Predicting aged pork quality using a portable Raman device

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dc.contributor.author Lonergan, Steven
dc.contributor.author Yu, Chenxu
dc.contributor.author Santos, C. C.
dc.contributor.author Zhao, J.
dc.contributor.author Huff-Lonergan, Elisabeth
dc.contributor.author Dong, X.
dc.contributor.author Lonergan, S. M.
dc.contributor.author Huff-Lonergan, E.
dc.contributor.author Outhuse, A.
dc.contributor.author Carlson, K. B.
dc.contributor.author Prusa, K. J.
dc.contributor.author Fedler, C. A.
dc.contributor.author Yu, C.
dc.contributor.author Shackelford, S. D,
dc.contributor.author King, D. A.
dc.contributor.author Wheeler, T. L.
dc.contributor.department Food Science and Human Nutrition
dc.contributor.department Animal Science
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2019-05-22T03:50:50.000
dc.date.accessioned 2020-06-29T23:40:55Z
dc.date.available 2020-06-29T23:40:55Z
dc.date.issued 2018-11-01
dc.description.abstract <p>The utility of Raman spectroscopic signatures of fresh pork loin (1 d & 15 d postmortem) in predicting fresh pork tenderness and slice shear force (SSF) was determined. Partial least square models showed that sensory tenderness and SSF are weakly correlated (R2 = 0.2). Raman spectral data were collected in 6 s using a portable Raman spectrometer (RS). A PLS regression model was developed to predict quantitatively the tenderness scores and SSF values from Raman spectral data, with very limited success. It was discovered that the prediction accuracies for day 15 post mortem samples are significantly greater than that for day 1 postmortem samples. Classification models were developed to predict tenderness at two ends of <a href="https://www.sciencedirect.com/topics/food-science/sensory-quality" title="Learn more about Sensory Quality">sensory quality</a> as “poor” vs. “good”. The accuracies of classification into different quality categories (1st to 4th percentile) are also greater for the day 15 postmortem samples for sensory tenderness (93.5% vs 76.3%) and SSF (92.8% vs 76.1%). RS has the potential to become a rapid on-line screening tool for the pork producers to quickly select meats with superior quality and/or cull poor quality to meet market demand/expectations.</p>
dc.description.comments <p>This article is published as Santos, C. C., J. Zhao, X. Dong, S. M. Lonergan, E. Huff-Lonergan, A. Outhouse, K. B. Carlson et al. "Predicting aged pork quality using a portable Raman device." <em>Meat science </em>145 (2018): 79-85. doi: <a href="https://doi.org/10.1016/j.meatsci.2018.05.021" target="_blank" title="Persistent link using digital object identifier">10.1016/j.meatsci.2018.05.021</a>. </p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ans_pubs/449/
dc.identifier.articleid 1449
dc.identifier.contextkey 14205172
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ans_pubs/449
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/9878
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ans_pubs/449/2018_Lonergan_PredictingPork.pdf|||Sat Jan 15 00:19:28 UTC 2022
dc.source.uri 10.1016/j.meatsci.2018.05.021
dc.subject.disciplines Agriculture
dc.subject.disciplines Animal Sciences
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Meat Science
dc.subject.keywords On-line data collection
dc.subject.keywords Pork quality
dc.subject.keywords Raman spectral
dc.subject.keywords Support vector machine
dc.subject.keywords Tenderness prediction
dc.title Predicting aged pork quality using a portable Raman device
dc.type article
dc.type.genre article
dspace.entity.type Publication
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