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Author: search.filters.author.Lapcharoensuk, RavipatSubject: search.filters.subject.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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    A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools
    (2023-12-01)
    Sitorus, Agustami
    ;
    Lapcharoensuk, Ravipat
    Coconut milk is a soft target for adulterators owing to its simplicity of chemical composition. Professionals and consumers want to control the originalitas of coconut milk, while sellers can profit by mixing fresh coconut milk from low-cost products into high-value fresh coconut milk. Non-destructively and rapidly identifying coconut milk classification goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near-infrared spectra (NIRs) were collected from fresh coconut milk (FCM), instant coconut milk (ICM), and adulterated fresh coconut milk (A-FCM) in order to investigate the prospect of non-invasively discriminating coconut milk type and at the same time predicting the level of A-FCM. Partial least squares (PLS), linear discriminant analysis (LDA), support vector machine (SVM), and multilayer perceptron (MLP) were employed to establish classification and regression models using NIRs. Combining 18 preprocessing types and hyperparameter optimization of individual machine learning algorithms is carried out together and evaluated using 5-folds cross-validation. All algorithms in this study (LDA, SVM, MLP) obtained the same satisfactory results with all the precision, recall, F1-score, and perfect accuracy (100%) to distinguish FCM, ICM, and A-FCM in both calibration and prediction. Regression models using the SVM obtained acceptable results, with a determination coefficient of calibration and prediction all over 0.93, root mean square error of calibration and prediction all below 8.30%, and ratio of prediction to deviation over 3.80. Last but not least, this study would help apply NIRs to detect the originality of coconut milk in real-world conditions.
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    Quantitative detection of buffalo milk adulteration with cow milk using Fourier transform near infrared spectroscopy
    (2019-01-01)
    Lapcharoensuk, Ravipat
    ;
    Chaiyanate, Jirapad
    ;
    Winichai, Supakit
    ;
    Phetnak, Achiraya
    A near infrared (NIR) spectroscopy model was used to quantitatively detect buffalo milk adulteration with cow milk. Pasteurized buffalo milk samples were purchased from a dairy farm and from a local supermarket. Adulterated milk samples were prepared with ratio of cow milk to buffalo milk at 9 levels of 10:90, 20:80, 30:70, 40:60, 50:50, 60:40, 70:30, 80:20 and 90:10 wt%. Spectra of pure buffalo milk, pure cow milk and adulterated milk samples were recorded by a Fourier transform NIR spectrometer in the wavenumber range of 12500-4000 cm<sup>-1</sup> with resolution of 8 cm<sup>-1</sup>. A NIR spectroscopy quantitative model was developed with partial least square (PLS) regression. The NIR spectroscopy model showed ability to detect adulterated milk as follows: R<inf>val</inf><sup>2</sup> = 0.998, RMSEP = 2.121 wt%, Bias =-0.396 wt% and RPD = 18.1. NIR spectroscopy coupled with PLS algorithm was shown to be an alternative technique to detect buffalo milk adulteration with cow milk in the global dairy industry.