Sirisomboon, Panmanas

Preferred name

Alternative Name

Sirisomboon, P.

Main Affiliation

School of Engineering

panmanas.si@kmitl.ac.th

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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.
;
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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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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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.
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Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy
(2024-01-01)
Shrestha, Bijendra
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Posom, Jetsada
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Sirisomboon, Panmanas
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Shrestha, Bim Prasad
;
Funke, Axel
The ultimate analysis parameters, including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass, were rarely found to be predicted by non-destructive tests to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near-infrared (NIR) raw spectra of non-wood and wood samples from fast-growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5-range, and MP 3-range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples were non-wood samples of 65–67% and wood samples of 33–35%, and the model performance was evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2<inf>P</inf>), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA–PLSR, yielding R2<inf>P</inf>, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR–5 range method with R2<inf>P</inf> of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR-3 range method, resulting in R2<inf>P</inf>, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of the H model provided R2<inf>P</inf>, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in the calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for a robust model, but also must provide their trend line characteristics in the scatter plot, i.e., correlation coefficient (R), slope, and intercept (same slope and slope approached to 1, and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope, and intercept to obtain the better-optimized model was studied. The results show that the different species affected the model performance of each parameter prediction in a different manner, and by scatter plot analysis, which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time the effect has been studied by the principle of a scatter plot.
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A new creep model for studying the non-linear viscoelastic behavior of cooked white, brown and germinated brown Thai jasmine rice by large deformation testing
(2018-08-01)
Sirisomboon, Panmanas
;
Posom, Jetsada
A new creep model with three parameters for non-linear viscoelastic behavior is proposed as ε<inf>t</inf>=ε<inf>0</inf>(1+[Formula presented]), where the applied stress is constant, ε<inf>t</inf> is the strain at retardation time (t), ε<inf>0</inf> is the initial strain and k<inf>1</inf>, k<inf>2</inf> and n are constants. The relationship has been proved using data derived from cooked Thai Jasmine rice including white, brown and germinated brown rice samples. The creep test at high strain was conducted on scoops of cooked rice using a compression test rig. The model developed showed very accurate prediction performance with coefficients of determination (R2) between 0.9991–0.9992 and residual standard errors (RSE) between 0.00030–0.0004.
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Two different portables visible-near infrared and shortwave infrared region for on-tree measurement of soluble solid content of marian plum fruit
(2020-01-01)
Posom, Jetsada
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Soonnamtiang, Navavit
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Kotethum, Patcharapong
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Konjun, Pakhpoom
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Sirisomboon, Panmanas
The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.
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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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Overall precision test of near infrared spectroscopy on mango fruits (Mangifera indica CV. 'Nam Dok Mai Si Thong') by on-line and off-line systems
(2020-09-08)
Lekhawattana, Wachiraya
;
Sirisomboon, Panmanas
The near infrared (NIR) spectroscopy both on-line and off-line scanning was applied on mango fruits (Mangifera indica CV. 'Nam dok mai-si Thong') for the overall precision test. The reference parameter was total soluble solids content (Brix value). The results showed that the off-line scanning had a higher accuracy than on-line scanning. The scanning repeatability of the off-line and on-line systems were 0.00199 and 0.00993, respectively. The scanning reproducibility of the off-line and online systems were 0.00279 and 0.00513, respectively. The reference of measurement repeatability was 0.2. The maximum coefficient of determination (R2max) of the reference measurement was 0.894.
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Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy
(2023-03-01)
Ditcharoen, Sirirak
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
;
Phuphaphud, Arthit
;
Rittiron, Ronnarit
The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.
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Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy
(2018-08-14)
Shrestha, Amrit
;
Sirisomboon, Panmanas
Moisture in biomass plays important role during storage, combustion and pelletization. In order to measure moisture content in bamboo chips, two diode array near-infrared instruments, NIR-Gun (600-1100 nm at 2 nm intervals) and Micro-NIR (1150-2150 nm at 7 nm intervals), were used for scanning bamboo chips. Total number of samples used for developing model after removing outliers was 252. The circumference and moisture content of bamboos used were in the range between 16-39 cm and 39-86% wet basis (wb) respectively. Partial least squares regression technique was used to develop the model to predict the moisture content in bamboo chips. The R2, SECV, SEP, bias and RPD of optimum model of NIR-Gun were found to be 0.924, 2.871% wb, 2.385% wb, -0.250% wb and 3.656, while for Micro-NIR model the values were found to be 0.743, 4.349% wb, 4.499% wb, 0.026% wb and 1.972 respectively. In prediction of moisture content in bamboo chip, both models show the effect of different constituents of bamboo more than moisture. This study indicates that the results are suitable for screening the moisture content in bamboo chips. This would be helpful for process controlling using the moisture parameter during drying, pelletization and thermochemical conversion.

Sirisomboon, Panmanas

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