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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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The improvement of germination method for producing the germinated brown rice using a water spraying system with a revolved sieve
(2024-11-01)
Chungcharoen, Thatchapol
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Sansiribhan, Sansanee
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Munsin, Ronnachart
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Phetpan, Kittisak
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Fonghiransiri, Surasak
Water soaking is an important method in germinated brown rice (GBR) production that causes fermentation, leading to an unpleasant smell of GBR. In this research, a water spraying system with a revolved sieve is applied to produce the GBR. The increased speed and time of spray break led to higher moisture content and water absorption. The spray break of 30 min and revolved speed of 15 rpm provided the shortest time to obtain the paddy with a moisture content of 30% (w.b.). The incubation pattern with a revolved sieve and water spray provided the shortest incubation time for 90% germination. When producing the GBR with a water spraying system with a revolved sieve (GBR-WSSRS), it had a lower number of microorganisms compared to the GBR with a water soaking (GBR-WS), leading to higher scores of overall acceptability. However, the GBR-WSSRS had a lower GABA content than the GBR-WS.
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Investigation of Dielectric Response in a 22/0.4 kV Distribution Transformer: Effects of Moisture Content
(2024-01-01)
Daengdee, Danaiwat
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Bunlaksananusorn, Chanin
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Suksawat, Dusit
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Chumpiboon, Komin
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Jeenmuang, Siwakorn
Transformers play a crucial role in power systems, ensuring efficient energy transmission and distribution. This research focuses on a 22/0.4 kV transformer decommissioned after over 20 years of service. The study involves draining the transformer oil completely and refilling it with oil characterized by known moisture content and breakdown voltage levels until reaching normal operating conditions. The objective is to study the effects of increased moisture content on the dielectric response within the actual structure of the transformer. Diagnostic measurement techniques, including Frequency Domain Spectroscopy (FDS) and Polarization Depolarization Current (PDC), are utilized to evaluate the insulation condition of the transformer. FDS provides information on the dielectric response over a wide frequency range, while PDC assesses the polarization and depolarization behavior of the insulation material. These techniques offer valuable information on the ageing and moisture content of the insulation. The experimental results will be presented and discussed in this paper.
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Drying edible jellyfish (Lobonema smithii) using a parabolic greenhouse solar dryer
(2022-12-01)
Maisont, S.
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Samutsri, W.
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Sansomboon, A.
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Limsuwan, P.
Thailand is one of Southeast Asia’s top exporters of salted edible jellyfish. Jellyfish products are generally exported in the form of semi-dried jellyfish. A recent survey on jellyfish distribution in coastal areas in the Gulf of Thailand and the Andaman Sea indicated that most edible jellyfish in the area belong to Lobonema smithii and Rhopilema hispidum. Drying is an alternative method to preserve food products. In this work, parabolic roof shape greenhouse solar drying of edible jellyfish (Lobonema smithii) was studied, and the quality of the dried jellyfish was assessed. The solar dryer system has a 300 kg loading capacity for jellyfish and the resulting dried and rehydrated jellyfish products are described. The moisture content of dried jellyfish decreased to 7.05-11.70% (wb) from 91-92% (wb) after drying for three days and the protein, fat, crude fibre, ash, and carbohydrate content of the dried jellyfish were 68.68%, 0.74%, 0.43%, 9.27%, and 4.67%, respectively. The moisture of the rehydrated jellyfish increased to 37.67-39.07% (wb) after soaking in distilled water for 5 hrs. In terms of colour, the rehydrated jellyfish products were found to be highly similar to salted jellyfish products.
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Experimental investigation into the performance of cutting betel nut machine via response surface methodology and desirability function
(2022-01-01)
Bulan, Ramayanty
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Siregar, Kiman
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Wardhana, Muhammad Yuzan
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Lubis, Hamzah Hambali
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Thamren, Dewi Sartika
Cutting betel nut machines are increasingly being designed by engineers using local material. However, the performance of the cutting betel nut machine is influenced by the moisture content of the betel nut and the rotational speed of the machine. In this study, the performance of cutting a betel nut machine under moisture content of betel nut and rotational speed of the machine was studied using response surface methodology (RSM) and desirability function. Central Composite Design (CCD) coupled with RSM and desirability function was employed to evaluate the impact of moisture content of betel nut (34.68–50.54%, w.b.) and rotational speed (600–1000 rpm) on machine capacity (kg/hr), efficiency (%), and losses (%) responses. The desirability function was then used to optimize moisture content and rotational speed yielding maximum machine capacity and efficiency at lower losses. Three verification experiments were run to ensure the empirical relationships were valid. Optimum requirements of process parameters have been seen at which moisture content of 50.54% (w.b.) and rotational speed of 1000 rpm was achieved in maximum machine capacity of 44.16 kg/hr at higher efficiency (92.72%) and lower losses (6.31%). The model's conclusions were very consistent with the confirmed values. The results proved that an appropriate performance of the machine can be achieved using moisture content of betel nut and rotational speed of machine cutting betel nut.
