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
The evaluation of mango yield and consumer behavior reflects an increasing awareness of product origins, with a growing demand for traceability to understand how the produce has been cultivated and managed. This study explores the relationship between mango characteristics and cultivation practices before harvest, using location identification to provide insights into these processes. To achieve this, a model was developed to detect and locate mangoes using 2D images via a Deep Learning approach. The study also investigates techniques to determine the real-world coordinates of mangoes from 2D images. The YOLOv8 model was employed for object detection, integrated with camera calibration and triangulation techniques to estimate the 3D positions of detected mangoes. Experiments involved 125 trials with randomized mango positions and camera placements at varying yaw and pitch angles. Parameters extracted from sequential images were compared to derive the actual 3D positions of the mangoes. The YOLOv8 model demonstrated high performance with prediction metrics of Precision (0.928), Recall (0.901), mAP50 (0.965), mAP50-95 (0.785), and F1-Score (0.914). These results indicate sufficient accuracy for predicting mango positions, with an average positional error of approximately 38 centimeters.
คณะบริหารธุรกิจ
In a world increasingly focused on sustainability and reducing environmental impact, DreamHigh is pioneering an innovative approach to packaging solutions using mycelium—a natural, biodegradable, and renewable material derived from fungi. Our mission is to revolutionize the packaging industry by offering eco-friendly alternatives that not only reduce waste but also align with global efforts to combat climate change. Mycelium packaging offers a compelling alternative to traditional plastic and Styrofoam packaging, which contribute significantly to environmental pollution. It is fully biodegradable, compostable, and capable of breaking down in natural environments within weeks, leaving no toxic residues behind. Additionally, mycelium-based products are lightweight, durable, and customizable, making them suitable for a wide range of applications, from consumer goods packaging to protective shipping materials. DreamHigh’s business plan outlines a scalable production process leveraging advanced mycelium cultivation techniques and partnerships with local agricultural sectors to utilize agricultural waste as a key raw material. This not only ensures cost-efficiency but also supports a circular economy by repurposing waste that would otherwise be discarded.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
This study presents the development of carbon-based multiphase metal oxide nanocomposites (CNF@MOx; M = Ag, Mn, Bi, Fe) incorporating silver, manganese, bismuth, and iron nanoparticles within polyacrylonitrile (PAN)-derived carbon nanofibers. These nanocomposites were fabricated via the electrospinning technique followed by annealing in an argon atmosphere. The resulting nanofibers exhibited a uniform structure, with diameters ranging from 559 to 830 nm and embedded nanoparticles of 9-21 nm. Structural characterization confirmed the presence of various oxidation states of metal oxides, which play a crucial role in charge storage mechanisms. Electrochemical performance testing demonstrated that CNF@Ag/Mn/Bi/Fe-20 achieved the highest specific capacitance of 156 F g⁻¹ at a scan rate of 2 mV s⁻¹ and exhibited excellent cycling stability, retaining over 96% of its capacitance after 1400 charge-discharge cycles. The synergistic combination of electric double-layer capacitance and redox-based charge storage enhances the performance of these nanofibers as promising electrode materials for supercapacitor applications.