Lapcharoensuk, Ravipat

Preferred name

Alternative Name

Lapcharoensuk, R.

Main Affiliation

School of Engineering

ravipat.la@kmitl.ac.th

Now showing 1 - 10 of 11

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Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques
(2019-09-24)
Lapcharoensuk, Ravipat
;
Phuphanutada, Jirawat
;
Wongpromrat, Patthranit
This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500-4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.
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Discrimination of vegetable oil types using Fourier transforms near infrared spectroscopy coupled with pattern recognition techniques
(2019-09-09)
Lapcharoensuk, R.
;
Malithong, A.
;
Thappho, D.
;
Phonpho, P.
The aim of this research was to investigate the potential of near infrared spectroscopy (NIRs) coupled with pattern recognition techniques for discriminating of vegetable oil types (i.e. coconut oil, olive oil, rice bran oil, sesame oil, soybean oil and sun flower oil). Principle component analysis (PCA) was performed for clustering vegetable oil types. Five of supervised pattern recognition techniques such as soft independent modelling of class analogies (SIMCA), Partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) were used to identify vegetable oil types. The PCA model could separate coconut oil from other vegetable oils. Two PLS-DA and SVM models showed 100% of precision, recall F-measure and accuracy for all vegetable oil whilst remainder techniques achieved a satisfactory classified performance. All supervised models could discriminate coconut oil from other oils with precision, recall F-Measure and accuracy of 100%. It seems that NIRs technique coupled with pattern recognition techniques is possible for discriminating vegetable oil types.
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Quantitative detection of pepper powder adulterated with rice powder using Fourier-transform near infrared spectroscopy
(2019-09-09)
Lapcharoensuk, R.
;
Chalachai, S.
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Sinjaru, S.
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Singsriand, P.
;
Hongwiangjan, J.
Near infrared (NIR) spectroscopy model was developed for detecting pepper powder adulterated with rice powder. The adulterated pepper powder samples were prepared by mixing rice powders with pure pepper powder to 19 levels of concentrations (w/w) from 5-95%w/w. Two hundred ten NIR spectra of pure and adulterant pepper powders were recorded using Fourier-transform near infrared spectrometer. The NIRs quantitative model for detecting adulterant pepper were established using partial least squares regression (PLS). The optimum model was established from NIR spectra treated by constant offset elimination with the of 0.99. These results show that the NIR spectroscopy could be a modern method for monitoring adulteration of pepper powder with rice powder.
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Evaluation of the physicochemical and textural properties of pomelo fruit following storage
(2012-12-01)
Sirisomboon, Panmanas
;
Lapcharoensuk, Ravipat
Introduction. Because of the long transportation times and storage durations that are often experienced, it is important to understand how pomelo properties change over time; that will be indicative of how long samples can be stored before they drop below acceptable quality limits. As no information is available regarding storage of pomelo fruit in the literature, the goal of our work was to study the change in pomelo physicochemical and textural properties following storage over a period of approximately 4 months. Materials and methods. The changes in physical, chemical and textural properties of commercial pomelo fruit were extensively evaluated over the course of a 4-month storage period. The correlation among physical, color, chemical and textural properties were also assessed. Results. Color properties (L*, a*, b*) changed significantly with storage; however, they did not change much during the initial 45 days of storage. The change in soluble solids content (SSC), acidity (A) and the [(SSC) / (A)] ratio suggested that pomelo fruit should not be stored for more than 75 days. Crucially, our study demonstrates that the a* parameter (greenness, R = -0.556) correlates best with the chemical property the [(SSC) / (A)] ratio, which is a recognized measurement of the consumer acceptability of citrus fruit. This means that measuring a* is a non-destructive way of monitoring the taste characteristics of pomelos in storage. This property also correlated well with the average firmness of the fruit and with the flesh texture. Conclusion. This fundamental information on the pomelo fruit could prove useful in the physical handling and processing of the fruit by breeders and postharvest technologists as well as distributors, market agents, importers and exporters. © 2012 Cirad/EDP Sciences.
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Evaluation of acetic acid and ethanol concentration in a rice vinegar internal venturi injector bioreactor using Fourier transform near infrared spectroscopy
(2019-12-01)
Phanomsophon, Thitima
;
Sirisomboon, Panmanas
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Lapcharoensuk, Ravipat