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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
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Posom, Jetsada
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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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In-line near infrared spectroscopy for the prediction of moisture content in the tapioca starch drying process
(2019-03-01)
Phetpan, Kittisak
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Udompetaikul, Vasu
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Sirisomboon, Panmanas
Moisture content is an important parameter measured in tapioca starch production as this parameter has been shown to correlate strongly with the quality of the finished product. However, there is currently no in-line sensor which can be used to directly measure the moisture content of the product in real time. The objective of the present work was to study the use of an in-line measurement which can be introduced at the end of the drying process for tapioca starch moisture content evaluation. Either in-line NIR data or at-line NIR data was used to develop the necessary calibration models for evaluating the moisture content. Furthermore, calibration models were also developed by pooling the in-line and at-line data. Its performance was then verified using additional in-line data. The NIR model developed using 100% of the at-line data and 50% of the in-line data was validated using the unused 50% of the inline data. This model was shown to provide better performance in moisture content prediction with an SEP of 0.61% and a bias of 0.001%. In addition, the results showed that the at-line spectrum can also be used for the calibration model development to predict the moisture content of the samples scanned by an in-line spectrometer. However, the in-line spectrometer installation on a pneumatic conveying circular tube where tapioca starch and air mixed was found to be complicated due to significant vibration. This caused additional variation in the data with time. Therefore, it is concluded that the most suitable place for installing a spectrometer would be at a position involving a low pressure, or where the stream flow of a product is steadier in order to avoid the dynamic mixing of the product within the drying tube affecting the uncertainty of NIR scattering during the measurement.
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Simple effective and robust weight sensor for measuring moisture content in food drying process
(2019-01-01)
Pongsuttiyakorn, Thadchapong
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Sooraksa, Pitikhate
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Pornchalermpong, Pimpen
Measurement values using weight sensors are inherently contaminated by noise generated from electrical sources, thermal dynamics, mechanical vibration, and environmental conditions. In this paper, we present a method of correcting the accuracy of the sensors by using filtering algorithms. Two well-known methods in electrical engineering, namely, low-pass filtering and the Kalman algorithm, are applied to correct the real-time measured weight under various temperatures to determine moisture content during the food drying process. From an experiment using pineapples as the food material, the results showed the effectiveness of the application even if it has a simple design and is easy to implement. For small and medium enterprises (SMEs), the method and demonstration shown in this paper can be adopted and the proposed system is scalable for designing weight sensor systems operating in a thermal drying cabinet.
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Non-destructive prediction of moisture content of lime (Citrus aurantifolia Swingle 'Paan') by multiple regression analysis of its electrical and physical properties
(2017-03-21)
Huong, H. T.
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Teerachaichayut, S.
Large quantity of juice is an important index of lime quality that consumers seek for. Therefore, a non-destructive technique for prediction of lime juice quantity is needed. In this study, moisture content (MC) of lime which is an indicator of its juice quantity was predicted by multiple regression analysis of its electrical properties -capacitance (C), inductance (L) and impedance (Z) at various frequencies (0.012, 0.05, 0.1, 0.2, 5, 10, 20, 50, 100 and 200 kHz) - and physical parameters - weight and geometric mean diameter (GMD). Samples (n=82) were divided into a calibration set (n=55) and a prediction set (n=27). A calibration model for moisture content of lime was established and cross-validated by partial least squares regression (PLSR). Prediction results achieved a coefficient of determination (R2) of 0.934 and a root mean square error of prediction (RMSEP) of 1.822% wet basic, demonstrating that this technique has a real potential for development into a practical non-destructive lime screening method.
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Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy
(2016-07-15)
Posom, Jetsada
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Shrestha, Amrit
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Saechua, Wanphut
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Sirisomboon, Panmanas
The MC (moisture content) and HHV (higher heating value) of Leucaena leucocephala pellets using NIR (near infrared) spectroscopy was investigated in this study. The MC of the pellets was adjusted by subjecting the samples to different relative humidity environments. The samples were scanned in diffuse reflection mode at wavenumbers of 12,500-4000 cm-1. Partial least squares regression models correlating the MC and HHV with the NIR spectra were developed and validated by full cross validation. The model for MC and HHV provided coefficients of determination (R2) of 0.995 and 0.964, a root mean square error of cross validation (RMSECV) of 0.187%wb and 79.2 J g-1, bias of -0.0008%wb and 1.29 J g-1 and a RPD (ratio of prediction to deviation) of 13.9 and 5.30, respectively. The models had excellent accuracy. This rapid quality evaluation method may be used for trading of biomass pellets. An equation related MC and HHV was also developed.

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