KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 7 of 7

Some of the metrics are blocked by your
consent settings
Estimation of the Weight and Volume of Lime (Citrus aurantifolia (Christm.) Swingle) Fruit Using Computer Vision Based on Traditional Machine Learning and Deep Learning
(2024-10-01)
Onmankhong, Jiraporn
;
Poonpakdee, Pasu
;
Lapcharoensuk, Ravipat
The post-harvest process is important to increasing the market value of limes and requires focus. During this process, limes are graded and categorized based on size, weight, and volume. Therefore, identifying efficient means of estimating these properties is very important and remains an open research area. This study applies the concept of computer vision based on traditional machine learning algorithms (partial least square regression (PLS), epsilon-support vector regression (ε-SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), Bagging meta-estimator (BME), and extremely randomized trees (ERTs)) and pre-trained deep learning (InceptionV3, MoblieNetV2, ResNet50, and VGG-16) for estimating the weight and volume of limes. Our findings showed that the BME and ResNet50 could yield the highest performance for estimating the weight and volume of limes. The BME produced (Formula presented.) values of 0.954 and 0.882 for weight and volume, respectively, while the (Formula presented.) values of ResNet50 models were between 0.951 and 0.957 for weight and volume, respectively. This study concluded that computer vision based on both traditional machine learning and deep learning could be used to estimate the weight and volume of limes. The approach proposed in this study can be adopted for applications related to computer vision in the post-harvest process.
Some of the metrics are blocked by your
consent settings
Enhancing small-scale acetification processes using adsorbed Acetobacter pasteurianus UMCC 2951 on κ-carrageenan-coated luffa sponge
(2024-01-01)
Sriphochanart, Wiramsri
;
Krusong, Warawut
;
Samuela, Nialmas
;
Somboon, Pichayada
;
Sirisomboon, Panmanas
Background. This study explored the utilization of luffa sponge (LS) in enhancing acetification processes. LS is known for having high porosity and specific surface area, and can provide a novel means of supporting the growth of acetic acid bacteria (AAB) to improve biomass yield and acetification rate, and thereby promote more efficient and sustainable vinegar production. Moreover, the promising potential of LS and luffa sponge coated with κ-carrageenan (LSK) means they may represent effective alternatives for the co-production of industrially valuable bioproducts, for example bacterial cellulose (BC) and acetic acid. Methods. LS and LSK were employed as adsorbents for Acetobacter pasteurianus UMCC 2951 in a submerged semi-continuous acetification process. Experiments were conducted under reciprocal shaking at 1 Hz and a temperature of 32 ◦C. The performance of the two systems (LS-AAB and LSK-AAB respectively) was evaluated based on cell dry weight (CDW), acetification rate, and BC biofilm formation. Results. The use of LS significantly increased the biomass yield during acetification, achieving a CDW of 3.34 mg/L versus the 0.91 mg/L obtained with planktonic cells. Coating LS with κ-carrageenan further enhanced yield, with a CDW of 4.45 mg/L. Acetification rates were also higher in the LSK-AAB system, reaching 3.33 ± 0.05 g/L d as opposed to 2.45 ± 0.05 g/L d for LS-AAB and 1.13 ± 0.05 g/L d for planktonic cells. Additionally, BC biofilm formation during the second operational cycle was more pronounced in the LSK-AAB system (37.0 ± 3.0 mg/L, as opposed to 25.0 ± 2.0 mg/L in LS-AAB). Conclusions. This study demonstrates that LS significantly improves the efficiency of the acetification process, particularly when enhanced with κ-carrageenan. The increased biomass yield, accelerated acetification, and enhanced BC biofilm formation highlight the potential of the LS-AAB system, and especially the LSK-AAB variant, in sustainable and effective vinegar production. These systems offer a promising approach for small-scale, semi-continuous acetification processes that aligns with eco-friendly practices and caters to specialized market needs. Finally, this innovative method facilitates the dual production of acetic acid and bacterial cellulose, with potential applications in biotechnological fields.
Some of the metrics are blocked by your
consent settings
Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
(2023-10-01)
Posom, Jetsada
;
Duangpila, Chutatip
;
Saengprachatanarug, Khwantri
;
Wongpichet, Seree
;
Onmankhong, Jiraporn
Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels.
Some of the metrics are blocked by your
consent settings
Application of baby corn husk as a biological sustainable feedstock for the production of cellulase and xylanase by Lentinus squarrosulus Mont.
