21 Citations (Scopus)

Abstract

Examining the purity of meat is a classical problem in developing countries, especially in Indonesia. The high economic value of beef causes counterfeiting to occur frequently. The forgery process is done through the simple practice of mixing in a certain percentage of pork. Several recent studies have shown that e-noses can examine beef purity through gas detection. This study aimed to determine the effect of gas concentration on the results of detection and classification of beef and pork mixtures by characterizing different meat samples in 3 chambers with a different size. The meat mixture dataset was retrieved by an e-nose device with an array of MQ series sensors that are sensitive to chemical scents. Classification of the meat mixtures was done in several stages: data acquisition from the 3 different sample chambers, statistical feature extraction, classification, ensemble learning, and performance evaluation based on a confusion matrix. The experimental results from this study indicate that the sample chamber with the highest gas concentration yielded the highest accuracy. The best accuracy result, i.e. 95.71%, was obtained with a 50-ml sample chamber using an ensemble method with the statistical parameters of kurtosis and skewness.

Original languageEnglish
Article number106838
JournalComputers and Electronics in Agriculture
Volume195
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Beef
  • Classification
  • E-Nose
  • Gas concentration
  • Machine learning
  • Pork

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