Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, 08028, Barcelona, Spain.
ICFO- Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860, Castelldefels, Spain.
Sci Rep. 2021 Sep 20;11(1):18665. doi: 10.1038/s41598-021-98000-0.
Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.
高光谱成像（HSI）是一种有用的非侵入性技术，可提供样品的空间和化学信息。通常，通过结合不同的图像模式（图像融合），使用不同的 HSI 技术从样品中获取互补信息。然而，需要正确解决与不同空间分辨率、样品方向或平台之间扫描区域相关的问题。解混方法有助于分析和解释与信号中每个贡献成分相关的 HSI 信息。在这些方法中，多变量曲线分辨交替最小二乘法（MCR-ALS）为图像融合提供了非常合适的功能，因为它可以轻松处理由来自不同样品和平台的图像块组成的多数据集结构，并允许使用可选和多样的约束条件来适应每个 HSI 的特定特征。在这项工作中，提出了一个基于拉曼、同步辐射红外和荧光成像技术对水稻叶片横截面进行研究的案例。首次将这三种不同技术的 HSI 融合到单个数据结构中，并通过 MCR-ALS 进行分析。这个例子在本质上具有挑战性，特别适合清楚地描述在图像融合背景下进行解混所需的步骤。尽管该方案是针对植物组织的研究提出并应用的，但它通常可以用于许多其他样品和成像平台的组合。
