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首页> 外文会议>Image processing: machine vision applications VII >Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates
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Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

机译：使用RGB颜色的高光谱图像重建，用于琼脂平板上食源性病原体检测

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This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean R-squared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.
机译：本文报告了一种彩色视觉技术的最新发展，该技术可利用高光谱图像分类模型检测在琼脂平板上生长的食源性病原菌的菌落，该模型使用完整的高光谱数据开发。高光谱分类模型取决于在400到1,000 nm（473个窄光谱带）的可见光谱和近红外光谱范围内测得的反射光谱。多变量回归方法用于根据RGB颜色值估计和预测高光谱数据。在Rainbow琼脂平板上培养了六个代表性的非O157志贺毒素生产性大肠杆菌（STEC）血清群（O26，O45，O103，O111，O121和O145）。使用线扫描推扫式高光谱图像传感器扫描在每个平板上生长有纯STEC菌落的36个琼脂平板。将琼脂板的36个高光谱图像分成两半，以创建训练集和测试集。对于线性，二次和三次多项式回归模型，在400至700 nm的光谱范围内，用于高光谱图像估计的平均R平方值约为0.98，并且具有主成分分析和k近邻的高光谱图像分类模型的检测精度测试集的邻居比例最高为92％（原始高光谱图像为99％）。因此，研究结果表明，与高光谱成像相比，基于颜色的检测作为多光谱成像解决方案可能是可行的，而不会大大降低预测精度。

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