高光谱成像
计算机科学
转化式学习
领域(数学)
风险分析(工程)
质量(理念)
生化工程
数据科学
人工智能
业务
工程类
数学
心理学
哲学
认识论
纯数学
教育学
作者
Jiewen Zuo,Yankun Peng,Yongyu Li,Yahui Chen,Tianzhen Yin
标识
DOI:10.1021/acs.jafc.4c08680
摘要
Assessing the nutritional value of muscle food (MF) necessitates comprehensive component analysis. Traditional chemical analytical methods are often time-intensive, destructive, and environmentally detrimental, requiring specialized laboratory expertise. Hyperspectral imaging (HSI) emerges as an innovative technique that effectively integrates spectral and spatial information to enable rapid, nondestructive, and multidimensional predictions of nutritional parameters in MF. This Review examines the cutting-edge advancements in HSI technology, elucidating its novel technical and methodological dimensions. It systematically explores the principles and methodologies of HSI, presenting recent research and diverse applications in predicting MF nutritional parameters, and evaluates HSI's significant advantages and current limitations while addressing field-specific challenges and prospective research trends, ultimately positioning HSI as a potentially transformative tool in ensuring meat industry quality and safety.
科研通智能强力驱动
Strongly Powered by AbleSci AI