KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 25

Some of the metrics are blocked by your
consent settings
Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection
(2024-01-05)
Phanomsophon, Thitima
;
Jaisue, Natthapon
;
Worphet, Akarawhat
;
Tawinteung, Nukoon
;
Khurnpoon, Lampan
Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500–3,600 cm−1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = −0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.
Some of the metrics are blocked by your
consent settings
Near-infrared spectroscopy, hyperspectral, multispectral imaging principles and applications in energy properties of biomass
(2023-08-21)
Posom, Jetsada
;
Shrestra, Bijendra
;
Maraphum, Kanvisit
;
Pitak, Lakkana
;
Saengprachatanarug, Khwantri
Biomass is renewable energy which is zero neutrality carbon energy. It is used for generating heat energy and electrical energy. Therefore, the use of biomass with high efficiency is important and the quality of biomass related to its energy should be measured before utilization and trading. The measurement of energy indexes of biomass is necessary to the thermal conversion process and the trading of biomass. However, the conventional measurement methods are laborious and take a long time, with a lot of costs. In recent years, near infrared spectroscopy (NIR) and imaging technologies (hyperspectral and multispectral images) have been widely investigated and applied as non-destructive, reliable and accurate techniques to monitor the quality and composition of biomass. This chapter contains the principle of NIR and imaging technique including essential component principles, NIR and imaging technique procedures, novel model development methods and applications. The non-destructive measurement of biomass quality as the real time and non- contact measurement will be represented. Moreover, this chapter will describe the application of NIR and imaging techniques for analysing the energy indexes of biomass, such as heating value or calorific value, proximate data, elemental composition, combustion index, pyrolysis characteristics, mechanical properties and so on.
Some of the metrics are blocked by your
consent settings
Texture evaluation of cooked parboiled rice using nondestructive milled whole grain near infrared spectroscopy
(2021-01-01)
Onmankhong, Jiraporn
;
Sirisomboon, Panmanas
One consumer acceptability criterion of cooked parboiled rice is its texture, particularly hardness and toughness. The samples were obtained from parboiled rice factory for export. The hardness and toughness calibration models based on milled whole grain near infrared spectroscopy was developed. The ISO 11747 Rice-Determination of Rice Kernel Resistance to Extrusion after Cooking method was used as reference test. The models were established using partial least squares regression (PLSR), principal component regression (PCR) and support vector machine regression (SVM). The PLSR optimal calibration model of hardness with moving average smoothing pre-processing gave coefficient of determination of validation (r2), root mean square error of prediction (RMSEP) and ratio of prediction to deviation (RPD) of 0.70, 7.24 N and 1.93, respectively. The PCR optimal model of toughness using mean normalization preprocessing provided r2, RMSEP and RPD of 0.66, 38.00 Nmm and 1.75, respectively. According to RPD threshold, the models were fair for prediction application. This feasibility study suggested that the NIR protocol developed was applicable for real use due to the error of the NIR scanning and other unexplained errors was only 5% and 1% for hardness and toughness models, respectively. However, the sample preparation before texture analysis has to be improved.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
On-line measurement of activation energy of ground bamboo using near infrared spectroscopy
(2019-04-01)
Sirisomboon, Panmanas
;
Posom, Jetsada
On-line measurement of activation energy (Ea) is very important in supporting the thermal conversion process. The main objective of this study was to evaluate the Ea of ground bamboo using near infrared spectroscopy in real time. 80 bamboo samples with different diameters were selected using random sampling. Ea was determined using the Coats-Redfern method, and Ea of reaction order (n) at n = 1 and n≠1 was investigated. The performance of on-line measurement predicted by PLS modelling for Ea at n = 1 and Ea at n≠1 showed coefficients of determination of 0.781 and 0.714, respectively; standard error of prediction of 5.249 and 6.858 kJ/mol, respectively; and bias values of −1.0628 and −1.871 kJ/mol, respectively. Both PLS models were found to be fair and could be applied toward screening. The results showed that the vibration bands of lignocellulosic components (CH<inf>2</inf>, hemicellulose, cellulose, and lignin) highly influenced model development. Moreover, internal relationships were identified among Ea, the pre-exponential factor (A), and n, such as A (1/min) = 63251 × e0.2200×Ea (at n = 1), A (1/min) = 33719 × e0.2267×Ea (at n≠1), and n = 0.008 × Ea+0.254. These relationships can be used to evaluate A and n if Ea is known. In the case of this study, Ea was forecasted using an NIR model.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Prediction of higher heating value, lower heating value and ash content of rice husk using FT-NIR spectroscopy
(2018-09-30)
Nakawajana, Natrapee
;
Posom, Jetsada
;
Paeoui, Jaruwat
Rice husk is the significant waste residue to be used as renewable energy. The growth of the use on rice husk for generating electricity lead to the verification of its properties. This research aimed to predict higher heating value (HHV), lower heating value (LHV), and ash content (A) of rice husk using Fourier Transform near infrared (FT-NIR) spectroscopy. Rice husk samples used in this experiment were collected from variable areas in Thailand in order to improve the model and get the robust model. The models were built using partial least squares (PLS) regression and validated by unknown sample collected from different area to calibration set. The prediction of HHV, LHV and A were represented the root mean square error of cross validation (RMSECV) of 119 J/g, 119 J/g, and 0.859%wb, respectively. The calibration model can predict the unknown sample successfully with the relative standard error of prediction (RSEP) of 1.104 %, 1.159 %, and, 5.975 %, which implied good performance of NIR model for future prediction. The results suggested that HHV, LHV, and A models should be able to assess the properties of rice husk samples and showed that NIR was reliable and suitable method for combustion system to screening material.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
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
;
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.
Some of the metrics are blocked by your
consent settings
Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy
(2017-10-01)
Posom, Jetsada
;
Sirisomboon, Panmanas
This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g-1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g-1, -0.43%, 0.03% and -0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results