Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Combination of multivariate curve resolution with factorial discriminant analysis for the detection of grapevine diseases using hyperspectral imaging. A case study: flavescence dorée

Hyperspectral imaging is an emergent technique in viticulture that can potentially detect bacterial diseases in a non-destructive manner. However, the main problem is to handle the substantial amount of information obtained from this type of data, for which reliable data analysis tools are necessary. In this work, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) is proposed to detect the flavescence dorée grapevine disease from hyperspectral imaging. The main purpose of MCR-ALS in this work was to provide chemically meaningful basic spectral signatures and distribution maps of the constituents needed to describe both healthy and infected leaf images by flavescence dorée. MCR scores (distribution maps) were used as the starting information for FDA to distinguish between healthy and infected pixels/images. Such an approach is presumably more powerful than the direct use of FDA on the raw imaging data, since MCR scores are compressed and noise-filtered information on pixel properties, which makes them more suitable for discrimination analysis. High levels of correct pixel discrimination rates (CR = 85.1%) for the MCR-ALS/FDA discrimination model were obtained. The model presents a lesser ability to determine infected leaves than healthy leaves. Nevertheless, only two images were misclassified. Therefore, the proposed strategy constitutes a good approach for the detection of flavescence dorée that could be potentially used to detect other phytopathologies.

Collaborative Low-Rank Matrix Approximation-Assisted Fast Hyperspectral Raman Imaging and Tip-Enhanced Raman Spectroscopic Imaging

Fast acquisition of Raman images is essential for accurately characterizing the analytes' information. In this paper, we developed a collaborative low-rank matrix approximation method for fast hyperspectral Raman imaging as well as tip-enhanced Raman spectroscopy (TERS) imaging. This method combines high signal-to-noise ratio (SNR) data with the target data to perform collaborative singular value decomposition. The high-quality reference data can impose constraints on factorization, which will force its components to approximate the true signal or noise components. The simulation demonstrated that this method offers state-of-the-art signal extraction performance and, thus, can be used to accelerate data acquisition. Specifically, the results indicate that the CLRMA can largely decrease the root-mean-square error by 20.92-54.12% compared with the baseline method of our previous study. We then applied this method to the fast TERS imaging of a Au/Pd bimetallic surface and significantly decreased the integration time down to 0.1 s/pixel, which is about 10 times faster than that of conventional experiments. High-SNR TERS spectra and clear TERS images that are well consistent with scanning tunneling microscopy (STM) images can be obtained even under such a weak signal condition. We further applied this method to the fast Raman imaging of HeLa cells and obtained clear Raman images at a short integration time of 2 s/line, which is about 5 times faster than that of conventional experiments. This method offers a promising tool for TERS imaging as well as conventional Raman imaging where fast data acquisition is required.

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

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.

3D and 4D Image Fusion: Coping with Differences in Spectroscopic Modes among Hyperspectral Images

Image fusion is often oriented to solve differences in spatial scale and orientation among different spectroscopic platforms. However, an additional problem arises when the nature of the spectroscopic information differs in dimensionality as well. Indeed, most imaging systems, e.g., Raman, IR, MS, etc., allow acquisition of 3D images, with a linear spectrum per pixel, but new platforms have emerged, such as the recent excitation-emission fluorescence imaging platforms that provide 4D images, with a 2D spectral landscape per pixel. A proper 3D/4D image fusion needs to take into account the difference in the dimension of the spectral information and in the underlying models of both measurements (bilinear for 3D images and trilinear for 4D images). This work solves this image fusion problem through a new dedicated variant of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for multiset analysis based on the incorporation of a hybrid bilinear/trilinear model that can handle the image fused structure preserving the natural behavior of the 3D and 4D imaging techniques coupled. The example is illustrated on the fusion of real 3D Raman and 4D fluorescence images recorded on cross sections of rice leaf samples.

A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

Hyperspectral imaging is a powerful tool to obtain both chemical and spatial information of biological systems. However, few algorithms are capable of working with full three-dimensional images, in which reshaping or averaging procedures are often performed to reduce the data complexity. Herein, we propose a new algorithm of three-dimensional principal component analysis (3D-PCA) for exploratory analysis of complete 3D spectrochemical images obtained through Raman microspectroscopy. Blood plasma samples of ten patients (5 healthy controls, 5 diagnosed with ovarian cancer) were analysed by acquiring hyperspectral imaging in the fingerprint region (∼780-1858 cm-1). Results show that 3D-PCA can clearly differentiate both groups based on its scores plot, where higher loadings coefficients were observed in amino acids, lipids and DNA regions. 3D-PCA is a new methodology for exploratory analysis of hyperspectral imaging, providing fast information for class differentiation.