Low-Cost Multispectral Sensor for Detecting Adulteration of Onion Powder with Machine Learning

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dc.contributor.authorLapcharoensuk, Ravipat
dc.contributor.authorLunvongsa, Thayanont
dc.contributor.authorSuntisakoonwong, Phanchay
dc.contributor.authorSitorus, Agustami
dc.contributor.authorBoodnon, Wutthiphong
dc.date.accessioned2026-08-06T10:44:33Z
dc.date.available2026-08-06T10:44:33Z
dc.date.issued2024-01-01
dc.description.abstractOnion powder has been frequently adulterated with cheaper materials to boost revenues for scammers. The effective technique is necessary for the application of authentication of onion powder. The aim of this study is application of low-cost multispectral sensor for detecting adulteration of onion powder. Traditional and machine learning algorithms including multiple linear regression (MLR), partial least square regression (PLS-R), nu-support vector regression (nu-SVR), and black propagation neural network (BPNN) were used to train prediction models. Visible and near infrared (Vis-NIR) spectral data was collected using low-cost multispectral sensor at wavelength of 610,680,730, 760,810 and 860 ~nm. Adulterated onion samples were prepared by blending corn flour and onion powder. The coefficient of determination (R_P2) value of all algorithms was between 0.888 and 0.959 while the range of root mean square error of prediction (RMSEP) was 4.214-6.964 %. This finding point indicated that combination of low-cost Vis-NIR multispectral sensor and machine learning could be used for detecting adulteration of onion powder.
dc.identifier.citation2024 10th International Conference on Mechatronics and Robotics Engineering Icmre 2024, 1-4, 2024
dc.identifier.doi10.1109/ICMRE60776.2024.10532183
dc.identifier.other2-s2.0-85195130534
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15240
dc.source2024 10th International Conference on Mechatronics and Robotics Engineering Icmre 2024
dc.subjectMachine learning
dc.subjectMultispectral sensor
dc.subjectOnion powder
dc.titleLow-Cost Multispectral Sensor for Detecting Adulteration of Onion Powder with Machine Learning
dc.typeConference Paper

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