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Evaluating limit of detection and quantification for higher heating value and ultimate analysis of fast-growing trees and agricultural residues biomass using NIRS
(2023-10-09)
Shrestha, Bijendra
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Shrestha, Zenisha
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Posom, Jetsada
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Sirisomboon, Panmanas
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Shrestha, Bim Prasad
Accurate non-destructive assessment of biomass energy properties is essential for optimizing its use as an alternative fuel. In this study, 200 biomass samples were used to determine higher heating value (HHV) and 120 biomass samples for analyzing ultimate analysis parameters using near-infrared spectroscopy within the full wavenumber range of 12489.48 – 3594.87 cm-1. The samples were grounded, and five different types of partial least squares regression (PLSR) models were developed using traditional preprocessing, multi-preprocessing (MP) with 5 range, MP with 3 range, genetic algorithm, and successive projection algorithm. Limit of detection (LOD) and quantification (LOQ) were calculated using the best-performing model among five different PLSR models for HHV in kJ/kg, as well as the weight percentage (wt.%) of carbon (C), oxygen (O), hydrogen (H), and nitrogen (N). The LOD and LOQ for HHV were calculated as 622.42 kJ/kg and 1886.13 kJ/kg, respectively. Additionally, LOD and LOQ for ultimate analysis parameters, including C, O, H, and N were calculated as: 3.24 weight percentage (wt.%) and 9.81 wt.% for C, 2.04 wt.% and 6.18 wt.% for O, 0.35 wt.% and 1.05 wt.% for H, and 0.22 wt.% and 0.68 wt.% for N. The LOD and LOQ values for HHV, C, O, and H were lower than the minimum reference values used for model development, demonstrating the models’ high sensitivity and potential to reliably detect and precisely quantify these parameters. However, the LOD and LOQ values exceeded the minimum reference value used during model development for the N, indicating that the selected models have certain limitations in assessing the N content in biomass. The sample range should be expanded for wt.% of N to enhance the model’s performance, surpassing the LOD and LOQ values. This will improve the overall sensitivity of the model for reliable detection and quantification of N content in biomass samples.
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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
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Phuphaphud, Arthit
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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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Evaluation of pyrolysis characteristics of milled bamboo using near-infrared spectroscopy
(2017-01-01)
Posom, Jetsada
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Saechua, Wanphut
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Sirisomboon, Panmanas
This paper reports the development of a rapid and low-cost method based on near-infrared spectroscopy as an alternative for thermogravimetric determination of the pyrolysis characteristics, including T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, T<inf>offset</inf>and DTG<inf>peak</inf>, of milled bamboo. T<inf>onset</inf>is the extrapolated onset temperature that is calculated from the partial peak resulting from the decomposition of the hemicellulose component, T<inf>sh</inf>is the temperature corresponding to the overall maximum of the hemicellulose decomposition rate, DTG<inf>peak</inf>is the overall maximum of the cellulose decomposition rate, T<inf>peak</inf>is the temperature corresponding to the overall maximum of the cellulose decomposition rate and T<inf>offset</inf>is the extrapolated offset temperature of the DTG<inf>peak</inf>curves determined using thermogravimetric analysis (TGA). The models may be used to control the pyrolysis processes of bamboo to achieve the most economical and environmental conditions. 80 samples of bamboo with various circumferences of culms in the ranges of approximately 16–18, 18–20, 20–22, 22–24, 24–26, 26–28, 28–30, 30–32, 32–34, 34–36, 36–38 and 38–40 cm were randomly collected for optimization of the models. The models were optimized by partial least squares regression (PLSR) with 80% of samples for the calibration set and 20% for the validation set. For T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, T<inf>offset</inf>and DTG<inf>peak</inf>the models showed coefficients of determination (R2) of 0.566, 0.845, 0.917, 0.973, and 0.671; root mean square errors of prediction (RMSEP) of 9.7 °C, 4.36 °C, 3.77 °C, 2.66 °C, and 0.428 wt loss %/min; ratios of prediction to deviation (RPD) of 1.52, 2.58, 3.48, 3.55, and 1.75; and biases of −0.344 °C, −0.765 °C, 0.349 °C, −5.41 °C, and 0.045 wt loss %/min, respectively. In addition, the results showed that pyrolysis characteristics did not depend on the circumference. The vibrational bands of water and CH<inf>3</inf>, O[sbnd]H stretch, first overtones of Ar–OH, CH<inf>2</inf>and HC[dbnd]CH in the cellulose and lignin structures, O[sbnd]H hydrogen bonds of polyvinyl alcohol and C[sbnd]H stretch corresponding to the first overtone of CH<inf>2</inf>had the highest influence on the values of T<inf>onset</inf>, T<inf>sh</inf>, T<inf>peak</inf>, and T<inf>offset</inf>, respectively. The vibrational band of the C[sbnd]O[sbnd]C asymmetrical stretches of cellulose and hemicellulose, and the combination of O[sbnd]H stretch and HOH bend of polysaccharides influenced the DTG<inf>peak</inf>value. These results are beneficial for studying the thermal behaviour of milled bamboo as a potential resource for producing biofuels, especially in the pyrolysis process.
