Spent coffee grounds (SCGs) are waste from coffee drink process, which are rich of a varieties of nutrients. This research applied SCGs as ingredient in cracker. The optimized formula and process are studied as well as addition of different levels of SCGs were studied. It was found that addition of SCGs in cracker had hedonic score in high level from panels, especially panels who drink coffee. Moreover, it was observed that SCGs could increase nutrients especially carbohydrate and fiber to the product.
ผลิตภัณฑ์เบเกอรี เป็นผลิตภัณฑ์ที่ผู้บริโภคนิยมชื่นชอบ และสะดวกต่อการบริโภคทั้งในเวลาเร่งด่วนและเวลาว่าง โดยเฉพาะแครกเกอร์ซึ่งเป็นหนึ่งในผลิตภัณฑ์ที่สะดวกพกพา และสามารถนำมาบริโภคได้ในทุกช่วงเวลา การเสริมคุณค่าทางโภชนาการในแครกเกอร์ จึงมีส่วนช่วยให้ผู้บริิโภคได้รับประโยชน์มากขึ้น นอกจากนี้ แครกเกอร์มีองค์ประกอบของส่วนผสมที่อาจส่งผลต่อคุณภาพ โดยเฉพาะไขมัน แนวมลทางการใช้ประโยชน์จากวัตถุดิบที่มีส่วยช่วยในการเสริมสารอาหารแบะช่วยต้านการเกิดปฎิกิริยาออกซิเดชันเนื่องจากไขมัน จึงมีศักยภาพในการนำไปใช้ผลิตจริง กากกาแฟจึงนับเป็นวัตถุดิบที่มีคุณลักษณะดังกล่าว งานวิจัยนี้จึงใช้กากกาแฟในการเสริมอาหารให้กับแครกเกอร์ ด้วยกากกาแฟมีใยอาหาร มีสารประกอบโพลีฟีนอลดและยังเป็นการใช้ประโยชน์จากส่วนเหลือการชงกาแฟ ได้อย่างมีประสิทธิภาพ
วิทยาเขตชุมพรเขตรอุดมศักดิ์
Abstract: Banana French Fries This project aimed to study and develop the product Banana French Fries, which is a snack made by frying bananas in a form similar to French fries, in order to add value to bananas and create new choices for consumers. The experiment consisted of selecting suitable banana varieties, developing a coating formula, and testing the taste of samples. The results of the study found that Nam Wa bananas are the most suitable for making banana French fries because they have a firm texture and naturally sweet taste. The best coating formula consists of wheat flour, eggs, and milk, which provide longer crispiness. The taste test found that most consumers gave a very good response and were satisfied with the taste and texture. This project shows that banana French fries are a product with potential to be developed as a healthy snack and can be further developed into a commercial product in the future.
คณะวิทยาศาสตร์
With the urgent need for rapid screening of Aflatoxin B1 (AFB1) due to its association with increased liver cirrhosis and hepatocellular carcinoma cases from contaminated agricultural foods, we propose a novel electrochemical aptasensor. This aptasensor is based on trimetallic nanoparticles AuPt-Ru supported by reduced graphene oxide (AuPt-Ru/RGO) modified on a low-cost and disposable goldleaf electrode (GLEAuPt-Ru/RGO) for detection of AFB1. The trimetallic nanoparticle AuPt-Ru was synthesized using an ultrasonic-driven chemical reduction method. The synthesized AuPt-Ru exhibited a waxberry-like appearance, with AuPt core-shell structure and ruthenium dispersed over the particles. The average particle size was 57.35 ± 8.24 nm. The AuPt-Ru was integrated into RGO sheets (inner diameter of 0.5 to 1.6 µm) in order to enhance electron transfer efficiency and increase the specific immobilizing surface area of the thiol-5’-terminated modified aptamer (Apt) to target AFB1. With a large electrochemical surface area and low electrochemical impedance, GLEAuPt-Ru/RGO displays ultra-high sensitivity for AFB1 detection. Differential pulse voltammetry (DPV) measurements revealed a linear range for AFB1 detection range from 0.3 to 30.0 pg mL-1 (R2 = 0.9972), with a limit of detection (LOD, S/N = 3) and a limit of quantification (LOQ, S/N = 10) of 0.009 pg mL-1 and 0.031 pg mL-1, respectively. The developed aptasensor also demonstrated excellent accuracy in real agricultural products, including dried red chili, garlic, peanut, pepper, and Thai jasmine rice, achieving recovery rates between 94.6 and 107.9%. The fabricated aptamer-based GLEAuPt-Ru/RGO performance is comparable to that of a modified commercial electrode, which has great potential application prospects for detecting AFB1 in agricultural products.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.