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Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning
(2025-12-15)
Shrestha, Zenisha
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Shrestha, Bijendra
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Sirisomboon, Panmanas
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Pun, Umed Kumar
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Bajracharya, Tri Ratna
Accurate determination of moisture content is essential for energy efficiency and biomass management for fuel materials such as durian husk. Traditional methods of determining biomass moisture content are time-consuming and require specialized expertise, posing challenges for continuous monitoring. To address this limitation, this study applies Near Infrared Spectroscopy (NIRS) combined with machine learning models to rapidly and accurately assess moisture content. Both linear Partial Least Squares Regression (PLSR) and non-linear approaches were used, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGB). The application of preprocessing techniques, notably the Savitzky-Golay second derivative (SD) and Standard Normal Variate (SNV), significantly augmented the predictive performance, highlighting the importance of data preprocessing in spectral analysis. Synthetic spectral augmentation using Gaussian noise revealed that while SVM and ANN exhibited near-perfect performance, SVM demonstrated quantifiable reliability. This study also demonstrates SVM as the most sensitive and reliable method for detecting and quantifying moisture content in durian husk. This research contributes novel insights to biomass analysis, highlighting the benefits of integrating NIRS and feasibility of explainable machine learning techniques to identify water related spectral parameters to advance aquaphotomics, thereby advancing rapid and accurate biomass characterization.
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Predicting biomass global warming potential with FT-NIR spectroscopy
(2025-12-01)
Gyawali, Prakash
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Shrestha, Bijendra
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Phanomsophon, Thitima
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Posom, Jetsada
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Pornchaloempong, Pimpen
This research is to predict the global warming potential (GWP) of biomass by using Fourier transform near-infrared (FT-NIR) spectroscopy. A partial least squares regression model of 197 biomass chip samples was developed for predicting GWP of fast-growing trees and agricultural residues. The reference value of GWP of biomass sample was calculated by the method provided by Intergovernmental Panel on Climate Change (IPCC). After applying different spectral pretreatments and variable selection methods, the best model for predicting GWP was found using the 1st derivative spectrum pretreatment and covariance method (COVM) based variable selection. The results indicate GWP model exhibit good predictive capabilities, where the model can be usable with caution for any purpose including research, by achieving a coefficient of determination for prediction set (R<sup>2</sup><inf>P</inf>) of 0.86, and ratio of prediction to deviation (RPD) of 2.6. Additionally, the RMSEP of 0.00063 suggests a low prediction error. This pioneering approach presents a swift and efficient means to determine GWP, the complex functionality parameter, which reveals an optimal relationship model, showcasing its efficacy in a significant advancement in the assessment of biomass functionality related to climate change issue. Additionally, the further research is recommended to integrate FT-NIR data with thermogravimetric analyser to simulate of different thermal conversion of biomass type where different emission gases are generated and with gas chromatography–mass spectrometry for evaluation of concentration of the generated gases for further refine GWP predictions which providing more comprehensive insights and exact content of emission gases affect global warming to support the IPCC.
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Non-destructive measurement of Tetrahydrocannabinol (THC) and Cannabidiol (CBD) using near-infrared spectroscopy
(2023-11-01)
Deewatthanawong, R.
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Kongchinda, P.
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Chanapan, S.
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Tontiworachai, B.
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Sakkhamduang, C.
Tetrahydrocannabinol (THC) and cannabidiol (CBD) are cannabinoids which produced by cannabis plants and major compounds found in cannabis products. A predictive method for non-destructive quantification of THC and CBD using near infrared spectroscopy (NIR) technology is developed. The prediction model for THC estimation had coefficient of determination (R-squared) and root mean square error of calibration (RMSEC) values of 0.9994 and 0.1926, respectively. The correlation between THC values of HPLC measurement and NIR prediction showed a correlation coefficient of 0.9078. For CBD prediction, the R-squared and RMSEC values of CBD equation were 0.9995 and 0.0006, respectively. The predicted and measured concentrations of CBD showed good correlation with a regression correlation of 0.9413. The test indicated NIR could be a promising alternative method for THC and CBD evaluation.