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Evaluation of the moisture content of Jatropha curcas kernels and the heating value of the oil-extracted residue using near-infrared spectroscopy
(2015-02-01)
Posom, Jetsada
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Sirisomboon, Panmanas
The use of near-infrared spectroscopy for evaluation of moisture content of Jatropha curcas kernels and heating value of its residue after oil extraction were studied. In total, 100 samples of whole kernels from green, yellow and black fruits and oven-dried kernels scanned in diffuse reflection mode using a Fourier transform NIR spectrometer at wave numbers of 1,250,000-400,000m-1 were used to develop moisture-predicting models. The corresponding residues after the oil extraction of the samples scanned in transflection mode using the same spectrometer and wave number range were used to develop the heating-value-predicting models. The models correlating the spectral data and the corresponding values measured using the reference method were developed by partial least squares regression and were validated using a test set. For the moisture content and heating value, coefficients of determination (R2) were 0.969 and 0.860, root mean square errors of prediction (RMSEP) were 4.0%wb and 360Jg-1, biases were -0.7%wb and -17Jg-1 and ratios of prediction to deviation (RPD) were 5.7 and 2.6, respectively. In addition, vibration bands of fibre and cellulose had important effects on the prediction of the heating value.
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Evaluation of the thermal properties of Jatropha curcas L. kernels using near-infrared spectroscopy
(2014-01-01)
Posom, Jetsada
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Sirisomboon, Panmanas
The determination of the thermal properties, including thermal diffusivity, thermal conductivity, and specific heat, of Jatropha curcas L. kernels was conducted using near-infrared (NIR) spectroscopy. A total of 100 samples of whole kernels from green, yellow and black fruits and oven dried kernels were scanned using a Fourier transform NIR spectrometry over the range of 1,250,000-400,000m-1. Models correlating the spectral data and the thermal properties measured by a reference method were developed by partial least squares regression and validated by test set validation. The results showed that for thermal diffusivity, thermal conductivity at 40°C and 100°C and specific heat at 40°C and 100°C, the coefficients of determination (R2) were 0.5968, 0.7592, 0.7509, 0.4211 and 0.6396%, respectively; the root mean square errors of prediction (RMSEP) were 1.1×10-6m2s-1, 0.0169Wm-1°C-1, 0.0685Wm-1°C-1, 5.88kJkg-1°C-1 and 15.8kJkg-1°C-1; the biases were -2.52×10-7m2s-1, 2.85×10-3Wm-1°C-1, 2.52×10-2Wm-1°C-1, 1.83kJkg-1°C-1 and 4.69kJkg-1°C-1; and the ratios of prediction to deviation (RPD) were 1.57, 2.04, 1.98, 1.28 and 1.64, respectively. These show the possibility of using NIR spectroscopy as an alternative method to estimate the properties of Jatropha kernels, especially the thermal conductivity at 40°C and 100°C. © 2014.

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