4.7 Article

Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut

Journal

FOOD CONTROL
Volume 145, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.foodcont.2022.109379

Keywords

Loin posterior end; Loin anterior end; Food quality; Meat quality; Fat marbling

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This study examined the feasibility of using hyperspectral imaging to predict the intramuscular fat (IMF) content in pork Longissimus thoracis et lumborum (LTL). The results showed that by using spectral data from Gluteus medius, the IMF content in the LTL could be accurately predicted, which could be useful in segregating pork LTL based on IMF content.
To assess the intramuscular fat (IMF) of Longissimus thoracis et lumborum (LTL) primal cut excised from an animal carcass, rib sections are dissected for chemical or subjective evaluation. Dissection destroys the integrity of the LTL. This study examined the feasibility of developing a method based on hyperspectral imaging to predict the IMF of pork LTL using hyperspectral imaging. Spectral information from different tissues was collected at the cranial (Serratus ventralis, Spinalis dorsi, longissimus, backfat), and the caudal (Gluteus medius, Psoas major) ends of the LTL. The spectral information and the IMF content chemically extracted from the mid-loin (3rd and 4th last rib of the LTL) were used to develop regression models for the prediction of the IMF content in the 3rd and 4th. The individual results of the analysis from the various tissues at both the cranial and caudal ends show that models exhibited performances for IMF prediction with root-mean-square-error (RMSEP) ranging from 0.36 to 0.92% and coefficient of determination in prediction ranging from 0.43 to 0.81. Gluteus medius showed the highest accuracy. This study has demonstrated that hyperspectral imaging could be used to accurately predict the IMF content in the mid-loin by using spectral data from Gluteus medius. The result could be useful in segregating pork LTL based on IMF content.

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