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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.)

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

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Nolasco Perez, Irene Marivel [1] ; Badaro, Amanda Teixeira [1] ; Barbon, Jr., Sylvio [2] ; Barbon, Ana Paula A. C. [3] ; Rodrigues Pollonio, Marise Aparecida [4] ; Barbin, Douglas Fernandes [1]
Total Authors: 6
[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
[4] Univ Campinas UNICAMP, Dept Food Technol, Campinas, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: Applied Spectroscopy; v. 72, n. 12, p. 1774-1780, DEC 2018.
Web of Science Citations: 1

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical-chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L{*}a{*}b{*} color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900-1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry. (AU)

FAPESP's process: 06/03263-9 - Process, packaging, edible coatings application and final quality evaluation of some high moisture tropical fruits
Grantee:Miriam Dupas Hubinger
Support type: Regular Research 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