Most rice is consumed as cooked, milled rice, but a small portion is also ground into flour or separated into a starch fraction and used by the food industry as a gluten-free ingredient. This study aims to find out if different types of rice flour and starch, such as white and colored rice, could be used in industry. This study employs green modification techniques to slow down the digestion process by combining polyphenols with starch. Our initial study found that the raw colored rice has a lower glycemic index than other types of rice, such as brown or white rice. Another study that looked at how the quality of colored rice flour was changed by different methods also discovered that out of the six green methods (annealing, heat moisture treatment, ultrasound, pregelatinization, wet-microwave, and dry-microwave). It found that ultrasound improved the polyphenol bioaccessibility in the rice flour and reduced the digestion rate. The pregelatinization process led to the flour having high solubility and an estimated glycemic index. Different techniques affected the flour/starch quality in different ways. Therefore, for further industrial application, it could also be easier to select the method for food product based on their required techno-functional quality of flour/starch. In addition to the modification techniques, this study showed that the high bioaccessible polyphenol content and high polyphenol content in rice greatly slowed down the rate of digestion. This study also open for further exploring the possibility of using high polyphenol agricultural waste to modify starch and flour in a sustainable manner.
Rice (Oryza sativa L.) is a crucial staple crop that supplies nutritional sustenance for half of the global population (Shao et al., 2018). Moreover, rice constitutes a significant commercial crop in Thailand, with its grains serving as a staple food, and a diverse array of rice types is present throughout the nation (Suebpongsang et al., 2020). Multiple rice types have been developed in Thailand, including colored/pigmented rice and non-pigmented rice, commonly referred to as brown and white rice, which can be utilized in many industries (Yamuangmorn & Prom, 2021). Furthermore, rice is regarded as a naturally gluten-free ingredient. Rice flour or starch can be utilized to create a variety of products, including cookies, bread, noodles, and crackers. Due to the elevated carbohydrate content in rice, most rice products are regarded as having a high glycemic index (GI). The link between polyphenols and digestive behavior has become a topic of attention, particularly for rice and its products. Researchers predominantly conducted comparisons of the features of pigmented rice and non-pigmented rice (with/without eGI) across various areas or types (Tangsrianugul et al., 2019; Verma & Srivastav, 2020; Waewkum & Singthong, 2021). Nevertheless, there remains a deficiency in information regarding the association between endogenous antioxidant qualities and other starch characteristics, which might be further considered for predicting the nutritional value of rice and for enhancing the foundational knowledge necessary for the development of rice products. The versatility of rice flour in industrial applications is primarily influenced by its physicochemical properties and usefulness. Unprocessed rice has limited utility and applicability (Iqbal et al., 2023). Consequently, novel techniques are necessary to enhance the quality of rice flours for further processing. Therefore, this research may offer essential insights for further investigation into select rice varieties and modification techniques for specific industrial applications by utilizing the starch-polyphenols complex concept.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
Durian is an important economic crop in Thailand that is affected by foliar diseases such as rust, leaf blight, and leaf spot. These diseases reduce the quality of the yield and increase management costs. This research focuses on developing AI software for screening durian leaf diseases by applying deep learning technology to classify different types of leaf lesions.
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This study aims to identify the toothbrush appearance factors that affect baby boomers purchasing decisions. The research divide into three stages: The first stage is to classify the toothbrush appearance factors through a review of literature, research, and examining toothbrushes currently available on the market, summarizing them as appearance factors. The second stage is to summarize the results of the toothbrush appearance factors to create a multiple-choice questionnaire in three dimensions: purchasing decisions, aesthetics, and functionality. Collecting data from a group of 30 Baby Boomers aged 57-75 years old. The last stage is to summarize the three dimensions of appearance factors affecting baby boomers' toothbrush purchasing decisions and report as percentages and rank them. The research findings indicate that the most significant toothbrush appearance factor is a "Curved handle," accounting for 80%, followed by “Multi-level bristles” at 70%, a "Rubber thumb rest" at 53.3%, "Handle divided into more than two parts" at 50%, and “Offset shape” at 40%, respectively. In terms of the reason for purchasing decision based on various factors are as follows: the curved handle and offset shape give a sense of purchase with its aesthetic, While the selection of multi-level bristles, the Rubber thumb rest, and the handle divided into more than two parts due to functionality.