;
Shrestha, Bim
;
Krusong, Warawut
In the process of fermenting rice vinegar, the concentration of acetic acid and ethanol concentration must be measured for monitoring of the total concentration. Near infrared spectroscopy has been used to rapidly monitor the concentration of acetic acid and ethanol concentration daily during 10 cycles of the fermentation process. The model was developed using partial least squares regression. For predicting concentration of acetic acid with near infrared spectroscopy, the coefficient of determination (R2), root mean square error of calibration, root mean square error of cross validation, ratio of standard error of validation to standard deviation, and bias was 0.96, 2.30 g L−1, 2.44 g L−1, 1.11 g L−1, and 5.56, respectively. For ethanol concentration, the value of R2, root mean square error of calibration, root mean square error of cross validation, bias and ratio of prediction to deviation were predicted to be 0.94, 3.15 g L−1, 2.73 g L−1, −0.40 g L−1, and 4.04, respectively. However, both models provided fair performance when tested with an external set of samples, indicating that the models could be applied for rough screening.
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Development of the near infrared spectroscopy model for detecting herbicide concentrations contaminated in water
(2019-09-09)
Lapcharoensuk, Ravipat
;
Wongpromrat, Patthranit
This research aims to develop the near infrared spectroscopy (NIR) models for detecting herbicide concentrations contaminated in water. Atrazine, the organochlorine herbicide, solution in the concentration range of 0-15 ppm were prepared in distilled water. Near infrared spectra were scanned by using Fourier transform spectrometer at wavenumbers of 12,500-4,000 cm-1 (800-2, 500 nm). Partial least square regression technique was used to establish the NIR models for detecting herbicide concentrations. The developed models showed high prediction potential of herbicide concentrations contaminated in water with the R2 of 0.97, RMSEE of 0.899 ppm, bias of -0.0003 ppm and RPD of 5.43. This indicated that these models can apply to analyse contamination of atrazine in the natural water sources.
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Eating quality of cooked rice determination using Fourier transform near infrared spectroscopy
(2014-10-20)
Lapcharoensuk, Ravipat
;
Sirisomboon, Panmanas
The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared (FT-NIR) Spectrometer. Samples of milled: parboiled rice, white rice, new Jasmine rice (harvested in 2012) and aged Jasmine rice (harvested in 2006 or during the period 2007-2011) were used in this study. The eating quality of the cooked rice, i.e., adhesiveness, hardness, dryness, whiteness and aroma, were evaluated by trained sensory panelists. FT-NIR spectroscopy models for predicting the eating quality of cooked rice were established using the partial least squares regression. Among the eating quality, the stickiness model indicated its highest prediction ability (i.e., R <inf>val</inf>2 = 0:71; RMSEP = 0:65; Bias = 0:00; RPD = 1:87) and SEP/SD of 2. In addition, it was clear that the water content did not affect the eating quality of cooked rice, rather the main chemical component implicated was starch.
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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-1 with resolution of 8 cm-1. 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>2 = 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.
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Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy
(2018-08-14)
Posom, Jetsada
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Phuphanutada, Jirawat
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Lapcharoensuk, Ravipat
The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.
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Identification of syrup type using fourier transform-near infrared spectroscopy with multivariate classification methods
(2018-03-01)
Lapcharoensuk, Ravipat
;
Nakawajana, Natrapee
This research aimed to establish near infrared (NIR) spectroscopy models for identification of syrup types in which the maple syrup was discriminated from other syrup types. Thirty syrup types were used in this research; the NIR spectra of each type were recorded with 10 replicates. The repeatability and reproducibility of NIR scanning were performed, and the absorbance at 6940cm-1 was used for calculation. Principal component analysis was used to group the syrup type. Identification models were developed by soft independent modeling by class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA). The SIMCA models of all syrup types exhibited accuracy percentage of 93.3-100% for identifying syrup types, whereas maple syrup discrimination models showed percentage of accuracy between 83.2% and 100%. The PLS-DA technique gave the accuracy of syrup types classification between 96.6% and 100% and presented ability on discrimination of maple syrup form other types of syrup with accuracy of 100%. The finding presented the potential of NIR spectroscopy for the syrup type identification.

Lapcharoensuk, Ravipat

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