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作 者:徐帅 朱启兵[1] 黄敏[1] XU Shuai;ZHU Qibing;HUANG Min(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)
出 处:《激光技术》2023年第6期872-880,共9页Laser Technology
基 金:国家自然科学基金资助项目(61775086)。
摘 要:为了解决食品生产加工场所的卫生检查地点不固定、检测效果受环境因素影响大等问题,根据不同种类食品残留物在紫外光照射下产生不同波长荧光的特性,采用荧光成像技术和分块大津算法,开发了一种能够协助现场检测员进行视觉卫生检查的手持式荧光成像设备。利用3种常见材料表面(木制案板、不锈钢板、聚乙烯塑料板)对奶粉(体积分数为50%、33%、20%、10%、2%)和菠菜残渣(体积分数为50%、33%、25%、20%、10%)进行了实验验证。结果表明,该系统可以协助检测员进行卫生安全检查,不同体积分数菠菜残渣在3种材料表面检出率为100%,不同体积分数奶粉残留物在木制案板和不锈钢板表面检出率为100%,在聚乙烯塑料板表面,除体积分数为2%的奶粉残留物部分检出外,其余体积分数奶粉残留物均能有效检出。该研究为食品加工场所手持式卫生检测设备开发提供了参考。In order to solve the problems of food production and processing sites,such as the unfixed location of health inspection and the great influence of environmental factors on the inspection effect,according to the characteristics of different kinds of food residues under ultraviolet irradiation,a handheld fluorescence imaging device was developed by using fluorescence imaging technology and block Otsu algorithm,which can assist field inspectors in carrying out visual health inspection.The equipment detected milk powder(the volume fractions are 50%,33%,20%,10%,and 2%)and spinach residue(the volume fractions are 50%,33%,25%,20%,and 10%)on the surface of three common materials(wooden board,stainless steel board,polyethylene board).The results show that the system can assist inspectors in carrying out health and safety inspections.The detection rate of spinach residue with different volume fractions on the surface of the three materials is 100%,the detection rate of milk powder residue with different volume fractions on the surface of the wooden board and the stainless steel board is 100%,and on the surface of polyethylene plastic plate,except for the residual part of milk powder with the volume fractions of 2%,the remaining volume fraction of milk powder residue can be effectively detected.This study provides a reference for the development of handheld sanitary inspection equipment in food processing places.
关 键 词:成像系统 残留物检测 荧光成像技术 食品安全 现场可编程门阵列 分块大津算法
分 类 号:TP29[自动化与计算机技术—检测技术与自动化装置]
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