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Impact of foam-mat drying conditions of “Gấc” aril on drying rate and bioactive compounds: Optimization by novel statistical approaches
(2024-12-30)
Thuy, Nguyen Minh
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Tien, Vo Quoc
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
This study was conducted to optimize the foam-mat drying conditions to maximize quality [β-carotene and total polyphenol content (TPC)] and drying rate of “Gấc” aril powder by using two novel statistical techniques as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) couple with Genetic Algorithm (GA). During production process, level of egg albumin (EA) used for foaming process and drying temperature mainly influenced the drying rate and content of antioxidant compounds in powder. ANN model of 3–10–3 showed more accuracy and faster prediction capacity than RSM model did. ANN-GA model predicted the optimal conditions to be 13.31 % EA, 0.26 % xanthan gum and drying temperature of 73.1 °C, with the drying rate of 1.89 g-water/g-dry matter/min, β-carotene content of 395.88 μg/g, TPC of 1.68 mgGAE/g. These results confirmed the suitability and promising of foam-mat drying for “Gấc” aril powder production, to be producing food ingredient containing highly bioactive compounds.
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Study on chemical structure stability and properties of chitosan-incorporated tamarind seed kernel xyloglucan hydrogels
(2024-12-05)
Manamoongmongkol, Kanjana
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Sriprom, Pongsert
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Narkrugsa, Woatthichai
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Phumjan, Lamphung
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Permana, Lasuardi
This study investigated the stability of chitosan-incorporated tamarind seed kernel xyloglucan hydrogels prepared at different ratios after storage for three years. The physical hydrogel samples were studied using ratios of xyloglucan to chitosan between 1.5:1 and 4:1, comparing fresh hydrogel with hydrogel stored for three years. The hydrogels were analyzed for Fourier transform infrared characteristics, rheological behavior, flow rate, and zeta potential. After three years of storage, a reversible reaction was confirmed by C-H molecular stretching using Fourier transform infrared. All hydrogel samples exhibited pseudoplastic fluid characteristics with liquid-like behavior. The zeta potential of fresh hydrogel at 1.5:1 and 4:1 ratios was 27.700 ± 0.964 mV and 22.633 ± 0.929 mV, respectively, whereas after three years, it became 28.067 ± 1.106 mV and 18.867 ± 0.503 mV, respectively. The amount of xyloglucan significantly affected the zeta potential of the xyloglucan-chitosan hydrogel, leading to a decrease in the stability of the hydrogel at a 4:1 ratio. The stability of the hydrogel at a ratio of 1.5:1 was confirmed by pH and zeta potential measurements. In conclusion, the properties and behavior of the xyloglucan-chitosan hydrogel remained stable after three years of storage at a 1.5:1 ratio.
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Antifungal copper nanocomposite-rubber compound for tree wound dressings
(2024-12-15)
Noppradit, Benjaporn
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Uthaipan, Nattapon
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Klinnawee, Lompong
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Kongtragoul, Pornprapa
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Phengdaam, Apichat
This study combines copper nanoparticles (CuNPs) with a rubber compound to develop a tree wound dressing that reduces tree mortality from infections, utilizing broad-spectrum antifungal and adhesive properties of composite material. Phytotoxicities of CuNPs with an average size of 6 nm were significantly lower than those of Cu ions, evaluated by the germination index using lettuce seeds and membrane integrity index using stems of rubber seedling. After that, the synthesized CuNPs were then loaded into a natural rubber (NR) latex compound at concentrations ranging from 0 to 90 mg/L to produce a CuNP composite adhesive. The shear strength, representing adhesion on wood, was evaluated using lap shear testing, revealing a 50 % reduction in maximum force at the highest CuNP loading. This loss of shear strength was attributed to interference by copper ions in the vulcanization of the NR latex compound, as indicated by FT-IR spectroscopy. After that, the CuNP composite adhesive was formed into a film to test antifungal properties against Colletotrichum spp. Based on the in vitro plate inhibition zone assay, 10 mg/L of CuNPs inhibited the growth of Colletotrichum spp. The size of inhibited zone was significantly increased by 150 and 300 % when concentrations of CuNPs were 70 and 90 mg/L, respectively. This research provides the first evidence of the low phytotoxicity of CuNPs compared to copper ions at equivalent concentrations and demonstrates the efficacy of CuNP composites with NR compounds as tree wound dressings.
