Sirisomboon, Panmanas

Preferred name

Alternative Name

Sirisomboon, P.

Main Affiliation

School of Engineering

panmanas.si@kmitl.ac.th

Now showing 1 - 10 of 51

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Precision test for the spectral characteristic of FT-NIR for the measurement of water content of wheat straw
(2019-09-09)
Fonseca, F. G.
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Funke, A.
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Saechua, W.
;
Sirisomboon, P.
Near Infrared (NIR) Spectroscopy is widely employed as a rapid technique for the evaluation of properties of biomass materials. Precision and accuracy of the instruments is an important aspect in order to minimize error in the determination of results. The objective of this publication is to determine scanning repeatability and reproducibility of the NIR spectrometer for wheat straw (Triticum aestivum L.), using either a fixed scan or a rotating scan. The former presented marginally better repeatability but worse reproducibility. Samples in equilibrium with the local atmosphere versus samples of controlled and different moisture contents were also compared, and the latter performed better on the precision test but both fixed and rotating scans. As the ultimate objective of this test is the use of this method to determine variations between different moisture content, and as the rotating scan presents better reproducibility, this method was selected as the reference method for further NIR analyses focused on the variation of moisture content.
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Near infrared spectroscopy as an alternative method for rapid evaluation of toluene swell of natural rubber latex and its products
(2018-06-01)
Lim, Chin Hock
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Sirisomboon, Panmanas
Toluene swell or equilibrium swelling is universally used by rubber factories to measure the degree of crosslink of their compounded or prevulcanized latices at different stages of production. To apply near infrared spectroscopy for rapid and accurate quality control, spectral acquisition of prevulcanized latex, thin film and thick film was performed using a Fourier transform near infrared spectrometer in diffuse reflection mode across the wavenumber range of 12,500–3600 cm1. For prevulcanized latex an effective model was developed using partial least squares regression with preprocessing (first derivative + straight line subtraction method). The coefficient of determination (r2), root mean square error of cross validation and bias of the validation set were 0.71, 3.93% and 0.005%, respectively. For the thin film model the r2, root mean square error of cross validation and bias were 0.65, 4.01% and 0.028%, respectively. Whereas for the thick film model the r2, root mean square error of cross validation and bias were 0.70, 4.00% and 0.006%, respectively. Three models including prevulcanized latex, thin film and thick film were validated by 23 unknown samples, providing standard error of prediction and bias of 5.357 and 2.494, 4.565 and 1.001 and 3.641 and 0.961%, respectively, for prevulcanized latex, thin film and thick film. The model developed for the thick film spectra gave the best results.
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Rapid evaluation of fat content in curry soup containing coconut milk by using near infrared spectroscopy
(2018-02-01)
Cheevitsopon, Ekkapong
;
Sirisomboon, Panmanas
The feasibility of a near infrared spectroscopy to evaluate the fat content in instant curry soup containing coconut milk including green curry, red curry, massaman curry and panang curry was investigated. The soup samples were collected from a processing line and as the finished product. There were also fat content-adjusted samples where the curry was made from the same recipe as in the processing line but increasing by 30, 60 and 90% coconut milk and reducing by 30, 60 and 90% coconut milk from normal. A Fourier transform near infrared spectrometer was used to collect scans. A partial least squares regression model for fat content was established using near infrared spectral data in conjunction with reference data, which was validated using a leave-one-out cross-validation and test set validation. The test set validation, using a set of unknown samples, showed better prediction performance. The best model developed using vector normalization spectral pre-treatment on 9404–7498 and 6102–5446 cm−1 provided coefficient of determination, root mean square error of prediction, bias and ratio of performance to interquartile values of 0.90, 0.9%, −0.1% and 1.2, respectively, for the validation samples. However, the model developed using samples without fat content adjusted samples gave a slightly lower coefficient of determination (0.89), but provided a lower root mean square error of prediction (0.5%) and acceptable ratio of standard error of validation to the standard deviation (3.2). In addition, the vibration bands of CH<inf>2</inf> which was in the long chain fatty acid moiety highly influenced the prediction of fat content in the curry soup. The near infrared spectroscopy protocol developed for the determination of fat could be applied in the instant curry soup production line.
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Potential of near infrared spectroscopy as a rapid method to detect aflatoxins in brown rice
(2019-06-01)
Dachoupakan Sirisomboon, C.
;
Wongthip, P.
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Sirisomboon, P.
