FSHN 499

Fall 2026 Part of Term 1

Part of Term 1
Aug 24-Dec 9

Cur Topics in FS & Human Nutr

Credit: 1 TO 3 hours.

Group discussion or an experimental course on a special topic in food science and human nutrition.

1 to 3 undergraduate hours. 1 to 3 graduate hours. Approved for Letter and S/U grading. May be repeated in the same or subsequent terms to a maximum of 12 hours as topics vary.

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FSHN 499 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			19588	Lecture Online	AIA AIA	10:00AM -10:50AM ARRANGED	F n.a.	272 Agricultural Engr Sciences Bld n.a.	Stasiewicz, M Stasiewicz, M	Availability: Open Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Title: Food Safety AIA Section Info: FOOD SAFETY AUDITING, INSPECTIONS AND ASSESSMENTS: This course introduces students to food safety auditing, inspections, and assessments (AIA) across regulatory, third‑party, and industry contexts. Students explore how food safety systems are governed at local, national, and international levels and practice foundational auditing skills through applied, scenario‑based learning. Emphasis is placed on risk‑based thinking, professional judgment, and effective communication of audit findings. The course will be offered as part of a multi‑institution cohort, enabling shared guest speakers, collaborative activities, and exposure to diverse food systems.
			10184	Lecture	AIE	2:00PM -3:50PM	W	144 Bevier Hall	Xu, C	Availability: Open Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 2 hours Section Title: AI-ENABLED FOOD PROC Section Info: AI-ENABLED SUSTAINABLE FOOD PROCESSING: The future of food manufacturing is digital, intelligent, and sustainable. As the global food system faces growing challenges related to climate change, resource constraints, supply chain disruptions, and increasing consumer demand for transparency and quality, the integration of artificial intelligence and sustainability-driven innovation is transforming how food is produced and processed. In this course, students will learn how AI, digital technologies, and sustainability principles are reshaping modern food processing systems. Students will explore how AI can optimize processing operations, reduce energy and water use, minimize waste, enhance food safety, improve product consistency, and strengthen resilient supply chains. Through case studies and applied data exercises, students will gain experience with digital sensing, predictive modeling, resource-efficient manufacturing strategies, circular processing approaches, and data-driven quality control. By integrating concepts from food engineering, food chemistry, sustainability assessment, and data analytics, students will develop the knowledge and practical skills needed to design and evaluate next-generation, climate-smart food manufacturing systems. Pre-requisites: Introductory Food Processing or Food Engineering, Introductory Statistics.