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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.)

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

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Author(s):
Barbon Jr, Sylvio ; Ayub da Costa Barbon, Ana Paula [1] ; Mantovani, Rafael Gomes [2] ; Barbin, Douglas Fernandes [3, 4]
Total Authors: 4
Affiliation:
[1] State Univ Londrina UEL, Dept Zootechnol, BR-86057970 Londrina, PR - Brazil
[2] Univ Sao Paulo, Sci Inst Math & Comp ICMC, BR-13566590 Sao Carlos, SP - Brazil
[3] Univ Estadual Campinas, UNICAMP, Dept Food Engn, BR-13083862 Campinas, SP - Brazil
[4] Fed Univ Technol UTFPR, Dept Food Sci, BR-86020430 Londrina, PR - Brazil
Total Affiliations: 4
Document type: Journal article
Source: JOURNAL OF SPECTROSCOPY; 2018.
Web of Science Citations: 0
Abstract

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIE L{*} a{*}b{*}, chroma, and hue), water holding capacity (WHO, and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices. (AU)

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