Multiplicative Signal Correction Research Articles

Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction, first derivative, second derivative, and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination, competitive adaptive reweighted sampling, and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

Prediction of the Harvest Time of Cabernet Sauvignon Grapes Using Near-Infrared Spectroscopy

Harvest time assessment during the grape-ripening process can provide meaningful information for vineyard harvest scheduling. The purpose of this study was to investigate the identification of the harvest time of grape clusters using near-infrared (NIR) spectroscopy. During the harvest season from September to October 2019, bunches of Cabernet Sauvignon grapes were examined. Before establishing two classification models, namely partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) models, raw spectra were processed by different pre-processing methods, including multiplicative signal correction (MSC), mean-centering, the standard normal variable (SNV), and the Savitzky-Golay method. Competitive adaptive weighted sampling (CARS) and the successive projections algorithm (SPA) were employed to select the optimal wavenumbers. The results indicate that NIR spectroscopy is a potentially promising approach for the rapid identification of different harvest times of Cabernet Sauvignon grapes, and the proposed technique is helpful for the prediction of ripened and over-ripened Cabernet Sauvignon grapes during the harvest time.

Fusion Recalibration Method for Addressing Multiplicative and Additive Effects and Peak Shifts in Analytical Chemistry

Analytical chemistry relies on the qualitative and quantitative analysis of multivariate data obtained from various measurement techniques. However, artifacts such as missing data, noise, multiplicative and additive effects, and peak shifts can adversely affect the accuracy of chemical measurements. To ensure the validity and accuracy of results, it is crucial to preprocess the data and correct for these artifacts. This paper proposes a fusion recalibration algorithm, called Spectral Offset Recalibration (SOR), that combines the Extended Multiplicative Signal Correction (EMSC) and Correlation-Optimized Warping (COW) algorithms to address both multiplicative and additive effects and peak shifts. The algorithm incorporates prior spectroscopic knowledge to down-weight or disregard spectral regions with strong absorption or significant distortion caused by peak alignment algorithms. Experimental validation on wood NIR datasets and simulated datasets demonstrates the effectiveness of the proposed method. The fusion recalibration approach offers a comprehensive solution for accurate analyses and predictions in analytical chemistry by mitigating the impact of artifacts.

Longitudinal Study of Cirrhosis Development in STAM and carbon tetrachloride Mouse Models Using Fourier Transform Infrared Spectral Imaging

Animal models of cirrhosis are of great interest to investigate the pathological process leading to the final stage of cirrhosis. The aim of this study was to analyze the different steps involved in the progressive development of cirrhosis using Fourier transform infrared spectral histology in 2 mouse models of cirrhosis, the STAM model of metabolic cirrhosis, and the carbon tetrachloride–induced cirrhosis model. Formalin-fixed, paraffin-embedded liver samples were obtained from 3 mice at 5 time points in each model to analyze the course of hepatic lesions up to the formation of cirrhosis. For each time point, adjacent 3-μm–thick liver sections were obtained for histologic stains and spectral histology. Fourier transform infrared acquisitions of liver sections were performed at projected pixel sizes of 25 μm × 25 μm and 6.25 μm × 6.25 μm. Spectral images were then preprocessed with an extended multiplicative signal correction and analyzed with common k-means clustering, including all stages in each model. In both models, the 2- and 4-class common k-means clustering in the 1000 to 1350 cm−1 range showed that spectral classes characterized by higher absorbance peaks of glycogen were predominant at baseline, then decreased markedly in early stages of hepatic damage, and almost disappeared in cirrhotic tissues. Concomitantly, spectral classes characterized by higher absorbance peaks of nucleic acids became progressively predominant during the course of hepatic lesions. These results were confirmed using k-means clustering on the peaks of interest identified for glycogen and nucleic acid content. Our study showed that the glycogen depletion previously described at the stage of cirrhosis is an early event in the pathological process, independently of the cause of cirrhosis. In addition, there was a progressive increase in the nucleic acid content, which may be linked to increased proliferation and polyploidy in response to cellular lesions.

Avaliação da espectroscopia no infravermelho próximo para previsão da composição químico-bromatológica da silagem de sorgo

The objective was to evaluate the adequacy of near-infrared spectroscopy for predicting the chemical-bromatological composition of sorghum BRS 658 and BRS Ponta Negra silage, in PVC mini-silos, ensiled at grain maturation stages E7 or milky, E8 or pasty and hard mealy. The model using the Savitzky-Golay 1st Derivative for DIVMS showed lower RMSEC, RMSECV and RMSEP (5.96, 7.42, 6.96) respectively. For DIVMO, the Multiplicative Signal Correction (MSC) + Savitzky-Golay 1st Derivative model with two windows showed better performance, with higher R2 in calibration, validation and prediction (0.82, 0.72, 0.56) respectively. In the independent validation of the models, no significant difference was observed between the predicted and reference values for the BP, IVMSD and IVMOD variables. The NIRS methodology is capable of accurately predicting the protein content, in vitro digestibility of dry matter and in vitro digestibility of organic matter in sorghum silage.

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications.

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy.

Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.

