF of lamb, correlated with the chemically analyzed IMF content across both muscles. In LTL, NMR parameter <i>p<sub>2f</sub></i> was weakly correlated with IMF and pH. For the LTL, NMR parameters <i>p<sub>21</sub></i> and <i>p<sub>22</sub></i> were related to sensory tenderness, while <i>T<sub>22</sub></i> was correlated with the liking of flavor. In both muscles, the collagen content and pH were related to all sensory attributes, and IMF was related to the liking of flavor. The chemical properties of SM were weakly correlated with those of LTL. The NIRS and NMR weakly predicted the pork chemical components and sensory properties, but more studies are required to improve the accuracy.

AnimalsVol. 15(20)
Statistics New Zealand (NZ), University of Leeds (GB), The University of Melbourne (AU), Murdoch University (AU), Zhaoqing University (CN), Myer Foundation (AU), Eagle Mount (US), Ecosystem Sciences (AU), University of New England (AU)
Openalex Percentile: Top 19%
Meat and Animal Product Quality
1.19
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