Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum).

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

AB-PLS-DA: Pansharpening tailored for scanning electron microscopy and energy-dispersive X-ray spectrometry multimodal fusion.

Pansharpening constitutes a category of data fusion techniques designed to enhance the spatial resolution of multispectral (MS) images by integrating spatial details from a high-resolution panchromatic (PAN) image. This process combines the high-spectral data of MS images with the rich spatial information of the PAN image, resulting in a pansharpened output ideal for more effective image analysis, such as object detection and environmental monitoring. Traditionally developed for satellite data, our paper introduces a novel pansharpening approach customized for the fusion of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectrometry (EDS) data. The proposed method, grounded in Partial Least Squares regression with Discriminant Analysis (PLS-DA), significantly boosts the spatial resolution of EDS data while preserving spectral details. A key feature of this approach involves partitioning the PAN image into intensity bins and dynamically adapting this division in cases of overlapping compounds with similar average atomic numbers. We evaluate the method's effectiveness using in-house EDS images obtained from both even and uneven sample surfaces. Comparative analysis against existing benchmarks and state-of-the-art pansharpening techniques demonstrates superior performance in both spectral and spatial quality indicators for our method.

Optimizing energy dispersive X-Ray Spectroscopy (EDS) image fusion to Scanning Electron Microscopy (SEM) images.

Fusion and quality enhancement of the low-resolution Energy Dispersive X-ray Spectroscopy (EDS) maps to Scanning Electron Microscopy (SEM) panchromatic images has been proven effective by various pansharpening algorithms. The present paper aims to target the preprocessing of these maps to enhance the efficiency of the pansharpening process, with as little information loss on the chemical distribution, and as little propagated noise as possible. EDS maps present different noise intensities depending on the flatness of the surface of the analyzed object. The uneven surface maps have limited analytical value due to the noise and have not been resolution-enhanced with pansharpening due to the noise propagation limitation. In this paper, different preprocessing methods are evaluated for enabling uneven-surface particles to pansharpening: background removal, upsampling, and noise filtering. The sequence of applying preprocessing steps is analyzed. The optimal order of preprocessing steps is (i) background removal, (ii) noise filtering, and (iii) interpolation. A methodology for each of these steps is presented in the paper. The best performing pansharpening methodology is chosen to be Affinity for individual map analysis and Wavelet for multi-elemental fusion purposes. Following the methodology results in high-resolution EDS maps, even for uneven-surface particles which are, for the first time in literature, subjected to pansharpening.

Image based evaluation of textured 3DSEM models.

Photogrammetric methods enable the construction of 3D SEM models from 2D images. Most software for this purpose is designed for photographic images. The software tries to minimize modelling error but some uncertainty usually remains in the model. In such approaches no ground truth measurement for microscopic objects exists for comparison with the finished model. In the proposed method, a textured model surface is compared to the SEM images to map the error locations on the model. The method is illustrated using two datasets.