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Appl Spectrosc. 2018 Jul 31:3702818788878. doi: 10.1177/0003702818788878. [Epub ahead of print]

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

Author information

1
1 Department of Food Engineering, University of Campinas (UNICAMP), Brazil.
2
2 Department of Computer Science, Londrina State University (UEL), Brazil.
3
3 Department of Zootechnology, Londrina State University (UEL), Brazil.
4
4 Department of Food Technology, University of Campinas (UNICAMP), Brazil.

Abstract

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.

KEYWORDS:

Meat; NIR; PCA; SVM; machine learning; near-infrared; prediction; principal component analysis; random forest; spectroscopy; support vector machine

PMID:
30063378
DOI:
10.1177/0003702818788878

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