Department of Life Sciences, Interdepartmental Research Centre for Agri-Food Biological Resources Improvement and Valorization, University of Modena and Reggio Emilia, Padiglione Besta, Via Amendola 2, 42122 Reggio Emilia, Italy.
Anal Chim Acta. 2013 Nov 13;802:29-39. doi: 10.1016/j.aca.2013.10.009. Epub 2013 Oct 11.
Hyperspectral Imaging (HSI) is gaining increasing interest in the field of analytical chemistry, since this fast and non-destructive technique allows one to easily acquire a large amount of spectral and spatial information on a wide number of samples in very short times. However, the large size of hyperspectral image data often limits the possible uses of this technique, due to the difficulty of evaluating many samples altogether, for example when one needs to consider a representative number of samples for the implementation of on-line applications. In order to solve this problem, we propose a novel chemometric strategy aimed to significantly reduce the dataset size, which allows to analyze in a completely automated way from tens up to hundreds of hyperspectral images altogether, without losing neither spectral nor spatial information. The approach essentially consists in compressing each hyperspectral image into a signal, named hyperspectrogram, which is created by combining several quantities obtained by applying PCA to each single hyperspectral image. Hyperspectrograms can then be used as a compact set of descriptors and subjected to blind analysis techniques. Moreover, a further improvement of both data compression and calibration/classification performances can be achieved by applying proper variable selection methods to the hyperspectrograms. A visual evaluation of the correctness of the choices made by the algorithm can be obtained by representing the selected features back into the original image domain. Likewise, the interpretation of the chemical information underlying the selected regions of the hyperspectrograms related to the loadings is enabled by projecting them in the original spectral domain. Examples of applications of the hyperspectrogram-based approach to hyperspectral images of food samples in the NIR range (1000-1700 nm) and in the vis-NIR range (400-1000 nm), facing a calibration and a defect detection issue respectively, demonstrate the effectiveness of the proposed approach.
高光谱成像（HSI）在分析化学领域越来越受到关注，因为这种快速、非破坏性的技术可以在非常短的时间内轻松获取大量的光谱和空间信息，适用于广泛的样本。然而，高光谱图像数据的庞大规模通常限制了该技术的可能用途，因为评估大量样本的难度较大，例如，当需要考虑代表性数量的样本以实现在线应用时。为了解决这个问题，我们提出了一种新的化学计量学策略，旨在显著减少数据集的大小，使得能够以完全自动化的方式分析数十个甚至数百个高光谱图像，而不会丢失光谱或空间信息。该方法的本质是将每个高光谱图像压缩成一个信号，称为高光谱图，通过将 PCA 应用于每个高光谱图像所得到的几个量组合而创建。高光谱图可以用作紧凑的描述符集，并可以对其进行盲分析技术处理。此外，通过对高光谱图应用适当的变量选择方法，可以进一步提高数据压缩和校准/分类性能。通过将所选特征映射回原始图像域，可以获得算法所做选择正确性的直观评估。同样，可以通过将其投影到原始光谱域中，解释与载荷相关的高光谱图中所选区域所包含的化学信息。在近红外（1000-1700nm）和可见-近红外（400-1000nm）范围内的食品样本高光谱图像中应用基于高光谱图的方法的示例，分别解决了校准和缺陷检测问题，证明了所提出方法的有效性。
