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

Solheim, Johanne Heitmann; Zimmermann, Boris; Tafintseva, Valeria; Dzurendová, Simona; Shapaval, Volha; Köhler, Achim

doi:10.3390/molecules27061900

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…Blazhko et al [ 43 ] proposed EMSA by improving the extended multiplicative signal correction (EMSC) [ 58 ] algorithm, and demonstrated that EMSA-augmented spectral data can effectively improve classification accuracy. EMSC processing of spectral data allows correcting for the effects of various physical and instrumental distortions, such as background and sample measurements of light source variations (baseline shifts), sample thickness variations (multiplicative effects), and instrumental scattering (spectral tilt) [ 59 ]. The basic model is as follows [ 58 ]:

where

is the measured spectrum;

is the reference spectrum, usually the mean of all spectra;

is the parameter associated with the baseline, multiplicative, linear and higher polynomials respectively; and

is the residual.…”

Section: Methodsmentioning

confidence: 99%

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Jiang

Zhao

Ding

et al. 2023

Sensors

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Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.

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where

is the measured spectrum;

is the reference spectrum, usually the mean of all spectra;

is the parameter associated with the baseline, multiplicative, linear and higher polynomials respectively; and

is the residual.…”

Section: Methodsmentioning

confidence: 99%

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Jiang

Zhao

Ding

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Although molecular spectra are influenced by inter-molecular interactions , and environmental conditions (e.g., temperature and pH-value), several studies suggest that complex blood spectra can be described by linear superposition of individual single component spectra. For infrared spectroscopy, it is known that the strength of a molecular spectrum scales linearly with concentration over many orders of magnitude. , Furthermore, it has been shown that the concentration of several different molecules in blood can be determined by linear regression. − Moreover, it was shown that a linear combination of the spectra of the most abundant molecular species can be used to describe experimentally observed differences in the spectra of blood serum from a healthy cohort and from patients with lung cancer …”

Section: Methodsmentioning

confidence: 99%

“…For infrared spectroscopy, it is known that the strength of a molecular spectrum scales linearly with concentration over many orders of magnitude. 3 , 28 Furthermore, it has been shown that the concentration of several different molecules in blood can be determined by linear regression. 29 − 32 Moreover, it was shown that a linear combination of the spectra of the most abundant molecular species can be used to describe experimentally observed differences in the spectra of blood serum from a healthy cohort and from patients with lung cancer.…”

Section: Methodsmentioning

confidence: 99%

“…Other sources of error in infrared spectroscopy which result from baseline drift 3 or sample delivery and preparation 10 , 14 can also be considered by introducing multiplicative noise and drift vectors in the model. 28 When transferring this model to Raman-based fingerprinting applications, it may be necessary to consider further, partly nonlinear noise sources 34 to obtain realistic results. As we show below, for the application of the model presented in this work, it is sufficient to consider only additive white noise to obtain realistic results.…”

Section: Methodsmentioning

confidence: 99%

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Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through In Silico Modeling

et al. 2023

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Molecular fingerprinting via vibrational spectroscopy characterizes the chemical composition of molecularly complex media which enables the classification of phenotypes associated with biological systems. However, the interplay between factors such as biological variability, measurement noise, chemical complexity, and cohort size makes it challenging to investigate their impact on how the classification performs. Considering these factors, we developed an in silico model which generates realistic, but configurable, molecular fingerprints. Using experimental blood-based infrared spectra from two cancer-detection applications, we validated the model and subsequently adjusted model parameters to simulate diverse experimental settings, thereby yielding insights into the framework of molecular fingerprinting. Intriguingly, the model revealed substantial improvements in classifying clinically relevant phenotypes when the biological variability was reduced from a between-person to a within-person level and when the chemical complexity of the spectra was reduced. These findings quantitively demonstrate the potential benefits of personalized molecular fingerprinting and biochemical fractionation for applications in health diagnostics.

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“…22 Extended multiplicative signal correction (EMSC) is used for eliminating uncontrollable path length or scattering effects. 23…”

Section: Methodsmentioning

confidence: 99%

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization

et al. 2023

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

Cited by 10 publications

References 29 publications

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through In Silico Modeling

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization

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