TY - JOUR
T1 - Gas sensor array to classify the chicken meat with E. coli contaminant by using random forest and support vector machine
AU - Astuti, Suryani Dyah
AU - Tamimi, Mohammad H.
AU - Pradhana, Anak A.S.
AU - Alamsyah, Kartika A.
AU - Purnobasuki, Hery
AU - Khasanah, Miratul
AU - Susilo, Yunus
AU - Triyana, Kuwat
AU - Kashif, Muhammad
AU - Syahrom, Ardiyansyah
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12
Y1 - 2021/12
N2 - Microbes such as Escherichia coli (E. coli) can easily contaminate raw chicken meat in clean conditions, causing decay and unpleasant scents. This study aims to characterize gas patterns by comparing fresh chicken meat and E. coli bacteria contaminated chicken meat based on shelf life using a Gas Sensor Array (GSA) system (MQ2, MQ3, MQ7, MQ8, MQ135, and MQ136) on electronic nose. The findings revealed GSA capability to detect a variety of typical gas patterns formed by the samples. This gas detection property is indicated by the appearance of the variance in the sensors output voltage pattern for each sample variation. The data for fresh and contaminated samples were classified by the random forest (RF) classifier with 99.25% and 98.42% precision, respectively. Furthermore, the support vector machine (SVM) classifier correctly identified the fresh and contaminated samples with 98.61% and 86.66% accuracy, respectively. This finding offers insight for GSA capability in classifying chicken meat contaminated with E. coli using an RF and SVM.
AB - Microbes such as Escherichia coli (E. coli) can easily contaminate raw chicken meat in clean conditions, causing decay and unpleasant scents. This study aims to characterize gas patterns by comparing fresh chicken meat and E. coli bacteria contaminated chicken meat based on shelf life using a Gas Sensor Array (GSA) system (MQ2, MQ3, MQ7, MQ8, MQ135, and MQ136) on electronic nose. The findings revealed GSA capability to detect a variety of typical gas patterns formed by the samples. This gas detection property is indicated by the appearance of the variance in the sensors output voltage pattern for each sample variation. The data for fresh and contaminated samples were classified by the random forest (RF) classifier with 99.25% and 98.42% precision, respectively. Furthermore, the support vector machine (SVM) classifier correctly identified the fresh and contaminated samples with 98.61% and 86.66% accuracy, respectively. This finding offers insight for GSA capability in classifying chicken meat contaminated with E. coli using an RF and SVM.
KW - Chicken meat
KW - E. coli
KW - Food security
KW - Gas sensor array
KW - Random forest
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85119369128&partnerID=8YFLogxK
U2 - 10.1016/j.biosx.2021.100083
DO - 10.1016/j.biosx.2021.100083
M3 - Article
AN - SCOPUS:85119369128
SN - 2590-1370
VL - 9
JO - Biosensors and Bioelectronics: X
JF - Biosensors and Bioelectronics: X
M1 - 100083
ER -