基于高光谱成像与深度学习的食品品质快速无损检测方法研究

Research on Rapid Non-Destructive Detection Methods for Food Quality Based on Hyperspectral Imaging and Deep Learning

LUO Jinfeng， PU Yu，LIU Chenjiao （Sichuan Institute of Industrial Technology， Deyang 618ooo， China）

Abstract： Hyperspectral imaging technology， with its non-destructive detection advantage， can preserve the original form of food intactand comprehensivelyacquire both spatial and spectral information of food， providing al-round data support for quality assessment. Deep learning demonstrates powerful feature extraction and good generalizationcapabilities.Itcan automatically mine features closelyrelated to food quality， efectively reduce detection errors caused by individual differences，and flexibly handle complex and diverse detection tasks. Combining hyperspectral imaging technology with deep learning and applying it to food quality detection， in the fieldof agricultural products，rapid andaccurate detection of fruit maturity and internal defects，as wellas vegetable freshness and pesticide residues，canbe achieved.In the fieldof processed foods，effctive evaluationoffatcontent and tendermess in meat products，along with the tasteand shelf lifeofbaked goods，is possible.This methodoffers an efficient and precise solution for food quality detection， which is of greatsignificance in ensuring food safety， improving product quality， and optimizing the production and sales process.

Keywords： hyperspectral imaging; deep learning; food quality; non-destructive detection; agricultural producl detection; processed food detection

食品品质与人们的日常生活和健康息息相关，随着消费者对食品安全和品质要求的不断提高，以及食品产业的快速发展，对食品品质进行快速、准确且无损的检测变得愈发重要。（剩余4705字）