高光谱成像
偏最小二乘回归
化学计量学
模式识别(心理学)
人工智能
线性判别分析
多光谱图像
多元统计
计算机科学
生物系统
人口
主成分分析
数学
生物
机器学习
人口学
社会学
作者
Víctor Olmos,Mónica Marro,Pablo Loza‐Alvarez,Demetrio Raldúa,Eva Prats,Francesc Padrós,Benjamı́n Piña,Romá Tauler,Anna de Juan
标识
DOI:10.1002/jbio.201700089
摘要
Changes on an organism by the exposure to environmental stressors may be characterized by hyperspectral images (HSI), which preserve the morphology of biological samples, and suitable chemometric tools. The approach proposed allows assessing and interpreting the effect of contaminant exposure on heterogeneous biological samples monitored by HSI at specific tissue levels. In this work, the model example used consists of the study of the effect of the exposure of chlorpyrifos‐oxon on zebrafish tissues. To assess this effect, unmixing of the biological sample images followed by tissue‐specific classification models based on the unmixed spectral signatures is proposed. Unmixing and classification are performed by multivariate curve resolution‐alternating least squares (MCR‐ALS) and partial least squares‐discriminant analysis (PLS‐DA), respectively. Crucial aspects of the approach are: (1) the simultaneous MCR‐ALS analysis of all images from 1 population to take into account biological variability and provide reliable tissue spectral signatures, and (2) the use of resolved spectral signatures from control and exposed populations obtained from resampling of pixel subsets analyzed by MCR‐ALS multiset analysis as information for the tissue‐specific PLS‐DA classification models. Classification results diagnose the presence of a significant effect and identify the spectral regions at a tissue level responsible for the biological change.
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