Abstract:
:In this study, the potential of hyperspectral imaging (HSI) for predicting hydroxyproline content in chicken meat was investigated. Spectral data contained in the hyperspectral images (400-1000 nm) of chicken meat was extracted, and a partial least square regression (PLSR) model was then developed for predicting hydroxyproline content. The model yielded acceptable results with regression coefficient in prediction (Rp) of 0.874 and root mean error squares in prediction (RMESP) of 0.046. Based on the eight optimal wavelengths selected by regression coefficients (RC) from the PLSR model, a new RC-PLSR model was built and good results were shown with high Rp of 0.854 and low RMSEP of 0.049. Finally, distribution maps of hydroxyproline were created by transferring the RC-PLSR model to each pixel in the hyperspectral images. The results demonstrated that HSI has the capability for rapid and non-destructive determination of hydroxyproline content in chicken meat.
journal_name
Food Chemjournal_title
Food chemistryauthors
Xiong Z,Sun DW,Xie A,Han Z,Wang Ldoi
10.1016/j.foodchem.2014.11.161subject
Has Abstractpub_date
2015-05-15 00:00:00pages
417-22eissn
0308-8146issn
1873-7072pii
S0308-8146(14)01899-8journal_volume
175pub_type
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