Enhancing Chemical Image Analysis: Utilizing the Successive Projection Algorithm for Unmixing.

Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum of chemically informative wavelengths. In this context, morphological information refers to the spatial structure, shape, texture, and distribution of elements within the image. Enhancing its already widespread application requires reducing the computational load of the voluminous hyperspectral images while unmixing signals from different chemical species with unknown spectral fingerprints.

Systematic reduction of hyperspectral images for high-throughput plastic characterization.

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information.

Multivariate Curve Resolution Slicing of Multiexponential Time-Resolved Spectroscopy Fluorescence Data.

Time-resolved fluorescence spectroscopy (TRFS), i.e., measurement of fluorescence decay curves for different excitation and/or emission wavelengths, provides specific and sensitive local information on molecules and on their environment.

Joint selection of essential pixels and essential variables across hyperspectral images.

An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs).

Second-order universal calibration.

Quantification and qualification of an analyte of interest in pharmaceutical tablets from different manufacturers/companies are a hard task because of the potential presence of various interfering molecules. Indeed, the composition of the tablets covers a wide range of interferents which can be even unknown. As a consequence, we propose to determine the concentration of an analyte of interest regardless of the interferents using the concept of universal calibration.

Essential Spectral Pixels for Multivariate Curve Resolution of Chemical Images.

We propose a methodology to select essential spectral pixels (ESPs) of chemical images. These pixels are on the outer envelope of the principal component scores of the data and can be identified by convex-hull computation. As ESPs carry all the linearly mixed spectral information, large hyperspectral images can be dramatically reduced before multivariate curve resolution (MCR) analysis.

Multi-excitation hyperspectral autofluorescence imaging for the exploration of biological samples.

Many plant tissues can be observed thanks to autofluorescence of their cell wall components. Hyperspectral autofluorescence imaging using confocal microscopy is a fast and efficient way of mapping fluorescent compounds in samples with a high spatial resolution. However a huge spectral overlap is observed between molecular species.

Effect of image processing constraints on the extent of rotational ambiguity in MCR-ALS of hyperspectral images.

Hyperspectral imaging is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity.

Strategy To Obtain Accurate Analytical Solutions in Second-Order Multivariate Calibration with Curve Resolution Methods.

A novel procedure is described for processing the second-order data matrices with multivariate curve resolution-alternating least-squares; while the data set is nontrilinear and severe profile overlapping occurs in the instrumental data modes. The area of feasible solutions can be reduced to a unique solution by including/considering the area correlation constraint, besides the traditional constraints (i.e.