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Physicochemical properties and melting behavior of coconut milk ice cream with Melinjo (Gnetum gnemon Linn.) leaves incorporation
(2025-09-01)
Chonlatarn, Threechart
;
Pinsirodom, Praphan
Melinjo (Gnetum gnemon Linn.), locally known in Thailand as “Liang,” is a widely consumed vegetable in Southern Thailand and often referred to as the “queen of local vegetables” due to its nutritional and health-promoting properties. However, old leaves remain underutilized. This study aimed to evaluate the effects of Melinjo leaf incorporation on the physicochemical properties and melting behavior of coconut milk ice cream. Ice cream formulations were prepared with 5%, 10%, 15%, and 15% (pulp-separated) leaves (w/w), and their physicochemical and melting characteristics—including color parameters, pH, apparent viscosity, overrun, melting rate, induction time to the first drip, and time to 50% drip-through—were analyzed. Increasing Melinjo leaf content significantly decreased L* values while increasing a* and b* values (p≤0.05). Leaf incorporation slightly elevated pH and significantly increased viscosity (p≤0.05), whereas overrun decreased with higher leaf levels, likely due to the influence of dietary fiber and phenolic compounds on air incorporation. Furthermore, Melinjo addition significantly reduced the melting rate and prolonged both induction time to the first drip and time to 50% drip-through (p≤0.05). Overall, incorporation of 15% Melinjo leaves was found to optimize melting resistance and functional properties. These results highlight the potential of Melinjo leaves as a functional ingredient for the development and quality enhancement of coconut milk ice cream and related plant-based frozen desserts.
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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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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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Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy
(2018-06-01)
Cheevitsopon, Ekkapong
;
Sirisomboon, Panmanas
A feasibility study was performed to assess whether near infrared spectroscopy could evaluate the salt content of curry soup containing coconut milk. The soup samples were from the mixing tank, a water content adjusted tank, the ultra-high temperature pipe, and laminated containers of a food processor plant. In addition, fish sauce adjusted samples made from the same recipe but with increasing or decreasing (±30%, 60%, and 90%) sauce content were prepared. There were 113 samples in total, which were scanned using a Fourier-transform near infrared spectrometer. The prediction models for salt content were established using near infrared spectral data in conjunction with partial least squares regression. Calibration models developed using all of the samples were validated using leave-one-out cross validation and test set validation. The unadjusted sample models were validated using test set validation. The results showed that both validation methods for the calibration models using all of the samples provided similar model performance where the r2, root mean square error of calibration/root mean square error of prediction, and residual predictive deviation were 0.956, 0.065%, and 4.77 for cross validation and 0.954, 0.064%, and 4.64 for the test set, respectively. However, the salt unadjusted sample model showed better performance where the r2, RMSEP, and RPD were respectively 0.963, 0.043%, and 5.23, indicating that excellent models can be developed to determine the salt content of curry soup containing coconut milk for any applications, including quality assurance.
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Evaluation of soluble solids of curry soup containing coconut milk by near infrared spectroscopy
(2017-06-01)
Sirisomboon, Panmanas
;
Nawayon, Jutarat
The aim of this research was to do a feasibility study of near infrared spectroscopy to evaluate soluble solids of curry soup containing coconut milk. The soup samples were collected from mixing tanks, water adjusting tanks, an ultra-high temperature process line and laminated cartons. There were also soluble solids adjusted samples by adding or reducing coconut sugar where the curry was made from the same recipe as in the processing line but increasing 30, 60 and 90% coconut sugar and reducing 30, 60 and 90% coconut sugar from normal. There were 119 samples in total. Sample was scanned with an FT-NIR spectrometer. A prediction model for soluble solids was established using near infrared spectral data in conjunction with partial least squares regression. When validated using a set of test samples, the model developed using spectra pretreated by min-max normalization in the range 9403.8–6094.3 cm-1, provided a coefficient of determination (r2), root mean square error of prediction, bias and ratio of performance to interquartile of 0.92, 1.0°Brix, 0.1°Brix and 2.4, respectively. It showed the potential of using near infrared spectroscopy to evaluate soluble solids in curry soup. With further development using more natural samples, a more robust model could be achieved to evaluate soluble solids in curry soup in a processing factory.
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Evaluation of total solids of curry soup containing coconut milk by near infrared spectroscopy
(2016-01-01)
Sirisomboon, Panmanas
;
Nawayon, Jutharat
The aim of this research was to perform a feasibility study of the potential of near infrared (NIR) spectroscopy to evaluate the total solids content of instant curry soups containing coconut milk; these included green curry, red curry, massaman curry and panang curry. The soup samples were collected from mixing tanks, water adjusting tanks, ultra-high temperature process line and laminated cartons. Adjusted samples were made from the same recipe as in the processing line but with the total solids increased by 30%, 60% and 90%, and reduced by 30%, 60% and 90% total solids from normal levels. Each sample was scanned with a Fourier transform NIR spectrometer. A prediction model for total solids was established using NIR spectral data in conjunction with reference data using partial least squares regression, which was validated using leave-one-out validation and test set validation. The test set validation showed better prediction performance as proved by using an unknown sample set. The test set validation model was developed using multiplicative scatter correction of spectra for the 6102-5446.3 cm-1 and 4605.4-4242.9 cm-1 regions, and provided a coefficient of determination for prediction (r2), root mean square error of prediction (RMSEP), bias and ratio of standard error of prediction to the standard deviation (RPD) of 0.92, 0.95%, -0.20% and 3.71, respectively. It was shown that NIR spectroscopy could be applied in an instant curry soup production line for process control and quality assurance.

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