用于食品安全自动化智能检测的多模态光学传感系统

IF 1.2 4区 农林科学 Q3 AGRICULTURAL ENGINEERING
J. Qin, Jeehwa Hong, Hyunjeong Cho, J. V. Van Kessel, I. Baek, K. Chao, M. Kim
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引用次数: 1

摘要

开发了一种用于食品安全的多模态光学传感系统。原型系统可以在785和1064 nm处进行双波段拉曼光谱。该系统可以自动测量培养皿或孔板中的样品。这个带有人工智能软件的系统有望用于识别食源性细菌的种类。摘要为实现食品安全检测的自动化和智能化,研制了一种新型的多模态光学传感系统。该系统采用785 nm和1064 nm的两对紧凑点激光器和色散光谱仪实现双波段拉曼光谱和成像,分别适用于测量产生低荧光和高荧光干涉信号的样品。自动光谱采集可以使用直接驱动的XY移动平台,将固体、粉末和液体样品放置在定制的孔板中或随机分散在标准培养皿中(例如,细菌菌落)。三个LED灯(白色背光、UV环光和白色环光)和两个微型彩色相机用于皮氏培养皿中样品的机器视觉测量,使用不同的照明和成像模式组合(例如,透射、荧光和彩色)。实时图像处理和运动控制技术用于实现自动样本计数,定位,采样和同步功能。系统软件采用集成人工智能功能的LabVIEW开发,能够即时识别和标记感兴趣的目标。通过对5种常见食源性细菌(包括蜡样芽孢杆菌、大肠杆菌、单核增生李斯特菌、金黄色葡萄球菌和沙门氏菌)的快速鉴定,验证了该系统的功能。利用基于线性支持向量机的机器学习模型,对生长在90 mm培养皿中的5种细菌222个菌落的拉曼光谱进行自动采集,分类准确率达到98.6%。整个系统建立在30×45 cm2面包板上,使其紧凑便携,可用于监管和工业应用中的现场和现场生物和化学食品安全检查。关键词:人工智能,自动采样,细菌,食品安全,机器学习,机器视觉,拉曼,传感
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multimodal Optical Sensing System for Automated and Intelligent Food Safety Inspection
Highlights A multimodal optical sensing system was developed for food safety applications. The prototype system can conduct dual-band Raman spectroscopy at 785 and 1064 nm. The system can automatically measure samples in Petri dishes or well plates. The system with AI software is promising for identifying species of foodborne bacteria. Abstract. A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabVIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30×45 cm2 breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications. Keywords: Artificial intelligence, Automated sampling, Bacteria, Food safety, Machine learning, Machine vision, Raman, Sensing.
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