Lapcharoensuk, Ravipat

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Lapcharoensuk, Ravipat
Alternative Name
Lapcharoensuk, R.
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Email
ravipat.la@kmitl.ac.th

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    Item type:Publication,
    Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration
    (2024-11-01)
    Sitorus, Agustami
    ;
    Lapcharoensuk, Ravipat
    This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%–50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP). The proposed performance strategy effectively addressed and produced satisfactory outcomes for classification and regression challenges in coconut milk adulteration. Finally, the results demonstrated that the proposed approach can more accurately determine the best model, particularly for NIR spectroscopy of coconut milk adulteration.
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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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    Item type:Publication,
    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.