The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy.

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances.

There is compelling evidence in the literature to support the application of Raman spectroscopy for analysis of bodily fluids in their native liquid state. Naturally, the strategies described in the literature for Raman spectroscopic analysis of liquid samples have advantages and disadvantages. Herein, recent advances in the analysis of plasma/serum in the liquid state are reviewed. The potential advantages of Raman analysis in the liquid form over the commonly employed infrared absorption analysis in the dried droplet form are initially highlighted. Improvements in measurement protocols based on inverted microscopic geometries, clinically adaptable substrates, data preprocessing and analysis and applications for routine monitoring of patient health as well as therapeutic administration are reviewed. These advances suggest that clinical translation of Raman spectroscopy for rapid biochemical analysis can be a reality. In the future, this method will prove to be highly beneficial to clinicians for rapid screening and monitoring of analytes and drugs in the biological fluids, and to the patients themselves, enabling early treatment, before the disease becomes symptomatic, allowing early recovery.

Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains.

Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization.

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.

Corrigendum: Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

[This corrects the article DOI: 10.3389/fchem.2022.818974.].

An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples.

In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image. Techniques for separating sample spectra from background spectra in hyperspectral images, and techniques to identify spectra affected by fringes are also provided.

Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data.

Spectral quality control is an important step in the analysis of infrared spectral data, however, often neglected in scientific literature. A frequently used quality test that was originally developed for infrared spectra of bacteria is provided by OPUS software from Bruker Optik GmbH. In this study, the OPUS quality test is applied to a large number of spectra of bacteria, yeasts and moulds and hyperspectral images of microorganisms. It is shown that the use of strict thresholds for parameters of the OPUS quality test leads to discarding too many spectra. A strategy for optimizing parameters thresholds of the OPUS quality test is provided and a novel approach for spectral quality testing based on extended multiplicative signal correction (EMSC) is suggested. For all the data sets considered in our study, the EMSC quality test is shown to be the best among different alternatives of OPUS quality test provided.

Extended Multiplicative Signal Correction for Infrared Microspectroscopy of Heterogeneous Samples with Cylindrical Domains.

Optical scattering corrections are invoked to computationally distinguish between scattering and absorption contributions to recorded data in infrared (IR) microscopy, with a goal to obtain an absorption spectrum that is relatively free of the effects of sample morphology. Here, we present a modification of the extended multiplicative signal correction (EMSC) approach that allows for spectral recovery from fibers and cylindrical domains in heterogeneous samples. The developed theoretical approach is based on exact Mie theory for infinite cylinders. Although rigorous Mie theory implies utilization of comprehensive and time-consuming calculations, we propose to change the workflow of the original EMSC algorithm to minimize extensive calculations for each recorded spectrum at each iteration step. This makes the modified EMSC approach practical for routine use. First, we tested our approach using synthetic data derived from a rigorous model of scattering from cylinders in an IR microscope. Second, we applied the approach to Fourier transform IR (FT-IR) microspectroscopy data recorded from filamentous fungal and cellulose samples with pronounced fiber-like shapes. While the corrected spectra show greatly reduced baseline offsets and consistency, strongly absorbing regions of the spectrum require further refinement. The modified EMSC algorithm broadly mitigates the effects of scattering, offering a practical approach to more consistent and accurate spectra from cylindrical objects or heterogeneous samples with cylindrical domains.

An open-source code for Mie extinction extended multiplicative signal correction for infrared microscopy spectra of cells and tissues.

Infrared spectroscopy of single cells and tissue is affected by Mie scattering. During recent years, several methods have been proposed for retrieving pure absorbance spectra from such measurements, while currently no user-friendly version of the state-of-the-art algorithm is available. In this work, an open-source code for correcting highly scatter-distorted absorbance spectra of cells and tissues is presented, as well as several improvements of the latest version of the Mie correction algorithm based on extended multiplicative signal correction (EMSC) published by Konevskikh et al. In order to test the stability of the code, a set of apparent absorbance spectra was simulated. To this purpose, pure absorbance spectra based on a Matrigel spectrum are simulated. Scattering contributions where obtained by mimicking the scattering features observed in a set of experimentally obtained spectra . It can be concluded that the algorithm is not depending strongly on the reference spectrum used for initializing the algorithm and retrieves well the underlying pure absorbance spectrum. The calculation time of the algorithm is considerably improved with respect to the resonant Mie scattering EMSC algorithm used by the community today.

