Prediction of chicken quality attributes by near infrared spectroscopy

•NIR spectroscopy was used for fast assessment of chicken samples attributes.•Multivariate statistical analyses were applied to spectral data.•Chicken samples were classified according to quality features.•PLSR models could successfully predict chicken quality features. In the present study, near-in...

Full description

Saved in:
Bibliographic Details
Published in:Food chemistry 2015-02, Vol.168, p.554-560
Main Authors: Barbin, Douglas Fernandes, Kaminishikawahara, Cintia Midori, Soares, Adriana Lourenco, Mizubuti, Ivone Yurika, Grespan, Moises, Shimokomaki, Massami, Hirooka, Elisa Yoko
Format: Article
Language:English
Subjects:
Animals
Biological and medical sciences
Chickens
Classification
Food Technology - methods
Food toxicology
Least-Squares Analysis
Meat - analysis
Medical sciences
NIR
Pale poultry muscle
Partial least squares regression
Pectoralis Muscles - chemistry
Principal Component Analysis
PSE
Spectroscopy, Near-Infrared
Toxicology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•NIR spectroscopy was used for fast assessment of chicken samples attributes.•Multivariate statistical analyses were applied to spectral data.•Chicken samples were classified according to quality features.•PLSR models could successfully predict chicken quality features. In the present study, near-infrared (NIR) reflectance was tested as a potential technique to predict quality attributes of chicken breast (Pectoralis major). Spectra in the wavelengths between 400 and 2500nm were analysed using principal component analysis (PCA) and quality attributes were predicted using partial least-squares regression (PLSR). PCA performed on NIR dataset revealed the influence of muscle reflectance (L∗) influencing the spectra. PCA was not successful to completely discriminate between pale, soft and exudative (PSE) and pale-only muscles. High-quality PLSR were obtained for L∗ and pH models predicted individually (R2CV of 0.91 and 0.81, and SECV of 1.99 and 0.07, respectively). Water-holding capacity was the most challenging attribute to determine (R2CV of 0.70 and SECV of 2.40%). Sample mincing and different spectra pre-treatments were not necessary to maximise the predictive performance of models. Results suggest that NIR spectroscopy can become useful tool for quality assessment of chicken meat.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2014.07.101