(2023-02-01)
Vichitraka, Asanee
;
Somboon, Pichayada
;
Tantratian, Sumate
;
Onmankhong, Jiraporn
;
Sirisomboon, Panmanas
In an effort to use baby corn husk (BCH) as a sustainable feedstock for cellulase and xylanase production by the Lentinus squarrosulus Mont. isolate LS-YA (LSM-LS-YA), a suitable pretreatment method and fermentation strategies were developed. BCH pretreated with 1 M sodium hydroxide for 90 min, an alkaline pretreatment, exemplified an appropriate pretreatment method. In a 10-L external Venturi injector bioreactor, the highest cellulase and xylanase production was 4.12 ± 0.36 unit/mL and 6.15 ± 0.36 unit/mL, respectively, when 1 g/L diammonium hydrogen phosphate was used as the nitrogen source and the aeration rate was controlled at 0.2 vvm. This study provides an informative perspective on the production of cellulase and xylanase from agricultural lignocellulosic materials, which could reduce agricultural waste while supporting a zero-waste circular economy, and this fermentation process would be applicable to larger-scale production.
Some of the metrics are blocked by your
consent settings
Cognitive spectroscopy for the classification of rice varieties: A comparison of machine learning and deep learning approaches in analysing long-wave near-infrared hyperspectral images of brown and milled samples
(2022-06-01)
Onmankhong, Jiraporn
;
Ma, Te
;
Inagaki, Tetsuya
;
Sirisomboon, Panmanas
;
Tsuchikawa, Satoru
Rapid and non-destructive detection of genuine Thai Jasmine rice (Khao Dawk Mali 105 (KDML105)) from Pathum Thani1 (PTT1) and Phitsanulok2 (PSL2) under either milled or brown conditions is required to disrupt fraudulent. This study aimed to resolve this real issue using long-wave near infrared hyperspectral imaging (NIR-HSI) coupled with machine learning and deep learning approaches. The best classification accuracy for the milled rice was achieved using the spectral imaging-based analysis on the NIR-HSI data with selected wavelength, approximately 95% for the test set either by convolutional neural network or support vector machine (SVM), whereas for the brown rice, the SVM model based on the averaged NIR spectra could achieve the best classification accuracy of 95.4%. It suggests the chemical component difference and its spatial distribution in the milled rice could contribute higher classification accuracy. Additionally, the surface bran effects of brown rice could be reduced by using averaged spectral data coupled with the SVM method.
Some of the metrics are blocked by your
consent settings
The influence of processing parameters of parboiled rice on its physiochemical and texture properties
(2021-04-01)
Onmankhong, Jiraporn
;
Jongyingcharoen, Jiraporn Sripinyowanich
;
Sirisomboon, Panmanas
The impact of different parboiled rice process conditions on physical (whiteness and yellowness), chemical (amylose and fat contents), and texture (hardness and toughness) properties was studied. The parboiled rice was produced from the Suphanburi 1 variety. The correlation between chemical and texture properties was also analyzed. To study the effect of the soaking process, the time (2, 3, and 6 hr) and temperature (65 and 75°C) of soaking were altered, while the steaming condition was fixed at 100°C for 20 min. To study the effect of the steaming process, the soaking condition was fixed at 65°C for 6 hr while steaming condition was altered, including time (10 and 20 min) and temperature (90 and 100°C). The results show that the different conditions influenced the physical and chemical properties of parboiled rice. The amylose content was negatively correlated (Hardness, r = −0.52) (Toughness, r = −0.38) and fat content was positive low correlated (Hardness, r = 0.20) (Toughness, r = 0.12) with textural properties. Due to the specification of parboiled rice for exportation varying according to customer requirements, the results of this research provided some useful information for parboiled rice factories.
Some of the metrics are blocked by your
consent settings
Texture evaluation of cooked parboiled rice using nondestructive milled whole grain near infrared spectroscopy
(2021-01-01)
Onmankhong, Jiraporn
;
Sirisomboon, Panmanas
One consumer acceptability criterion of cooked parboiled rice is its texture, particularly hardness and toughness. The samples were obtained from parboiled rice factory for export. The hardness and toughness calibration models based on milled whole grain near infrared spectroscopy was developed. The ISO 11747 Rice-Determination of Rice Kernel Resistance to Extrusion after Cooking method was used as reference test. The models were established using partial least squares regression (PLSR), principal component regression (PCR) and support vector machine regression (SVM). The PLSR optimal calibration model of hardness with moving average smoothing pre-processing gave coefficient of determination of validation (r2), root mean square error of prediction (RMSEP) and ratio of prediction to deviation (RPD) of 0.70, 7.24 N and 1.93, respectively. The PCR optimal model of toughness using mean normalization preprocessing provided r2, RMSEP and RPD of 0.66, 38.00 Nmm and 1.75, respectively. According to RPD threshold, the models were fair for prediction application. This feasibility study suggested that the NIR protocol developed was applicable for real use due to the error of the NIR scanning and other unexplained errors was only 5% and 1% for hardness and toughness models, respectively. However, the sample preparation before texture analysis has to be improved.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results