Brown rice is a main popular health food with high nutritional value and health benefits. As a result of poor post-harvest drying and inappropriate storage conditions, rice grains are often damaged through fungal spoilage as well as mycotoxin production. The objective of this research was to evaluate the possibility of using the near infrared spectroscopy, with a wavenumber range between 12500 and 4000 cm−1 (800–2500 nm), as a rapid method for detection of aflatoxins in brown rice. Firstly, storage trials were carried out to generate representative of samples contaminated and non-contaminated with aflatoxins. These data were used to create a partial least squares regression model using 120 brown rice samples with the required near infrared spectral data and aflatoxin concentration levels that were determined using a standard enzyme-linked immunosorbent assays method. The accuracy of developed models was externally validated using the test set. The statistical model developed from the treated spectra (vector normalization; SNV) provided the best accuracy in prediction with a coefficient of determination of prediction (r2) of 0.95, a root mean square error of prediction of 415.00 µg kg−1 and a bias −54.00 µg kg−1. The model developed showed good predictive performance which suggests that it could have practical applications as a rapid method to detect aflatoxins in brown rice.
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Determination of dry matter and soluble solids of durian pulp using diffuse reflectance near infrared spectroscopy
(2015-01-01)
Onsawai, Phalanon
;
Sirisomboon, Panmanas
Fourier transform near infrared spectroscopy was used as a non-invasive technique for the determination of dry matter and soluble solids of durian pulp. A set of 25 fruit was randomly harvested every 10 days, starting from 80 days until 127 days after the onset of fruit development covering six levels of maturity (80 days, 90 days, 100 days, 110 days, 120 days and 127 days). After applying ethephon on the fruit stems, the fruits were kept for 3 days at room temperature and allowed to ripen. Only the pulp of the durian was scanned. The dry matter and soluble solids reference values of the samples were determined by a hot-air-oven method and using a refractometer, respectively. Prediction models using half the samples related the spectral data, and dry matter and soluble solids data were subsequently established using partial least-squares regression and validated using the other half of the samples in a prediction set. A full cross-validation was also generated using all 149 samples. Both the half-of-the-samples model and the all-sample model were then validated using a true validation set of samples collected in a later year. When tested against the validation half of the samples, the halfof- the-samples model predicted dry-matter content with a coefficient of determination (r2) and root mean square error of prediction (RMSEP) of 0.89 and 3.60%, respectively, and for soluble solids content 0.55 and 1.63 °Brix (Bx), respectively. When tested on samples from a later season, the model for dry-matter content returned an r2, RMSEP and bias of 0.26, 6.10% and 2.16%, respectively, and for soluble solids content 0.27, 1.25 °Bx and 1.09 °Bx, respectively. The cross-validated model for dry matter yielded a slightly better r2 and root mean square error of cross-validation (RMSECV) of 0.90 and 3.58%, respectively, however, the model for soluble solids did not provide a better r2 and RMSECV: 0.51 and 1.81 °Bx, respectively. When tested on samples from a later season, the cross-validated models gave, r2, RMSEP and bias of 0.15, 5.17% and 1.49%, respectively, for dry-matter content, and for soluble solids content 0.37, 1.32 °Bx and 1.23 °Bx, respectively. The poor results obtained when predicting dry matter in samples in later seasons indicate that samples from several seasons must be included in the set of calibration samples. This is the first report on the application of NIR spectroscopy to evaluate the dry matter and soluble solids of durian pulp and could be useful to customers, exporters, importers and also postharvest technologists. However, prediction accuracy was not demonstrated in the model for durian pulp soluble solids, possibly because of the effect of ethephon applied after harvesting to induce ripening within 3 days to make the fruit suitable for consumption. In addition, it was found that the vibration bands of cellulose and fat, and those of aromatic, CH2 and sucrose highly affected the predictions of dry matter and soluble solids in the durian pulp, respectively.
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Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice
(2013-09-01)
Dachoupakan Sirisomboon, C.
;
Putthang, R.
;
Sirisomboon, P.
The objective of this research was to apply the near infrared spectroscopy (NIRS), with a wavelength range between 950 and 1650 nm, to determine the percentage of fungal infection found in rice samples. The total fungal infection and yellow-green Aspergillus infection, which is often indicative of aflatoxigenic fungal infection, are the focus of this research. Spectra were obtained on 106 rice samples, by reflection mode, including 90 naturally contaminated samples, and 16 artificially contaminated samples. Calibration models for the total fungal infection were developed using the original and pretreated absorbance spectra in conjunction with partial least square regression (PLSR). The statistical model developed from the untreated spectra provided the greatest accuracy in prediction, with a correlation coefficient (. r) of 0.668, a standard error of prediction (SEP) of 28.874%, and a bias of -0.101%. For yellow-green Aspergillus infection, the most accurate predictive statistical model was developed using a pretreated (maximum normalization) NIR spectra, with the following statistical characteristics (. r = 0.437, SEP = 18.723% and bias = 4.613%). Therefore, the result showed that the NIRS could be used to detect aflatoxigenic fungal contamination in rice with caution and the technique should be improved to get better prediction model. However, there is an evident from NIR spectra that the moisture and starch content in rice affects the overall extent of fungal infection. © 2013 Elsevier Ltd.
