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Author: search.filters.author.Boodnon, WutthiphongHas files: No

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    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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    Quantitative analysis based on image processing combined with machine learning and deep learning to determine the adulteration in nutmeg powder
    (2025-10-01)
    Sitorus, Agustami
    ;
    Pambudi, Suluh
    ;
    Boodnon, Wutthiphong
    ;
    Lapcharoensuk, Ravipat
    The global demand for nutmeg powder has increased, raising the risk of adulteration and necessitating an efficient and cost-effective screening method. The objective of this study is to develop a calibration model to predict the adulteration of nutmeg powder by cinnamon powder using a novel approach by integrating image processing with machine learning (ML) and deep learning (DL). Eight regressors, including four ML regressors (multiple linear ridge regression–MLRR, partial least squares regression–PLSR, multi-layer perceptron–MLP, and adaptive boosting–ABS) and four DL regressors (convolutional neural networks–CNN, AlexNET, residual networks–ResNET, and GoogleNET), were employed to analyze 1800 images of adulteration samples ranging from 0 % to 35 % (w/w). Among ML models, MLP achieved the highest accuracy in prediction (R<inf>p</inf>²=0.922, RMSEP=2.804 %, RPD=3.59), while PLSR (R<inf>p</inf>²=0.876, RMSEP=3.538 %, RPD=2.84), MLRR (R<inf>p</inf>²=0.872, RMSEP=3.596 %, RPD=2.80), and ABS (R<inf>p</inf>²=0.849, RMSEP=3.904 %, RPD=2.58) underperformed. For DL, ResNET (R<inf>p</inf>²=0.882, RMSEP=3.416 %, RPD=2.91) surpassed CNN (R<inf>p</inf>²=0.876, RMSEP=3.505 %, RPD=2.84), AlexNET (R<inf>p</inf>²=0.801, RMSEP=4.429 %, RPD=2.24), and GoogleNET (R<inf>p</inf>²=0.751, RMSEP=4.963 %, RPD=2.00). The MLP's superiority highlights its compatibility with ORB-based feature extraction for nonlinear adulteration patterns, outperforming complex DL architectures. This research highlights the potential of image processing supported by ML and DL as a rapid and low-cost tool for future nutmeg powder adulteration screening.
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    Near-Infrared Spectroscopy with Machine Learning for Classifying and Quantifying Nutmeg Adulteration
    (2024-01-01)
    Sitorus, Agustami
    ;
    Pambudi, Suluh
    ;
    Boodnon, Wutthiphong
    ;
    Lapcharoensuk, Ravipat
    Near-infrared spectroscopy (NIRS) provides broadbands, overtones, and combinations of organic-bond vibrations and has been used to characterize agricultural and food products. The adulteration of grated nutmeg with cinnamon is extremely profitable and difficult to detect; to prevent retail fraud, it is vital to differentiate between these materials. This study proposes a model for classifying the adulteration of nutmeg with cinnamon and predicting the level of adulteration. NIR spectra were characterized with six machine learning (ML) algorithms, namely, the principal component-multilayer perceptron (PC-MLP), principal component-linear discriminant analysis (PC-LDA), partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and decision tree (DT) methods. PC-MLP provided 100% accuracy in calibration and prediction in distinguishing nutmeg from cinnamon. In addition, this approach showed excellent performance in predicting the adulteration ratio of nutmeg and cinnamon with a high coefficient of determination of prediction (R <sup>2</sup><inf>pred</inf>) value of 0.9969, low root mean square error of prediction (RMSEP) value of 0.5728%, and high ratio of prediction to deviation (RPD) value of 17.9605. Therefore, this study indicates the potential of integrating NIR spectroscopy with PC-MLP to classify and quantify the adulteration of nutmeg.
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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.