This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.
โรงงานผู้ผลิตข้าวพบปัญหาการปลอมปนของข้าวสารที่มีอายุการเก็บรักษาต่างกัน โดยทั่วไปการคัดแยกการปลอมปนจะใช้วิธีมาตรฐานโดยการหุงข้าว จากนั้นนำข้าวหุงสุกไปวัดเนื้อสัมผัสเพื่อแยกอายุของข้าว ซึ่งใช้เวลาและเป็นการทำลายตัวอย่างและเกิดความล่าช้าในการตรวจสอบคุณภาพข้าวสาร งานวิจัยนี้ใช้เทคนิคเนียร์อินฟราเรดสเปกโทรสโกปี (Near-Infrared Spectroscopy, NIRS) ในการตรวจสอบการปลอมปนของข้าวสารพันธุ์ขาวดอกมะลิ 105 (KDML 105) ที่อายุการเก็บรักษาต่างกันเพื่อแก้ไขปัญหาดังกล่าว
คณะวิศวกรรมศาสตร์
This research focuses on the development of a 3D-printed spectrophotometer. The device was designed using AutoCAD, ensuring durability, low cost, and portability. It is intended for measuring the amount of formaldehyde in fresh seafood.
คณะอุตสาหกรรมอาหาร
Tepache is a traditional Mexican fermented beverage commonly made using pineapple peels, which naturally contain sugars and the enzyme bromelain. These components contribute to its distinctive aroma and unique flavor. This project aims to develop a health-enhancing tepache by fermenting pineapple peels with probiotic yeast and lactic acid bacteria. Additionally, prebiotics, including inulin and xylo-oligosaccharides, are incorporated as nutrients to support probiotic growth. The resulting synbiotic tepache promotes gut microbiota balance, exhibits antioxidant properties, and enhances the immune system, making it a functional and beneficial beverage for consumers.
คณะวิทยาศาสตร์
This special problem aims to study and compare the performance of predicting the air quality index (AQI) using five ensemble machine learning methods: random forest, XGBoost, CatBoost, stacking ensemble of random forest and XGBoost, and stacking ensemble of random forest, SVR, and MLP. The study uses a dataset from the Central Pollution Control Board of India (CPCB), which includes fifteen pollutants and nine meteorological variables collected between January, 2021 and December, 2023. In this study, there were 1,024,920 records. The performance is measured using three methods: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination. The study found that the random forest and XGBoost stacking ensemble had the best performance measures among the three methods, with the minimum RMSE of 0.1040, the minimum MAE of 0.0675, and the maximum of 0.8128. SHAP-based model interpretation method for five machine learning methods. All methods reached the same conclusion: the two variables that most significantly impacted the global prediction were PM2.5 and PM10, respectively.