An improved algorithm for fast resonant Mie scatter correction of infrared spectra of cells and tissues.

Mie scattering effects create serious problems for the interpretation of Fourier-transform infrared spectroscopy spectra of single cells and tissues. During recent years, different techniques were proposed to retrieve pure absorbance spectra from spectra with Mie distortions. Recently, we published an iterative algorithm for correcting Mie scattering in spectra of single cells and tissues, which we called "the fast resonant Mie scatter correction algorithm." The algorithm is based on extended multiplicative signal correction (EMSC) and employs a meta-model for a parameter range of refractive index and size parameters. In the present study, we suggest several improvements of the algorithm. We demonstrate that the improved algorithm reestablishes chemical features of the measured spectra, and show that it tends away from the reference spectrum employed in the EMSC. We suggest strategies for choosing parameter ranges and other model parameters such as the number of principal components of the meta-model and the number of iterations. We demonstrate that the suggested algorithm optimizes an error function of the refractive index in a forward Mie model. We suggest a stop criterion for the iterative algorithm based on the error function of the forward model.

Performance evaluation of preprocessing techniques utilizing expert information in multivariate calibration.

Partial Least Squares (PLS) regression is one of the most used methods for extracting chemical information from Near Infrared (NIR) spectroscopic measurements. The success of a PLS calibration relies largely on the representativeness of the calibration data set. This is not trivial, because not only the expected variation in the analyte of interest, but also the variation of other contributing factors (interferents) should be included in the calibration data. This also implies that changes in interferent concentrations not covered in the calibration step can deteriorate the prediction ability of the calibration model. Several researchers have suggested that PLS models can be robustified against changes in the interferent structure by incorporating expert knowledge in the preprocessing step with the aim to efficiently filter out the spectral influence of the spectral interferents. However, these methods have not yet been compared against each other. Therefore, in the present study, various preprocessing techniques exploiting expert knowledge were compared on two experimental data sets. In both data sets, the calibration and test set were designed to have a different interferent concentration range. The performance of these techniques was compared to that of preprocessing techniques which do not use any expert knowledge. Using expert knowledge was found to improve the prediction performance for both data sets. For data set-1, the prediction error improved nearly 32% when pure component spectra of the analyte and the interferents were used in the Extended Multiplicative Signal Correction framework. Similarly, for data set-2, nearly 63% improvement in the prediction error was observed when the interferent information was utilized in Spectral Interferent Subtraction preprocessing.

Profiling serologic biomarkers in cirrhotic patients via high-throughput Fourier transform infrared spectroscopy: toward a new diagnostic tool of hepatocellular carcinoma.

Identification of novel serum biomarkers of hepatocellular carcinoma (HCC) is needed for early-stage disease detection and to improve patients' survival. The aim of this study was to evaluate the potential of serum Fourier transform infrared (FTIR) spectroscopy for differentiating sera from cirrhotic patients with and without HCC. Serum samples were collected from 2 sets of patients: cirrhotic patients with HCC (n = 39) and without HCC (n = 40). The FTIR spectra (10 per sample) were acquired in the transmission mode, and data homogeneity was tested by cluster analysis to exclude outliers. After data preprocessing by extended multiplicative signal correction and principal component analysis, the Support Vector Machine (SVM) method was applied using a leave-one-out cross-validation algorithm to classify the spectra into 2 classes of cirrhotic patients with and without HCC. When SVM was applied to all spectra (n = 790), the sensitivity and the specificity for the diagnosis of HCC were, respectively, 82.02% and 82.5%. When applied to the subset of spectra excluding the outliers (n = 739), SVM classification led to a sensitivity and specificity of 87.18% and 85%, respectively. Using median spectra for each patient instead of all replicates, the sensitivity and specificity were 84.62% and 82.50%, respectively. The overall accuracy rate was 82%-86%. In conclusion, this study suggests that FTIR spectroscopy combined with advanced methods of pattern analysis shows potential for differentiating sera from cirrhotic patients with and without HCC.