Enhancing Culinary Precision: Students Embarking on a Project-Based Learning Adventure

Simon Zhang; Joshua Dennis; Haridas Kumarakuru; Bala Maheswaran

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Student Division Technical Session 4: Project-based Learning
Tagged Division: Student Division (STDT)
Permanent URL: https://peer.asee.org/47302

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Haridas Kumarakuru, PhD, MInstP
Department of Physics,
College of Science,
Northeastern University,
360 Huntington Ave, Boston, MA 02115
E.Mail: h.kumarakuru@northeastern.edu

Hari has 18+ years of educational leadership experience amplifying academic and scientific endeavours in the higher education setting that has brought him to four separate continents. He capitalizes on his in-depth competencies in curriculum implementation, instructional delivery, scientific research, technical writing, and student mentoring to provide students with the tools for academic and professional success. Since 2007, he has had the privilege of mentoring numerous undergraduate and master’s students, a pursuit he is most passionate about. He has applied his established teaching skills to a wide range of undergraduate courses in general physics, engineering physics, electronics for scientists, advanced physics labs and specialized courses in the fields of functional nano material science and nanotechnology. Hari is a member of IOP (UK), JSA, AAPT and ASEE and he is a reviewer for several scientific journals.

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Bala Maheswaran Northeastern University

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Bala Maheswaran, PhD
Northeastern University
367 Snell Engineering Center
Boston, MA 02115

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Abstract

In the dominion of project-based learning, we embarked on a journey to create an innovative time prediction thermometer tailored for food systems. Throughout this endeavor, we explored a range of fundamental principles that have proven invaluable for our lifelong learning journey. As students, this project provided fertile ground for honing our problem-solving skills and immersing ourselves in the intricate world of engineering design.

Our journey began with identifying a culinary challenge, followed by brainstorming potential solutions, selecting the most efficient approach, and executing it meticulously. Beyond these experiences, this endeavor equipped us with a wealth of practical skills, including fabrication, design, analysis, and the art of technical writing. It served as a platform for us to refine our expertise in computer-aided design (CAD), research methodologies, and the dynamics of collaborative teamwork. Remarkably, the implications of our design extend beyond the confines of our educational journey, offering potential applications within the broader realm of engineering education

Presently, many amateur cooks lack the intuition and experience required to gauge the optimal cooking time, often resulting in suboptimal culinary outcomes that are either undercooked or overcooked. Our team made a deliberate choice to construct a temperature probe that would mitigate this issue by precisely measuring the internal temperature of food and forecasting its future temperature, which we have aptly named a "smart thermometer." In this paper, we elucidate the design criteria for ThermoChef++ (TC++), a budget-friendly smart thermometer, in comparison to existing time prediction models in literature and smart thermometers available in the consumer market. We elucidate the development of software and Arduino circuits in the realization of our project.

During the culinary process, TC++ continuously monitors real-time temperature data and employs regression analysis to construct thermal models that predict the future behavior of the food system. An additional feature of TC++ is a library containing standard cooking models for common foods, promoting heightened awareness of cooking safety. The current iteration of our product is tailored for novice cooks who rely on indirect temperature cues. However, an enhanced iteration of TC++ holds potential implications for the food manufacturing industry.

Citation
Format

Zhang, S., & Dennis, J., & Kumarakuru, H., & Maheswaran, B. (2024, June), Enhancing Culinary Precision: Students Embarking on a Project-Based Learning Adventure Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47302

TY  - CPAPER
AB  - In the dominion of project-based learning, we embarked on a journey to create an innovative time prediction thermometer tailored for food systems. Throughout this endeavor, we explored a range of fundamental principles that have proven invaluable for our lifelong learning journey. As students, this project provided fertile ground for honing our problem-solving skills and immersing ourselves in the intricate world of engineering design.

Our journey began with identifying a culinary challenge, followed by brainstorming potential solutions, selecting the most efficient approach, and executing it meticulously. Beyond these experiences, this endeavor equipped us with a wealth of practical skills, including fabrication, design, analysis, and the art of technical writing. It served as a platform for us to refine our expertise in computer-aided design (CAD), research methodologies, and the dynamics of collaborative teamwork. Remarkably, the implications of our design extend beyond the confines of our educational journey, offering potential applications within the broader realm of engineering education

Presently, many amateur cooks lack the intuition and experience required to gauge the optimal cooking time, often resulting in suboptimal culinary outcomes that are either undercooked or overcooked. Our team made a deliberate choice to construct a temperature probe that would mitigate this issue by precisely measuring the internal temperature of food and forecasting its future temperature, which we have aptly named a &quot;smart thermometer.&quot; In this paper, we elucidate the design criteria for ThermoChef++ (TC++), a budget-friendly smart thermometer, in comparison to existing time prediction models in literature and smart thermometers available in the consumer market. We elucidate the development of software and Arduino circuits in the realization of our project.

During the culinary process, TC++ continuously monitors real-time temperature data and employs regression analysis to construct thermal models that predict the future behavior of the food system. An additional feature of TC++ is a library containing standard cooking models for common foods, promoting heightened awareness of cooking safety. The current iteration of our product is tailored for novice cooks who rely on indirect temperature cues. However, an enhanced iteration of TC++ holds potential implications for the food manufacturing industry.

AU  - Simon Zhang
AU  - Joshua Dennis
AU  - Haridas Kumarakuru
AU  - Bala Maheswaran
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Enhancing Culinary Precision: Students Embarking on a Project-Based Learning Adventure 
UR  - https://peer.asee.org/47302
ER  -