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Techno-economic performance analysis of biomass-to-methanol with solid oxide electrolyzer for sustainable bio-methanol production
(2024-12-30)
Detchusananard, Thanaphorn
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Wiranarongkorn, Kunlanan
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Im-orb, Karittha
The analysis of the technical and economic performance of an integrated biomass to methanol and solid oxide electrolysis process (BtM-SOEC) is studied to find more sustainable process of bio-methanol production. The oil palm empty fruit branch (EFB) which is abundant in Thailand is used as biomass feedstock. Modeling of the BtM-SOEC is done using Aspen Plus. For technical aspects, the production rate of oxygen and hydrogen from the SOEC can be enhanced through an appropriate adjustment of the number of cells and cell temperature. The BtM-SOEC offers higher methanol yield and overall efficiency, while consumes less energy than the conventional biomass to methanol process (BtM). The maximum methanol production rate of 0.4995 kmol hr−1 derived from BtM-SOEC is achieved at a number of cells of 325 cells and a cell temperature of 700 °C, at this condition the overall efficiency is 64.79 %. The economic assessment indicates that the conventional BtM and BtM-SOEC are still not economically feasible. However, the conventional BtM is more economically feasible than the BtM-SOEC. The methanol cost of BtM-SOEC can turn out to be economically feasible when renewable electricity cost and SOEC cost decrease substantially. The methanol cost of the BtM-SOEC (620 USD ton−1) can be competitive to that of the BtM (703 USD ton−1) when the cost of input renewable electricity decreases by 80 %. Consequently, this research highlights the potential of BtM-SOEC from agricultural residues for sustainable bio-methanol production in the future market condition that the cost of renewable electricity tends to continuously decrease with the technology development and increased technology adoption and the carbon policy tends to be tightened to relieve global warming.
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An k-Nearest Neighbors Machine Learning Algorithm for the PM2.5 Early Warning System in Bang Khun Tian, Bangkok, Thailand
(2024-12-02)
Thongtha, Kaboon
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Pochai, Nopparat
The problem of particulate matter with a diameter of less than 2.5-10 microns, such as PM2.5-PM10 in Bangkok, affects the health of people because there are small particles that can penetrate deep into the alveoli. If there is an early warning system to warn people about the harmful levels of PM2.5 in Bangkok, such as an early warning of 2-3 days, it can help the people have time to prevent themselves. In this research, an early warning system to warn people about the harmful levels of PM2.5 in Bangkok is proposed. The air quality data of the Bang Khun Tian station, Bangkok, for 2 months, from December 1, 2020, to January 31, 2021, were selected because the area is an air-quality-worrying area. A proposed early warning system for the harmful levels of PM2.5 around Bang Khun Tian, Bangkok, was developed using the k-nearest neighbors machine learning algorithm. As the results show, the proposed technique gives an agreeable prediction for the earliest warning by 4 days.
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Two new species of Sigambra (Annelida, Pilargidae) from the Andaman coast and the Gulf of Thailand
(2024-12-17)
Plathong, Jintana
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Plathong, Sakanan
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Klangnurak, Wanlada
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Dean, Harlan K.
Two species of Pilargidae, Sigambra pakbaraensis sp. nov. and S. sirilukae sp. nov. are described from different coasts of Southern Thailand (Andaman Sea and the Gulf of Thailand). Both species belong to the subgroup of Sigambra which lack ventral cirri at chaetiger 2 and have capillary notochaetae. Sigambra pakbaraensis sp. nov. is described from the Andaman coast and can be distinguished from other species in the genus by the occurrence of the first dorsal hook at chaetiger 8, the presence of capillaries in notopodia, and 14 pharyngeal papillae. Sigambra sirilukae sp. nov. is described from the Gulf of Thailand and it is clearly distinguished from all other species in the genus by having 12 pharynx papillae, an elongate median antenna with a large ceratophore, dorsal hooks from middle chaetigers, and up to five capillary notochaetae from anterior most chaetigers (4–5). Molecular phylogenetic analyses, based on cytochrome oxidase subunit I (COI) and mitochondrial genes 16S, indicate S. pakbaraensis sp. nov. clusters within Sigambra Müller, 1858. Pores on the dorsal and ventral cirri of both species are herein described for the first time for the genus. An updated key to species of Sigambra from the Indo-Pacific region is also provided.
