Nakawajana, Natrapee

Preferred name

Alternative Name

Nakawajana, N.

Main Affiliation

KMITL Prince of Chumphon Campus

natrapee.na@kmitl.ac.th

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The energy potential evaluation of biomass fuel from weed pellets
(2023-03-01)
Saechua, W.
;
Posom, J.
;
Hongwiangjan, J.
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Nakawajana, N.
The giant sensitive plant had the most appropriated qualifications for pellet production, as it contained a low content of moisture (3.07%) and ash (2.68%), and a high percentage of fixed carbon (14.13%) and volatile matter (80.12%) with the highest lower heating value of 18,334.08 J/g. It was found that a higher mixing ratio of water for pelletization led to increasing moisture and ash content but resulted in lower volatile matter and fixed carbon. The best mixing ratio for was 8% of water by weight, at which the evaluated properties of the giant sensitive plant pellets: LHV, FC, VM and ash content, were 18,747.46 J/g, 10.92%, 78.77% and 2.24%, respectively. The evaluation of the physical properties of the pellets revealed that a higher amount of water resulted in a larger diameter, greater length and higher fines content. Comparison of pellets from this work with the standard classes of biomass pellets found that the giant sensitive plant pellets could be classified in class I3. It was clearly seen that the weed can be utilized as a useful biomass fuel and formed in a pellet shape for combustion in industrial sectors.
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Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp
(2021-11-01)
Saechua, Wanphut
;
Sharma, Sneha
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Nakawajana, Natrapee
;
Leepaitoon, Kritsanaphon
;
Chunsri, Rashphon
The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (R<inf>C</inf>2), and prediction (R<inf>P</inf>2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.
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Determination of translucent content in mangosteen by means of near infrared transmittance
(2012-03-01)
Terdwongworakul, Anupun
;
Nakawajana, Natrapee
;
Teerachaichayut, Sontisuk
;
Janhiran, Athit
Translucent flesh disorder is undesirable in mangosteen meant for export. However, mangosteens are judged as translucent when the translucent flesh is visible on the pulp surface regardless of the quantity of the internal translucent flesh which may result in some mangosteen assessed as normal having the same amount of translucent flesh content as a mangosteen judged as translucent. The critical amount of translucent flesh to be visible on the pulp surface needs to be determined for assessment purposes. A non-destructive technique to measure the translucent content is a practical tool as the first step towards the establishment of the critical value. A non-destructive model was developed to estimate the translucent content in mangosteens using near infrared transmittance. The translucent area of the flesh section on the fruit surface was used to indicate the translucent content. The effects of the orientation of the fruit and also of the light source to the relative position of the detector as well as the effect of the measurement position of the fruit on the predictive performance were examined. The results showed that the best partial least squares model was achieved with spectra acquired from the fruit position which revealed the largest flesh segment (prediction correlation coefficient was 0.86 and root mean square error of prediction was 7.58%). The horizontal stem-calyx fruit axis and a 135° angle from the light source relative to the detector were the optimal fruit orientation and configuration for measurement. © 2011 Elsevier Ltd. All rights reserved.
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Minimally destructive assessment of mangosteen translucency based on electrical impedance measurements
(2016-02-01)
Nakawajana, Natrapee
;
Terdwongworakul, Anupun
;
Teerachaichayut, Sontisuk
Electrical impedance spectroscopy in a frequency range of 1 kHz-200 kHz was studied to develop a classifying model for translucent mangosteen. The optimal configuration of the measurement was investigated. Transverse alignment of two measuring needles with the stem-calyx axis and with the measured position on the part of the pericarp pertinent to the largest flesh segment proved to be the optimal configuration. The optimal electrical parameters were selected at frequencies of 1, 4, 7, 8, 14, 47, 73, and 81 kHz as the classifying variables based on the student t-test analysis for a significant difference between the normal and translucent mangosteen and the largest difference of the average values of the electrical parameters. The differences in the electrical parameters and their reciprocals were the optimal classifying variables. The model constructed from the samples from two seasons was robust in terms of seasonality, providing a classification accuracy of 82.7%. The difference in the initial moisture content of the pericarp was justifiably compensated by the differences in the electrical parameters. The EIS technique was suitable for measurement of mangosteen samples at the maturity color stage in which the sample contained no yellow latex in the pericarp.
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Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression
(2022-12-01)
Pornchaloempong, Pimpen
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Sharma, Sneha
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Phanomsophon, Thitima
;
Srisawat, Kraisuwit
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Inta, Wasan
Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.
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A low-cost system for moisture content detection of bagasse upon a conveyor belt with multispectral image and various machine learning methods
(2021-05-01)
Nakawajana, Natrapee
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Lerdwattanakitti, Patchara
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Saechua, Wanphut
;
Posom, Jetsada
;
Saengprachatanarug, Khwantri
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2 ) 0.72, root mean square error of prediction (RMSEP) 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.
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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.
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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.

Nakawajana, Natrapee

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