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Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality
(2026-07-01)
Sahachairungrueng, Woranitta
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Aozora, Wayan Dipasasri
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Tantinantrakun, Achiraya
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Suwapanich, Rachit
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Workhwa, Saranya
The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI-based chemometric and machine-learning modeling was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of first derivative and SNV spectral pretreatment was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the first derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (R<inf>p</inf>) of 0.959, 0.961 and 0.957 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369% and 7.850, and a ratio of performance to deviation (RPD) of 3.29, 3.52 and 3.34 for the TSS, TA, and TSS/TA ratio evaluations, respectively. Partial least squares–discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard (≤4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.
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Modelling the Effect of Types and Concentrations of Sugar on Hardness and Overall Acceptability of Freeze-Dried “Sáp” Coconut Meat: An Artificial Intelligent Approach
(2025-01-01)
Loan, Le Thi Kim
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Chi, Huynh Thi Kim
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Mansamut, Chaiyut
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Van Tai, Ngo
Coconut is one of the common plants in Southeast Asia countries, which could further develop into various products. This study is aimed to study the effect of soaking conditions (types and concentrations of sugar) on the hardness and acceptance score of freeze-dried meat of “Sáp” coconut variety in Vietnam. Artificial neural network (ANN) model was used for predicting and modelling. A total of 540 datasets with two input variables and two output responses were divided into 70% of the training dataset, 15% of the validation dataset, and 15% of the testing dataset. ANN model was successfully forecasted the impact of soaking conditions on physical hardness and eating quality of product. The high agreement between actual and ANN’s predicted values was found with high coefficient determination (R<sup>2</sup> > 0.9). Moreover, the correlation between the hardness and acceptability of product also provided. This information could be supported for further study to optimize the process of producing high-quality and sensory freeze-dried coconut meat.
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Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging
(2024-04-01)
Workhwa, Saranya
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Khanthong, Thitirat
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Manmak, Napatsorn
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Thompson, Anthony Keith
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Teerachaichayut, Sontisuk
Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (R<inf>p</inf>) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images.
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PREDICTION OF AIR POLLUTION FROM POWER GENERATION USING MACHINE LEARNING
(2024-01-31)
Photsathian, Thongchai
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Suttikul, Thitiporn
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Tangsrirat, Worapong
Electrical energy is now widely recognized as an essential part of life for humans, as it powers many daily amenities and devices that people cannot function without. Examples of these include traffic signals, medical equipment in hospitals, electrical appliances used in homes and offices, and public transportation. The process that generates electricity can pollute the air. Even though natural gas used in power plants is derived from fossil fuels, it can nevertheless produce air pollutants involving particulate matter (PM), nitrogen oxides (NO<inf>x</inf>), and carbon monoxide (CO), which affect human health and cause environmental problems. Numerous researchers have devoted significant efforts to developing methods that not only facilitate the monitoring of current air quality but also possess the capability to predict the impacts of this increasing rise. The primary cause of air pollution issues associated with electricity generation is the combustion of fossil fuels. The objective of this study was to create three multiple linear regression models using artificial intelligence (AI) technology and data collected from sensors positioned around the energy generator. The objective was to precisely predict the amount of air pollution that electricity generation would produce. The highly accurate forecasted data proved valuable in determining operational parameters that resulted in minimal air pollution emissions. The predicted values were accurate with the mean squared error (MSE) of 0.008, the mean absolute error (MAE) of 0.071, and the mean absolute percentage error (MAPE) of 0.006 for the turbine energy yield (TEY). For the CO, the MSE was 2.029, the MAE was 0.791, and the MAPE was 0.934. For the NO<inf>x</inf>, the MSE was 69.479, the MAE was 6.148, and the MAPE was 0.096. The results demonstrate that the models developed have a high level of accuracy in identifying operational conditions that result in minimal air pollution emissions, with the exception of NO<inf>x</inf>. The accuracy of the NO<inf>x</inf> model is relatively lower, but it may still be used to estimate the pattern of NO<inf>x</inf> emissions.
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Artificial intelligence assistance in deciding management strategies for polytrauma and trauma patients
(2024-01-01)
Angthong, Chayanin
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Rungrattanawilai, Naruebade
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Pundee, Chaiyapruk
Introduction: Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However, little is known about the application of AI in treatment decisions for patients with polytrauma. In this systematic review, we investigated the benefits and performance of AI in predicting the management of patients with polytrauma and trauma. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were extracted from the PubMed and Google Scholar databases from their inception until November 2022, using the search terms “Artificial intelligence” AND “polytrauma” AND “decision”. Seventeen articles were identified and screened for eligibility. Animal studies, review articles, systematic reviews, meta-analyses, and studies that did not involve polytrauma or severe trauma management decisions were excluded. Eight studies were eligible for final review. Results: Eight studies focusing on patients with trauma, including two on military trauma, were included. The AI applications were mainly implemented for predictions and/or decisions on shock, bleeding, and blood transfusion. Few studies predicted death/survival. The identification of trauma patients using AI was proposed in a previous study. The overall performance of AI was good (six studies), excellent (one study), and acceptable (one study). Discussion: AI demonstrated satisfactory performance in decision-making and management prediction in patients with polytrauma/severe trauma, especially in situations of shock/bleeding. Importance: The present study serves as a basis for further research to develop practical AI applications for the management of patients with trauma.
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Improved prediction of pile bending moment and deflection due to adjacent braced excavation
(2023-11-01)
Phutthananon, Chana
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Jongpradist, Pornkasem
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Sirirak, Duangkamol
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Lueprasert, Prateep
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Jamsawang, Pitthaya
Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.
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Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging
(2023-07-01)
Tantinantrakun, Achiraya
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Thompson, Anthony Keith
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Terdwongworakul, Anupun
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Teerachaichayut, Sontisuk
Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (R<inf>p</inf>) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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Nondestructive quality assessment of longans using near infrared hyperspectral imaging
(2022-03-30)
Sahachairungrueng, Woranitta
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Teerachaichayut, Sontisuk
Near infrared hyperspectral imaging (NIR-HSI) is a method that can be used to evaluate the quality of fruit nondestructively. The objective of this research was to study the feasibility of NIR-HSI reflectance mode, within the wavelength of 935-1720 nm, for predicting the quality of longans. The two important factors chosen were: total soluble solids (TSS) and moisture content (MC). Each longan was assessed by first measuring its spectral data then measuring its TSS and MC to establish calibration models using multiple linear regression (MLR) compared with partial least squares regression (PLSR). Original spectra of longans gave the optimum results by PLSR for developing the models with correlation coefficients (Rp) of 0.76 for TSS and 0.88 for MC as well as root mean square error of predictions (RMSEP) of 0.42% and 0.45% respectively. By image processing, the predictive images from the models for TSS and MC were created based on color scales. They showed different colors of longans related to the level of TSS and MC and the deviation in levels in different parts of each longan by the predictive image. The results showed it could be used for grading fruit giving NIR-HSI potential to be developed in on-line systems.

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