摘要
针对食品分析检测实验课程教学中人工智能实践案例欠缺、学科交叉融合不足等问题,设计了基于光谱快速检测猪肉品质的综合实验。该实验构建了从“数据采集—模型构建—测试优化—APP开发”的完整流程,系统引导学生掌握人工智能技术的应用路径,弥补现有课程在实践环节的空白。面对食品专业学生计算机基础薄弱、影响教学效果的情况,实验采用“AI辅助+课堂实践+课后拓展”的混合教学模式。通过引入生成式人工智能工具DeepSeek辅助数据分析与代码编写,有效降低了学生的编程门槛,实现了学习过程的个性化与指导的针对性。实验结果表明,所构建的深度学习模型在猪肉品质检测任务中表现优异,性能显著优于传统方法。实验有效促进了学生对人工智能技术与专业知识的融合与内化,切实提升了其跨学科创新思维与实践能力。
Addressing the lack of artificial intelligence(AI)practical cases and interdisciplinary integration in the teaching of food analysis and detection experiments.A comprehensive experiment for the rapid spectroscopic detection of pork quality was designed by using AI,which covers the full workflow from data acquisition and model construction to testing,optimization and APP development.The experiment systematically guides students through the complete application of AI technology.To overcome food science students’limited computational background,a blended teaching model which integrates AI assistance,in-class practice and after-class extension is adopted to achieve personalized learning and targeted guidance.Moreover,the generative AI tool DeepSeek is innovatively introduced to assist with data analysis and code writing,thereby reducing the barrier to programming.Results demonstrate that the deep learning model significantly outperforms traditional methods.This experiment can effectively help students internalize AI and professional knowledge,while enhancing interdisciplinary innovative thinking and practical ability.
作者
张琳
蒲洪彬
杨丽
娄文勇
ZHANG Lin;PU Hongbin;YANG Li;LOU Wenyong(College of Food Science and Engineering,South China University of Technology,Guangzhou 510641,China)
出处
《实验室研究与探索》
北大核心
2025年第12期152-157,165,共7页
Research and Exploration In Laboratory
基金
国家重点研发计划项目(2018YFC1603404)
华南理工大学人工智能赋能本科教学试点课程项目(C9252010)
华南理工大学探索性实验项目(C9252600)。
关键词
人工智能
学科交叉
猪肉品质快速检测
教学质量
综合实验
artificial intelligence
interdisciplinary integration
rapid pork quality detection
teaching quality
comprehensive experiment
