Open AccessExamination system of chicken meat quality based on hyperspectral imaging
Author(s) -
Engrid Latifa Noferita Kaswati,
Adhi Harmoko Saputro,
Cuk Imawan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1528/1/012045
Subject(s) - broiler , food science , vnir , organoleptic , correlation coefficient , mathematics , hyperspectral imaging , partial least squares regression , chicken breast , coefficient of determination , chemistry , statistics , computer science , artificial intelligence
The freshness of the chicken meat will be degraded due to microbiological and chemical processes and will affect the quality of the chicken’s meat. Measurements of freshness were done by a laboratory test that usually destructively and takes a long time. In this study, a VNIR imaging system was built with a wavelength range of 400-1000 nm to determine the freshness of broiler chicken meat. The freshness of the chicken meat was analyzed by using the organoleptic and pH measurement approach. Classification using Random Forest (RF) modeling has been developed to predict the freshness of chicken meat. The freshness of chicken meat was evaluated by using the correction value of 85.5%. The Partial Least Square Regression (PLSR) algorithm was successfully used to determine the pH. The pH measurement system for fresh chicken meat was evaluated using a correlation coefficient of 0.80 and RMSE 0.16. Meanwhile, for the spoiled chicken meat, pH was measured using a correlation coefficient of 0.84 and RMSE of 0.18. Both classification and regression methods indicate that this measurement system is adequate for predicting the quality of chicken meat.