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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Comparison of rapid techniques for classification of ground meat

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Author(s):
Nolasco-Perez, Irene M. [1] ; Rocco, Luiz A. C. M. [1] ; Cruz-Tirado, Jam P. [1] ; Pollonio, Marise A. R. [1] ; Barbon, Jr., Syluio [2] ; Barbon, Ana Paula A. C. [3] ; Barbin, Douglas F. [1]
Total Authors: 7
Affiliation:
[1] Univ Campinas Unicamp, Dept Food Engn, Campinas, SP - Brazil
[2] Londrina State Univ UEL, Dept Comp Sci, Londrina - Brazil
[3] Londrina State Univ UEL, Dept Zootechnol, Londrina - Brazil
Total Affiliations: 3
Document type: Journal article
Source: BIOSYSTEMS ENGINEERING; v. 183, p. 151-159, JUL 2019.
Web of Science Citations: 0
Abstract

Computer vision and near infrared spectroscopy are fast and non-invasive techniques currently available for processing control in the meat industry. These techniques can be used, either separately or combined, for on-line assessment of meat quality parameters. This study aimed to compare a portable near-infrared (NIR) spectrometer, near infrared hyperspectral imaging (NIR-HSI) and red, green and blue imaging (RGB-I) to differentiate ground samples from beef, pork and chicken meat; and to quantify amounts of each in mixtures. Chicken breast meat was adulterated with either pork leg meat or beef round meat from 0 to 50% (w/w). Partial Least Squares regression (PLSR) models were performed using full spectra and after selecting most important wavelengths. The best results were obtained with NIR-HSI, with coefficient of prediction (R-p(2)) of 0.83 and 0.94, ratio performance to deviation (RPD) of 1.96 and 3.56, and ratio of error range (RER) of 10.0 and 18.1, for samples of chicken adulterated with pork and beef, respectively. In addition, the results obtained using NIR spectroscopy and RGB-I confirm that these techniques provide an alternative for rapid, on-line inspection of ground meat in the food industry. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 14/50951-4 - INCT 2014: Advanced Analytical Technologies
Grantee:Celio Pasquini
Support type: Research Projects - Thematic Grants
FAPESP's process: 08/57808-1 - National Institute of Advanced Analytical Science and Technology
Grantee:Celio Pasquini
Support type: Research Projects - Thematic Grants
FAPESP's process: 15/24351-2 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
Grantee:Douglas Fernandes Barbin
Support type: Research Grants - Young Investigators Grants