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Author: search.filters.author.Suntisakoonwong, Phanchay

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    Item type:Publication,
    Low-cost Multispectral Acquisition Device Coupled with Machine Learning for Detecting Adulteration of Honey
    (2026-07-15)
    Boodnon, Wutthiphong
    ;
    Lunvongsa, Thayanont
    ;
    Suntisakoonwong, Phanchay
    ;
    Sitorus, Agustami
    ;
    Lapcharoensuk, Ravipat
    Honey is a natural sweetener created by honeybees from the nectar of flowers. Honey's extensive health benefits have led to its widespread use across multiple industries. Honey adulteration with inferior substances undermines its quality, reducing natural nutrients and antioxidants, and diminishing its health benefits. This study aimed to study the possibility of detection of honey adulteration with a low-cost multispectral device coupled with machine learning. The adulterated honey came from deliberate adulteration with cane syrup in the 1 to 90% range. Spectral data was collected for pure honey and the adulterated honey samples at the wavelengths of 610, 680, 730, 760, 810, and 860 nm. The detection models for distinguishing pure and adulterated honey were developed by Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), C-Support Vector Machine (C-SVM), and K-Nearest Neighbors (KNN). All models achieved high accuracy between 0.91 and 0.98 and maintained balanced precision and recall metrics. This study serves as a guideline for developing a low-cost portable honey authentication device that is practical for real-world applications.
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    Low-Cost Multispectral Sensor for Detecting Adulteration of Onion Powder with Machine Learning
    (2024-01-01)
    Lapcharoensuk, Ravipat
    ;
    Lunvongsa, Thayanont
    ;
    Suntisakoonwong, Phanchay
    ;
    Sitorus, Agustami
    ;
    Boodnon, Wutthiphong
    Onion 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.