Hyperspectral imaging (HSI) and classification are established methods that are being applied in new ways to the analysis of nanoscale materials in a variety of matrices. Typically, enhanced darkfield microscopy (EDFM)‐based HSI data (also known as image datacubes) are collected in the wavelength range of 400–1000 nm for each pixel in a datacube. Utilising different spectral library (SL) creation methods, spectra from pixels in the datacube corresponding to known materials can be collected into reference spectral libraries (RSLs), which can be used to classify materials in datacubes of experimental samples using existing classification algorithms. In this study, EDFM‐HSI was used to visualise and analyse industrial cerium oxide (CeO2; ceria) nanoparticles (NPs) in rat lung tissues and in aqueous suspension. Rats were exposed to ceria NPs via inhalation, mimicking potential real‐world occupational exposures. The lung tissues were histologically prepared: some tissues were stained with hematoxylin and eosin (H&E) and some were left unstained. The goal of this study was to determine how HSI and classification results for ceria NPs were influenced by (1) the use of different RSL creation and classification methods and (2) the application of those methods to samples in different matrices (stained tissue, unstained tissue, or aqueous solution). Three different RSL creation methods – particle filtering (PF), manual selection, and spectral hourglass wizard (SHW) – were utilised to create the RSLs of known materials in unstained and stained tissue, and aqueous suspensions, which were then used to classify the NPs in the different matrices. Two classification algorithms – spectral angle mapper (SAM) and spectral feature fitting (SFF) – were utilised to determine the presence or absence of ceria NPs in each sample. The results from the classification algorithms were compared to determine how each influenced the classification results for samples in different matrices. The results showed that sample matrix and sample preparation significantly influenced the NP classification thresholds in the complex matrices. Moreover, considerable differences were observed in the classification results when utilising each RSL creation and classification method for each type of sample. Results from this study illustrate the importance of appropriately selecting HSI algorithms based on specific material and matrix characteristics in order to obtain optimal classification results. As HSI is increasingly utilised for NP characterisation for clinical, environmental and health and safety applications, this investigation is important for further refining HSI protocols while ensuring appropriate data collection and analysis.

中文翻译：

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用于分析组织学和水性样品中氧化铈纳米颗粒的高光谱分类方法的比较

高光谱成像 (HSI) 和分类是已建立的方法，正以新的方式应用于分析各种矩阵中的纳米级材料。通常，针对数据立方体中的每个像素，在 400-1000 nm 的波长范围内收集基于增强型暗场显微镜 (EDFM) 的 HSI 数据（也称为图像数据立方体）。利用不同的光谱库 (SL) 创建方法，可以将数据立方体中与已知材料对应的像素的光谱收集到参考光谱库 (RSL) 中，参考光谱库 (RSL) 可用于使用现有分类算法对实验样品数据立方体中的材料进行分类。在这项研究中，EDFM-HSI 用于可视化和分析大鼠肺组织和水悬浮液中的工业氧化铈（CeO2；二氧化铈）纳米颗粒（NPs）。大鼠通过吸入暴露于二氧化铈 NPs，模拟潜在的现实世界职业暴露。肺组织经组织学制备：一些组织用苏木精和伊红 (H&E) 染色，一些组织未染色。本研究的目的是确定二氧化铈 NP 的 HSI 和分类结果如何受到 (1) 使用不同的 RSL 创建和分类方法以及 (2) 将这些方法应用于不同基质（染色组织、未染色组织）中的样品的影响组织或水溶液）。三种不同的 RSL 创建方法——粒子过滤 (PF)、手动选择和光谱沙漏向导 (SHW)——被用来创建未染色和染色组织中已知材料的 RSL，然后用于对 NP 进行分类在不同的矩阵中。两种分类算法——光谱角度映射器 (SAM) 和光谱特征拟合 (SFF)——被用来确定每个样品中氧化铈 NP 的存在与否。比较分类算法的结果，以确定每个算法如何影响不同矩阵中样本的分类结果。结果表明，样品基质和样品制备显着影响复杂基质中的 NP 分类阈值。此外，当对每种类型的样本使用每种 RSL 创建和分类方法时，在分类结果中观察到相当大的差异。Results from this study illustrate the importance of appropriately selecting HSI algorithms based on specific material and matrix characteristics in order to obtain optimal classification results.