Jia-Yong Song , Ze-Sheng Qin , Chang-Wen Xue , Li-Feng Bian , Chen Yang
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引用次数: 0
摘要
高光谱显微成像(HMI)可提供空间形态和光谱特征,是检测食品中微生物污染物的一种高效、非破坏性方法。为了在检测中减少热效应、降低成本并提高光谱分辨率,我们提出了一种流水线操作的 LED 单色照明模式,它集成了基于光栅和基于 LED 的 HMI 系统的设计理念。通过对 LED 组、共享光栅单色光路和协调控制系统的设计,开发出了一种高光谱人机界面系统,可获得 400-700 nm 范围内 101 个波段的高光谱数据立方体。利用该原型系统制备了三种曲霉菌的高光谱数据集,并在经典分类算法(1D-CNN (97.33 %)、k-NN (96.33 %)、SVM (97.67 %) 和 ResNet-18 (95.67 %))的训练和测试中取得了高效的结果。结果表明,所提出的单色照明模式和相关系统是低成本、高精度的食源性微生物污染物潜在检测解决方案。
A monochrome pipelined HMI system for foodborne microorganisms testing
Hyperspectral microscopy imaging (HMI) is an efficient and non-destructive method to detect microbial contaminants in food, as it can provide both spatial morphology and spectral signature. Aims at reducing thermal effect, low cost, and improving spectral resolution in testing, a pipeline-operated LEDs monochromatic illumination mode is proposed, which integrates the design concepts of both grating-based and LED-based HMI systems. By design of the LED set, shared grating monochromatic optical path, and coordinated control system, an HMI system has been developed that could obtain the hyperspectral data cube with 101 bands in 400–700 nm. Hyperspectral datasets of three species of Aspergillus are prepared using the prototype, and efficient results have been achieved in the training and testing of classical classification algorithms (1D-CNN (97.33 %), k-NN (96.33 %), SVM (97.67 %) and ResNet-18 (95.67 %)). The results demonstrate that the proposed monochromatic illumination mode and associated system are potential detection solutions for foodborne microbial contaminants with low-cost and high-accurate.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.