Lapcharoensuk, Ravipat

Preferred name

Alternative Name

Lapcharoensuk, R.

Main Affiliation

School of Engineering

ravipat.la@kmitl.ac.th

Now showing 1 - 10 of 12

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Nondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach
(2023-03-01)
Lapcharoensuk, Ravipat
;
Fhaykamta, Chawisa
;
Anurak, Watcharaporn
;
Chadwut, Wasita
;
Sitorus, Agustami
The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908–1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.
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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 2<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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Discrimination model of geographical area from coconut milk by near-infrared spectroscopy: Exploration in tandem with classical chemometrics, machine learning, and deep learning
(2024-11-01)
Sitorus, Agustami
;
Lapcharoensuk, Ravipat
This work proposes exploring the discrimination model by near-infrared (NIR) spectroscopy (FT-NIR and Micro-NIR) for geographical source areas of coconut milk in tandem with the classical to modern chemometrics classifier. The discrimination model was developed using qualitative chemometrics techniques from classic (Principal Component Analysis-PCA, Partial Least Squares Discriminant Analysis-PLS-DA, Linear Discriminant Analysis-LDA) to modern, including classifiers from machine learning (Support Vector Machine-SVM, k-Nearest Neighbor-KNN, Artificial Neural Network-ANN) and deep learning (Simple Convolutional Neural Networks-S-CNN, S-AlexNET, Residual Networks-ResNET). Three sources as geographical areas of coconut milk originally from Thailand were used, including the south region (Chumphon Province), middle region (Samut Songkhram Province), and east region (Chonburi Province). Our findings showed that a classifier from SVM and ResNET could yield the optimal performance for discriminating the geographical source area of coconut milk using FT-NIR. Furthermore, when using Micro-NIR, the classifier from LDA, SVM, KNN and ResNET delivered the highest accuracy. The performance discrimination models above were excellent when classified based on the kappa coefficient. This study concluded that both FT-NIR and Micro-NIR supported by classical to modern chemometric classifiers could be used to evaluate the geographical area source from coconut milk. Also, the method in this study includes a strategy for discovering feature-important NIR spectra for interpretability purposes, thereby facilitating the qualitative interpretation of results for all types of classifiers.
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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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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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Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy
(2024-04-01)
Sitorus, Agustami
;
Lapcharoensuk, Ravipat
Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886–0.999, RMSE of 0.370–6.108%, and Bias of −0.176–1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.
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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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A COMPREHENSIVE OVERVIEW OF NEAR INFRARED AND INFRARED SPECTROSCOPY FOR DETECTING THE ADULTERATION ON FOOD AND AGRO-PRODUCTS—A CRITICAL ASSESSMENT
(2022-01-01)
Sitorus, Agustami
;
Lapcharoensuk, Ravipat
In the past decade, fast and non-destructive methods based on spectroscopy technology have been studied to detect and discriminate against food adulteration and agro-products. Numerous linear and nonlinear chemometric approaches have been developed for spectroscopy analysis. Recently, various approaches have been developed for spectroscopic calibration modelling to detect and discriminate adulteration food and agro-products. This article discusses the application of spectroscopy technology, including near infrared and infrared, in detecting and discriminating the adulteration of food and agro-products based on recent research and delivered a critical assessment on this topic to serve as lessons from current studies and future outlooks. The current state-of-the-art techniques, including detection and classification of various adulteration in food and agro-products, have been addressed in this paper. Key findings from this study, near infrared and infrared spectroscopy is a non-destructive, rapid, simple-preparation, analytical rapidity, and straightforward method for classification and determination of adulteration in the food and agro-products so it is suitable for large-scale screening and on-site detection. Although there are still some unsatisfactory research results, especially in detecting tiny adductors, these technologies can potentially detect any adulteration in the various food and agro-products at an economically viable level, at least for the initial screening process. In that respect, near infrared and infrared spectroscopy should be expanded to cover all food and agro-products sold in the market. Only then will there be an acceptable deterrent in place to stop adulteration activity in widely consumed food and agro-products ingredients
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Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration
(2026-10-01)
Lapcharoensuk, Ravipat
;
Sitorus, Agustami
Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500–4000 cm−1) were collected from binary mixtures (0%–100%; w /w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT ' 2D-asynchronous ' 2D-synchronous ' 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial–spectral correlations.

Lapcharoensuk, Ravipat

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