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Direct And Label Free Gram Differentiation Of Microbial Colonies On Agar by Hyperspectral Imaging

机译:用高光谱成像直接和标记自由克微生物菌落的微生物菌落

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Diffuse reflectance spectra of bacterial colonies, from hyperspectral images, allowed for a label-free Gram classification into Gram-positive (GP) and Gram-negative (GN) types. Thirty-eight strains belonging to 14 bacterial species typically encountered in urinary tract infections (UTI) were cultivated on chromID CPS Elite translucent chromogenic culture medium to build training and testing sets. Using Support Vector Machine (SVM) supervised learning models, we demonstrated excellent classification rates with a percentage of correctly classified samples as high as 95%. Because determination of discriminant spectral channels is critical both for fundamental reasons to help understand the origin of the discriminant signal and for practical reasons to envision simpler multispectral systems, parsimonious analysis was conducted employing a Fused LASSO (Least Absolute Shrinkage and Selection Operator) or based on an uncertainty test in Partial least squares PLS regression analysis. Two prominent distinct spectral regions were thus identified allowing to hypothesize that cytochrome ratios might be, at least in part, at the origin of the differences observed between Gram-negative and Gram-positive bacteria populations.
机译:细菌菌落的漫反射光谱,从高光谱图像,允许的无标记的革兰氏分类为革兰氏阳性(GP)和革兰氏阴性(GN)的类型。属于典型的尿路感染(UTI)遇到14个菌种三十八个菌株培养的chromID CPS精英半透明的显色培养基上打造的培训和测试集。基于支持向量机(SVM)监督学习模型,我们展现了出色的分类率有高达95%的正确分类样本的百分比。因为判别光谱信道的确定是既为根本原因,以帮助理解的判别信号的起源和实际原因设想更简单的多光谱系统的关键,是进行简约分析采用熔融LASSO(最小绝对收缩和选择算子),或基于在偏最小二乘的不确定性测试PLS回归分析中。被如此识别两个突出的不同的光谱区域,允许推测的细胞色素比率可能是,至少部分,在革兰氏阴性和革兰氏阳性菌群之间观察到的差异的来源。

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