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Near infrared scanning precision analysis for intact durian fruits (cv. Chanee, Kanyao and Monthong)
(2019-09-09)
Chanachot, K.
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Saechua, W.
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Sirisomboon, P.
In order to develop a model of near infrared (NIR) spectroscopy, it is important to first perform the precision analysis of scanning of the NIR spectrometer. Therefore, it was the main aim of this paper by evaluating the scanning repeatability and reproducibility on intact durian fruit of 3 varieties including Chanee, Kanyao and Monthong using 3 spectrometers including FT-NIR spectrometer (MPA.), Long wavelength diode array spectrometer (MICRO NIR PRO) and Short wavelength diode array spectrometer (FQA NIR GUN). Results show that the lowest repeatability was of MPA for the scanning of Chanee and Kanyao. The lowest reproducibility was of the scanning by MICRO NIR PRO for all varieties. Therefore, from precision analysis of intact durian scanning, the MPA and MICRO NIR PRO could be recommended for the scanning to get the spectra for development the NIR predictive model for identify the geographic origin and variety of the durian.
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Nondestructive estimation of maturity and textural properties on tomato 'Momotaro' by near infrared spectroscopy
(2012-10-01)
Sirisomboon, Panmanas
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Tanaka, Munehiro
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Kojima, Takayuki
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Williams, Phil
Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety "Momotaro". Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix. © 2012 Elsevier Ltd. All rights reserved.
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Determination of the gamma-aminobutyric acid content of germinated brown rice by near infrared spectroscopy
(2014-01-01)
Kaewsorn, K.
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Sirisomboon, P.
Rice rich in gamma-aminobutyric acid (GABA) has been increasing in popularity worldwide, particularly within the commercially important health food market. The aim of this research was to develop a near infrared (NIR) spectroscopic method to evaluate the GABA content of germinated brown rice. Germinated brown rice (GBR) from two groups was used in this study. The first group contained GABA adjusted rice samples produced from germinated rough rice, which had been soaked for 24 h and 48 h and then incubated for 0 h, 6 h, 12 h, 18 h, 24 h, 30 h and 36 h (GBR samples). The second group was the GBR purchased from local markets (MGBR samples). The GABA content of each sample was subsequently determined by high performance liquid chromatography (HPLC). A prediction model for the GABA content was subsequently established using the NIR spectral data in conjunction with partial least square regression (PLSR), which was validated using test set validation. There were three optimal models obtained from GBR samples, MGBR-Khao Dawk Mali 105 samples and MGBR-various varieties. The first model was established using spectral data pre-treated with first derivative + multiple scatter correction and the second and third models were from first derivative + vector normalisation pre-treated spectra. The coefficient of determination (r2), root mean squared error of prediction (RMSEP) and a bias of 0.97-0.98 mg, 0.21-0.52 mg per 100 g dry matter and -0.285-0.067 mg per 100 g dry matter, respectively, were obtained. This is the first report on the application of NIR spectroscopy to evaluate the GABA content of the germinated brown rice, which could prove useful in an industrial applications and consumers. © IM Publications LLP 2014.
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Thermal properties of Jatropha curcas L. kernels
(2012-12-01)
Sirisomboon, Panmanas
;
Posom, Jetsada
The thermal properties of Jatropha curcas L. kernels, including thermal diffusivity and conductivity were studied where kernels in 3 different forms were investigated, i.e. whole kernels, 1/4 kernels and kernel powder. The specific heat of kernels in 2 different forms was also assessed (i.e. small piece of kernel and powdered kernel). The experiments were undertaken using kernels obtained at 2 different harvesting periods, a mature, ripe stage (yellow fruit) and a fully ripe stage (black fruit). The thermal diffusivity of whole kernels, 1/4 kernels and kernel powder obtained from yellow fruit were 9.303 × 10-6, 8.370 × 10-6 and 7.456 × 10-6 m2 s-1, respectively, while those obtained from black fruit were 8.792 × 10-6, 7.723 × 10-6 and 6.652 × 10-6 m2 s-1, respectively. The thermal conductivity of whole kernels, 1/4 kernels and powdered kernels increased with increasing temperature from 25 to 100 °C, ranged from 0.0663 to 0.1181, 0.0593 to 0.1087 and from 0.0536 to 0.1015 W m-1 °C-1 for the 3 kernel forms derived from yellow fruit, respectively. For the different kernel forms obtained from the black fruit the corresponding values were 0.0608-0.0977, 0.0527-0.0841 and 0.0452-0.0740 W m-1 °C-1, respectively. The specific heat of the small piece kernel and powder obtained from yellow fruit, ranged from 0.7852 to 1.3929 and from 1.3823 to 2.4510 kJ kg-1 °C-1, respectively. For black fruit, the values ranged from 0.6258 to 0.9933 and from 0.8930 to 1.7810 kJ kg-1 °C-1, respectively. Linear relationships between thermal conductivity and specific heat with temperature are reported. © 2012 IAgrE.

Sirisomboon, Panmanas

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