基于高光谱技术的培养基上细菌菌落分类方法研究

利用高光谱技术对培养基上细菌(大肠杆菌、李斯特菌和金黄色葡萄球菌)菌落进行快速识别和分类。采集琼脂培养基上细菌菌落的高光谱反射图像(390~1040 nm),在对波段差图像进行大津阈值分割的基础上自动提取细菌菌落光谱,并建立细菌分类检测的全波长和简化偏最小二乘判别( PLS-DA)模型。全波长模型对预测集样本的分类准确率和置信预测分类准确率分别为100%和95.9%。此外,利用竞争性自适应重加权算法( CARS)、遗传算法( GA)和最小角回归算法( LARS-Lasso)进行波长优选并建立对应简化模型。其中,CARS简化模型在精度、稳定性及分类准确率方面均优于GA和LARS-Lasso简化模型,其对预测集样本的分类准确率和置信预测分类准确率分别达到了100%和98.0%。研究表明,高光谱是一种细菌菌落高精度、快速、无损识别检测的有效方法。简化模型中优选的波长可以为开发低成本检测仪器提供理论依据。

Classification of Bacterial Colonies on Agar Plates Using Hyperspectral Imaging Technology

Rapid detection and classification of bacteria colonies ( Escherichia coli, Listeria monocytogens and Staphylococcus aureus) were investigated by using hyperspectral imaging. The hyperspectral reflectance images (390-1040 nm ) of bacterial colonies on agar plates were collected. Bacterial spectra were extracted automatically based on the masks produced by segmenting a band difference image using the OTSU method. Full wavelength and simplified PLS-DA models were established for classification of bacterial colonies. For the full wavelength model, the overall correct classification rate ( OCCR) and confident OCCR for the prediction set were 100% and 95. 9%, respectively. Besides, competitive adaptive reweighted sampling ( CARS), genetic algorithm ( GA ) and least angle regression-least absolute shrinkage and selection operator ( LARS-Lasso) were used to select feature wavelengths for the development of simplified models. Among them, the CARS-model outperformed the other two in terms of precision, stability and classification accuracy with OCCR and confident OCCR of 100% and 98. 0% for the prediction set, respectively. It was demonstrated that hyperspectral imaging was an effective technology for nondestructive detection of bacterial colonies with high accuracy and high speed. The allocated feature wavelengths by CARS could lay theoretical basis for developing low cost multispectral imaging systems for bacterial colony detection.