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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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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
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Sharma, Sneha
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Nakawajana, Natrapee
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Leepaitoon, Kritsanaphon
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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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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis
(2021-04-01)
Pitak, Lakkana
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
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Posom, Jetsada
The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line-scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2<inf>p</inf>) and standard error of prediction (SEP) of 0.83 and 1.33%; 0.84 and 0.17%; and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets.
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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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Shortwave near-infrared spectroscopy for rapid detection of aflatoxin B1 contamination in polished rice
(2019-01-01)
Putthang, R.
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Sirisomboon, P.
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Sirisomboon, C. Dachoupakan
The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B<inf>1</inf> (AFB<inf>1</inf>) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB<inf>1</inf> and 15 samples artificially contaminated with AFB<inf>1</inf>. Quantitative calibration models to detect AFB<inf>1</inf> were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 µg/kg, and a bias of-0.778 µg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 µg/kg, bias = 0.015 µg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 µg/kg, bias = 0.008 µg/kg). A classification-based partial least squares discriminant analysis model of AFB<inf>1</inf> contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB<inf>1</inf> contamination. HIGHLIGHTS • Shortwave near-infrared spectroscopy allowed rapid detection of AFB<inf>1</inf> in polished rice. • The partial least squares model provided the best accuracy for prediction (r = 0.967). • The partial least squares discriminant analysis model had a classification accuracy of 90%.
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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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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
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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.
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Evaluation of soluble solids of curry soup containing coconut milk by near infrared spectroscopy
(2017-06-01)
Sirisomboon, Panmanas
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Nawayon, Jutarat
The aim of this research was to do a feasibility study of near infrared spectroscopy to evaluate soluble solids of curry soup containing coconut milk. The soup samples were collected from mixing tanks, water adjusting tanks, an ultra-high temperature process line and laminated cartons. There were also soluble solids adjusted samples by adding or reducing coconut sugar where the curry was made from the same recipe as in the processing line but increasing 30, 60 and 90% coconut sugar and reducing 30, 60 and 90% coconut sugar from normal. There were 119 samples in total. Sample was scanned with an FT-NIR spectrometer. A prediction model for soluble solids was established using near infrared spectral data in conjunction with partial least squares regression. When validated using a set of test samples, the model developed using spectra pretreated by min-max normalization in the range 9403.8–6094.3 cm-1, provided a coefficient of determination (r2), root mean square error of prediction, bias and ratio of performance to interquartile of 0.92, 1.0°Brix, 0.1°Brix and 2.4, respectively. It showed the potential of using near infrared spectroscopy to evaluate soluble solids in curry soup. With further development using more natural samples, a more robust model could be achieved to evaluate soluble solids in curry soup in a processing factory.
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Evaluation of prevulcanisate relaxed modulus of prevulcanised natural rubber latex using Fourier transform near infrared spectroscopy
(2017-01-01)
Lim, Chin Hock
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Sirisomboon, Panmanas
The analysis of the cross-link density of prevulcanised natural rubber latex using near infrared spectroscopy was conducted using a Fourier transform near infrared spectrometer in diffuse reflection mode over the wavenumber range of 12500- 3600 cm-1. As the density of cross-link is an indication of the degree of cure, hence the properties of the latex products, the proposed method is useful for industrial purposes. For samples of prevulcanised latex of 50% total solids content (i.e. PV 50%) at 100% extension (prevulcanisate relaxed modulus 100%), the best model was developed using the partial least squares regression from the spectra, which were pre-treated using the first derivative method, where the coefficient of determination (r2), root mean square error of prediction and bias were 0.66, 6.06×104 Nm-2 and 1.63×104 Nm-2, respectively. The ratio of standard error of prediction to the standard deviation of the reference data in the prediction sample set was 1.8. This model could be used for screening. For samples at 300% extension (prevulcanisate relaxed modulus 300%) for PV 50%, the best model was developed using spectra pre-treated for scattering correction: r2, root mean square error of prediction and bias were 0.88, 6.74×104 Nm-2 and 1.35×104 Nm-2, respectively and the ratio of prediction to deviation was 3.0. Hence, the near infrared spectroscopy technique can be utilised as a rapid screening method for estimating the cross-link densities of prevulcanised natural rubber latex.
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Feasibility study on the evaluation of the dry rubber content of field and concentrated latex of Para rubber by diffuse reflectance near infrared spectroscopy
(2013-01-01)
Sirisomboon, Panmanas
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Kaewkuptong, Apidul
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Williams, Phil
The analysis of the dry rubber content (DRC) of Para rubber latex, including field latex and concentrated latex, using near infrared spectroscopy was conducted using a Fourier transform near infrared (FT-NIR) spectrometer in diffuse reflection mode over the wavenumber range of 4000-10,000 cm -1. The proposed method is useful for industrial purposes. The best model was developed using the partial least square regression (PLSR) from the spectra, which were pretreated using the 2nd derivative method, where the correlation (r2), standard error of prediction (SEP) and bias were 0.997, 0.3398% and -0.0239%, respectively. The ratio of standard deviation (SD) to SEP of the reference data in the prediction sample set (RPD) was 18.18 and the ratio of the range to the SEP of the prediction set (RER) was 74.4. The model was validated using a new batch of samples and the prediction performance was good with an r2 of 0.999, a SEP of 0.3898% and a bias of -0.0008%. Therefore, the NIR spectroscopy technique can be used as an accurate and rapid method for estimating the DRC of Para rubber latex for both field and concentrated latex. © IM Publications LLP 2013. All rights reserved.

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