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Spectral Identity Mapping for Enhanced Chemical Image Analysis

机译:光谱身份映射用于增强化学图像分析

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Advances in spectral imaging instrumentation during the last two decades has lead to higher image fidelity, tighter spatial resolution, narrower spectral resolution, and improved signal to noise ratios. An important sub-classification of spectral imaging is chemical imaging, in which the sought-after information from the sample is its chemical composition. Consequently, chemical imaging can be thought of as a two-step process, spectral image acquisition and the subsequent processing of the spectral image data to generate chemically relevant image contrast. While chemical imaging systems that provide turnkey data acquisition are increasingly widespread, better strategies to analyze the vast datasets they produce are needed. The Generation of chemically relevant image contrast from spectral image data requires multivariate processing algorithms that can categorize spectra according to shape. Conventional chemometric techniques like inverse least squares, classical least squares, multiple linear regression, principle component regression, and multivariate curve resolution are effective for predicting the chemical composition of samples having known constituents, but are less effective when a priori information about the sample is unavailable. To address these problems, we have developed a fully automated non-parametric technique called spectral identity mapping (SIMS) that reduces the dependence of spectral image analysis on training datasets. The qualitative SIMS method provides enhanced spectral shape specificity and improved chemical image contrast. We present SIMS results of infrared spectral image data acquired from polymer coated paper substrates used in the manufacture of pressure sensitive adhesive tapes. In addition, we compare the SIMS results to results from spectral angle mapping (SAM) and cosine correlation analysis (CCA), two closely related techniques.
机译:在过去的二十年中,光谱成像仪器的进步导致了更高的图像保真度,更严格的空间分辨率,更窄的光谱分辨率以及更高的信噪比。光谱成像的重要子分类是化学成像,其中从样品中寻求的信息是其化学组成。因此,化学成像可以被认为是两步过程,即光谱图像采集和光谱图像数据的后续处理以产生化学相关的图像对比度。尽管提供交钥匙数据采集的化学成像系统越来越普及,但仍需要更好的策略来分析它们产生的大量数据集。从光谱图像数据生成化学相关的图像对比度需要多种处理算法,这些算法可以根据形状对光谱进行分类。常规化学计量学技术,例如反最小二乘,经典最小二乘,多元线性回归,主成分回归和多元曲线分辨率,可有效预测具有已知成分的样品的化学组成,但在无法获得有关样品的先验信息时效果不佳。为了解决这些问题,我们开发了一种称为光谱身份映射(SIMS)的全自动非参数技术,该技术可减少光谱图像分析对训练数据集的依赖性。 SIMS定性方法提供了增强的光谱形状特异性和改进的化学图像对比度。我们提供SIMS结果的红外光谱图像数据,该数据是从用于压敏胶带制造中的聚合物涂层纸基材获得的。此外,我们将SIMS结果与光谱角映射(SAM)和余弦相关分析(CCA)这两种密切相关的技术的结果进行了比较。

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