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Applying singular value decomposition technique for quantifying the insects in commercial Thai Hommali Rice from NIR Spectrum
(2017-03-01)
Jarruwat, Puttinun
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Choomjaihan, Prasan
Insect infestation in rice stock is a significant issue in rice exporting business, resulting in the loss of product quality, nutrient as well as the economic losses. However, detecting the insect contamination with the traditional sorting techniques were destructive, inaccurate, time consuming and unable to detect the internal insect infestation. This study used near infrared (NIR) spectroscopy for obtaining the absorbent spectra from the insect contamination in two kinds of rice samples, Milled Hommali rice (MHR) and Brown Hommali rice (BHR). The mathematical methods of partial least squares (PLSs) regression and singular value decomposition (SVD) were employed to construct the predicting model. The statistical analysis results, R2, RMSEP, RPD and bias, concluded that the predictive models from PLS for MHR and BHR were 0.95 and 0.90, 0.014 and 0.019, 4.79 and 3.11, as well as -0.007 and -0.008, respectively; while the statistical analysis results from SVD for MHR and BHR were 0.97 and 0.96, 0.012 and 0.013, 5.71 and 5.39, as well as -0.003 and 0.002, respectively. It showed that SVD technique performed better than PLS technique which shows that using the advantage of SVD technique required less amounts of wave numbers for predicting and was possible to construct the low cost handheld equipment for detecting the insects in rice samples.
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Nondestructive prediction of internal browning in pineapple using transmittance short wavelength near infrared spectroscopy
(2013-01-01)
Sukwanit, S.
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Teerachaichayut, S.
Pineapple [Ananas comosus (L.) Merr.] is one of the most important commercial fruit of Thailand. The taste and consistency of the fruit is of great importance, however "internal browning", a common physiological disorder affecting the fruit, which cannot be identified by visual inspection, makes the product unacceptable for export. In this study, Near Infrared (NIR) spectroscopy in the range of 665-955 nm was investigated as a non-destructive means to identify internal browning. Partial least squares-discriminant analysis (PLS-DA) was used in conjunction with the pre-treated NIR spectra as a first step in the development of an automated method of pineapple fruit sorting. A set of 243 samples was used for this research (131 commercially acceptable pineapples and 112 pineapples suffering from internal browning). A sample of 145 fruits was used for a training set and 98 samples for a test set. The smoothing and the first derivative pretreatment of averaged spectra were performed to obtain the best calibration model. The overall classification accuracy of the PLS-DA/NIR model on the prediction set was 90.8% (47 out of 53 for the sound pineapples and 42 out of 45 for the internally browned pineapples). This study demonstrates that NIR transmittance spectroscopy is potentially a useful nondestructive method that can be used to predict internal browning disorder in intact pineapples. © ISHS 2013.
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Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy
(2011-10-01)
Teerachaichayut, Sontisuk
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Terdwongworakul, Anupun
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Thanapase, Warunee
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Kiji, Kazuaki
A non-destructive technique to predict a hardening pericarp disorder in intact mangosteen is proposed by using near infrared (NIR) transmittance spectroscopy in the wavelength range of 660-960 nm. The study found that the spectral features of normal pericarp mangosteen and hardening pericarp mangosteen were different. The averaged spectra and individual spectra of hardening pericarp mangosteen from a calibration set (N = 560) were used to develop classification models, using partial least squares discriminant analysis (PLS-DA). A model based on individual spectra obtained better classification. The overall accuracy of classification for a prediction set (N = 358) was 91%. Out of 179 samples of normal pericarp fruits, 167 were identified correctly, while 159 samples out of 179 samples with hard pericarp were predicted correctly. The results showed that NIR transmittance spectroscopy can be used to predict hard pericarp disorder in intact mangosteen fruit accurately. © 2011 Elsevier Ltd. All rights reserved.

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