Piecewise preprocessing of near-infrared spectra for improving prediction ability of a PLS model

In this study, a new strategy for preprocessing of near-infrared spectra, piecewise preprocessing (PP) is proposed. Unlike routine in optimization of preprocessing methods, in PP a spectrum is split into a number of intervals alone wavelength and the optimization of preprocessing method is independently implemented to each interval, that means that different intervals in the spectrum may select different preprocessing methods. And genetic algorithm (GA) is used in the optimization of preprocessing methods or their combinations on each wavelength interval. This strategy was tested with three near infrared (NIR) spectra datasets. Some common spectral preprocessing algorithms, such as Standard Normal Variate (SNV), multiplicative signal correction (MSC), Savitzky-Golay smoothing (smooth), first Savitzky–Golay derivative (1D), second Savitzky–Golay derivative (2D), and their combinations are used. The performance of PP was compared with the traditional method selection strategies. The results show that the proposed strategy PP is very effective in improving prediction ability of PLS models built with the pretreated spectra.

Deep learning–based turbidity compensation for ultraviolet-visible spectrum correction in monitoring water parameters

Ultraviolet-visible spectroscopy is an effective tool for reagent-free qualitative analysis and quantitative detection of water parameters. Suspended particles in water cause turbidity that interferes with the ultraviolet-visible spectrum and ultimately affects the accuracy of water parameter calculations. This paper proposes a deep learning method to compensate for turbidity interference and obtain water parameters using a partial least squares regression approach. Compared with orthogonal signal correction and extended multiplicative signal correction methods, the deep learning method specifically utilizes an accurate one-dimensional U-shape neural network (1D U-Net) and represents the first method enabling turbidity compensation in sampling real river water of agricultural catchments. After turbidity compensation, the R2 between the predicted and true values increased from 0.918 to 0.965, and the RMSE (Root Mean Square Error) value decreased from 0.526 to 0.343 mg. Experimental analyses showed that the 1D U-Net is suitable for turbidity compensation and provides accurate results.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error () was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

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.

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.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.

Detection of adulterated coffee by fourier-transform infrared (FTIR) spectroscopy associated with sensory analysis

Because of its huge economic value, coffee has been the target of adulteration worldwide. Given the successful application of spectroscopic methods in detecting adulterants, this study aimed to employ attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) to detect adulterants in roasted coffee samples and compare the results with that of sensory analysis. In this study, twelve coffee samples were intentionally adulterated with varying concentrations, i.e., 10%, 30%, and 50%, of corn, beans, sawdust, and coffee husk. These adulterated samples were compared with one unadulterated coffee sample and four roasted and ground commercially available coffee samples; spectral readings of caffeine and chlorogenic acid (CGA) standards were performed for reference. The sensory analysis was performed by 17 tasters who were trained by a Q-grader. The infrared (IR) spectra (FTIR) data were processed by multiplicative signal correction (MSC) and subjected to a principal component analysis (PCA), along with the results of the sensory analysis. The combination of sensory analysis and IR spectrum allowed to differentiate samples of adulterated coffee and unadulterated coffee by PCA, with an explanation of 79% variance. The results demonstrated that the wavenumbers associated with CGA and caffeine contribute significantly in distinguishing adulterated coffee samples.Key words: Adulterants; coffee quality; chemometric; sensory analysis.

The quantitative detection of botanical trashes contained in seed cotton with near infrared spectroscopy method

This study is performed to investigate the potential of near infrared (NIR) spectroscopy for the detection of botanical trashes content of seed cotton harvested by cotton-picker (SCHCP). Large quantity of trashes become comingled with cotton fiber in the harvesting process, especially when the cotton is harvested with cotton-picker. In China, trashes content of seed cotton (SC) has to be detected when farmers sell the SC to ginneries because trashes reduce the prices of SC and it should be deducted from the whole weight. The conventional instrumental method used to detect the trashes content of SC, ginning and trashes analysis, is complex and time consuming. In this study, 353 SC samples were collected from three ginneries, the NIR spectra bands from 12,000 to 4000 cm−1 were collected with the FT-NIR spectrometer Nexus. Models between NIR spectra and the trashes contents of these SC samples have been developed with the method of partial least square regression (PLSR), bands of 12,000–4000 cm−1, multiplicative signal correction (MSC) was used to eliminate the negative effects caused by sample shapes, second derivative spectra were used to eliminate the translation and the rotation in the spectral baseline. And the parameters of optimized model: R2 is up to 0.985 (calibration set) and 0.973 (prediction set), RMSEC is as low as 0.072 g and RMSEP is 0.158 g. Results of ANOVA also certified the trashes contents calculated with the models are consistent with the actual trashes contents.

Influence of data pre-processing techniques for plsr model to predict blood glucose by nir spectroscopy-=SUP=-*-=/SUP=-

NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords:

Influence of data pre-processing techniques for PLSR model to predict blood glucose by NIR spectroscopy

NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords: multiplicative scatter correction (MSC), orthogonal signal correction (OSC), standard normal variate (SNV), Diffuse Reflectance Spectrometer (DRS).

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.

Correcting replicate variation in spectroscopic data by machine learning and model-based pre-processing

In this study we present a pre-processing and an augmentation approach based on Extended Multiplicative Signal Correction (EMSC) for removing and modelling replicate variation in spectroscopic data. The EMSC replicate correction method estimates replicate variation from replicate samples and integrates the estimated variation into the EMSC model. In the field of deep learning, augmentation is a frequently applied approach to deal with variability in images. In this study, we suggest augmentation of vibrational spectroscopic data with replicate variation. Replicate correction and replicate augmentation can be considered as two inverse procedures which are compared in our study. Three data sets of Fourier Transform Infrared spectra of yeasts, filamentous fungi and bacteria were used in this study to compare classification performance on genus and species level by (1) Random Forest using replicate corrected spectra vs. (2) Deep Learning using augmented spectra. Both technical and biological replicate correction/augmentation were considered.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.