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Improving Water Salinity Forecasting in Bang Pakong River with Attention Mechanism
(2024-12-02)
Saksopit, Thongthai
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Khawne, Amnach
Seawater intrusion in the Bang Pakong River estuary poses a significant threat to freshwater resources used for agriculture, municipal consumption, and industrial applications. Accurate prediction of salinity fluctuations is crucial for effective water management strategies. This study proposes an enhanced univariate salinity prediction method utilizing a Long Short-Term Memory (LSTM) model augmented with an Attention Mechanism. The Attention Mechanism empowers the LSTM to selectively focus on crucial information within extended historical salinity data sequences. The optimal input sequence length for the model is determined through a training process, aiming for the most accurate predictions. Here, the model forecasts salinity values 24 hours ahead and is evaluated against actual measurements. Performance metrics demonstrate that the Attention-LSTM model achieves the lowest error (MAE: 0.007834, MSE: 0.000094, RMSE: 0.009697, MAPE: 0.048736) and the highest accuracy (R2: 0.782927) at an input sequence length of 504 hours. These findings highlight the potential of the Attention-LSTM model for improved salinity prediction in the Bang Pakong River estuary, aiding water resource management strategies.
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Monitoring Pesticide Residue on Bok Choi using Convolution Neural Network with NIR spectral Data
(2024-12-29)
Lapcharoensuk, Ravipat
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Fhaykamta, Chawisa
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Anurak, Watcharaporn
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Chadwut, Wasita
Deep learning (DL) has been applied in agriculture, especially quality control in agricultural processing. One key area of interest is the detection and monitoring of pesticide residues in crops. The most popular measurement tool for nondestructive monitoring of pesticide residues is near-infrared spectroscopy (NIRS). A combination of CNN model with NIR spectral data was developed for monitoring pesticide residue on bok choi. The NIR spectral of bok choi with and without pesticide residue (chlorpyrifos) was collected in wavelength range between 908 and 1676 nm. A simple structure of CNN was modified for a one-dimensional task and this deep learning architecture was trained for classification of the bok choi samples. The results showed prefect prediction with 100% accuracy, precision, recall and specificity. This study also found that deep learning for NIR spectroscopy data requires less processing than traditional machine learning while still achieving great results.
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Continuous tremor monitoring in Parkinson's disease: A wristwatch-inspired triboelectric sensor approach
(2024-12-20)
Ukasi, Sirinya
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Pongampai, Satana
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Panigrahi, Basanta Kumar
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Panda, Swati
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Hajra, Sugato
Parkinson's disease (PD) prevalence is projected to reach 12 million by 2040. Wearable sensors offer a promising approach for comfortable, continuous tremor monitoring to optimize treatment strategies. Here, we present a wristwatch-like triboelectric sensor (WW-TES) inspired by automatic watches for unobtrusive PD tremor assessment. The WW-TES utilizes a free-standing design with a surface-modified polytetrafluoroethylene (PTFE) film and a stainless-steel rotor within a biocompatible polylactic acid (PLA) package. Electrode distance is optimized to maximize the output signal. We propose and discuss the WW-TES working mechanism. The final design is validated for activities of daily living (ADLs), with varying signal amplitudes corresponding to tremor severity levels (“normal” to “severe”) based on MDS-UPDRS tremor frequency. Wavelet packet transform (WPT) is employed for signal analysis during ADLs. The WW-TES demonstrates the potential for continuous tremor monitoring, offering an accurate screening of severity and comfortable, unobtrusive wearability.
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Bimetallic copper- and nickel-rich Cu-Ni phyllosilicate catalysts for the liquid phase selective hydrogenation of furfural to furfuryl alcohol
(2024-12-02)
Shoosri, Tanyarat
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Chotiwilaiwan, Pisacha
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Rattanapornchaiwat, Tanisorn
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Teerawatananond, Thapong
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Miyake, Takanori
Bimetallic Cu-Ni phyllosilicates (Cu-NiPS) with various Ni/Cu ratios (5 : 15, 10 : 10, 15 : 5 wt%) were synthesized using ammonia evaporation hydrothermal method to obtain copper-rich or nickel-rich Cu-Ni alloys. These catalysts were evaluated for the selective hydrogenation of furfural (FF) to furfuryl alcohol (FA) in the liquid phase under relatively mild conditions (100 °C, 20 bar H<inf>2</inf>). The bimetallic Cu-NiPS catalysts exhibited excellent dispersion of the Ni-Cu alloy with average particle sizes ranging from 3.8 to 4.8 nm and demonstrated significantly enhanced catalytic performance over the monometallic nickel phyllosilicate (20% NiPS). The copper-rich Ni-Cu alloy (15% Cu-5% NiPS) exhibited the highest FF conversion efficiency (88%) and FA selectivity (90%). This superior performance is attributed to its smallest particle size, higher proportion of Cu0 and the synergistic interactions between Ni and Cu. This synergy effectively stabilizes the carbonyl group while promoting efficient H<inf>2</inf> dissociation and FA desorption, thereby facilitating the hydrogenation of FF to FA. Furthermore, the catalyst exhibited excellent recyclability and maintained high conversion efficiency and selectivity over multiple cycles.

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