Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy

Martens, Harald; Stark, Edward

doi:10.1016/0731-7085(91)80188-f

Cited by 441 publications

(353 citation statements)

References 2 publications

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“…Each spectrum zi (where i = 1,…, N; N is number of spectra in an image) was pre-processed by EMSC (Martens and Stark 1991). EMSC estimates and corrects baseline effects and multiplicative effects using the model…”

Section: Discussionmentioning

confidence: 99%

Vibrational microspectroscopy enables chemical characterization of single pollen grains as well as comparative analysis of plant species based on pollen ultrastructure

Zimmermann

Bağcıoğlu

Sandt

et al. 2015

Planta

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Main conclusion: Chemical imaging of pollen by vibrational microspectroscopy enables characterization of pollen ultrastructure, in particular phenylpropanoid components in grain wall for comparative study of extant and extinct plant species.Keywords: FTIR microspectroscopy, Raman microspectroscopy, Pinales, imaging, cell wall. 2 SUMMARYA detailed characterization of conifer (Pinales) pollen by vibrational microspectroscopy is presented. The main problems that arise during vibrational measurements were scatter and saturation issues in Fourier transform infrared (FTIR), and fluorescence and penetration depth issues in Raman. Single pollen grains larger than approx. 15 µm can be measured by FTIR microspectroscopy using conventional light sources, while smaller grains may be measured by employing synchrotron light sources. Pollen grains that were larger than 50 µm were too thick for FTIR imaging since the grain constituents absorbed almost all infrared light. Chemical images of pollen were obtained on sectioned samples, unveiling the distribution and concentration of proteins, carbohydrates, sporopollenins and lipids within pollen substructures. The comparative analysis of pollen species revealed that, compared with other Pinales pollens, Cedrus atlantica has a higher relative amount of lipid nutrients, as well as different chemical composition of grain wall sporopollenin. The pre-processing and data analysis, namely extended multiplicative signal correction and principal component analysis, offer simple estimate of imaging spectral data and indirect estimation of physical properties of pollen. The vibrational microspectroscopy study demonstrates that detailed chemical characterization of pollen can be obtained by measurement of an individual grain and pollen ultrastructure. Measurement of phenylpropanoid components in pollen grain wall could be used, not only for the reconstruction of past environments, but for assessment of diversity of plant species as well. Therefore, analysis of extant and extinct pollen species by vibrational spectroscopies is suggested as a valuable tool in biology, ecology and palaeosciences.3

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Section: Discussionmentioning

confidence: 99%

Vibrational microspectroscopy enables chemical characterization of single pollen grains as well as comparative analysis of plant species based on pollen ultrastructure

Zimmermann

Bağcıoğlu

Sandt

et al. 2015

Planta

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“…If those samples were deleted, one must be aware that there may be future samples similar to the deleted outliers, and the new samples will be outliers again -basically without any reason. There are targeted correction methods like EMSC [23] that can help to eliminate such known and unwanted effects, but the aim of the present study was out of this scope.…”

Section: Outlier Samplesmentioning

confidence: 99%

Multicomponent blood lipid analysis by means of near infrared spectroscopy, in geese

Bázár

Éles

Kovács

et al. 2016

Talanta

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a b s t r a c tThis study provides accurate near infrared (NIR) spectroscopic models on some laboratory determined clinicochemical parameters (i.e. total lipid (5.57 71.95 g/l), triglyceride (2.59 71.36 mmol/l), total cholesterol (3.81 7 0.68 mmol/l), high density lipoprotein (HDL) cholesterol (2.45 70.58 mmol/l)) of blood serum samples of fattened geese. To increase the performance of multivariate chemometrics, samples significantly deviating from the regression models implying laboratory error were excluded from the final calibration datasets. Reference data of excluded samples having outlier spectra in principal component analysis were not marked as false. Samples deviating from the regression models but having non outlier spectra in PCA were identified as having false reference constituent values. Based on the NIR selection methods, 5% of the reference measurement data were rated as doubtful. The achieved models reached R 2 of 0.864, 0.966, 0.850, 0.793, and RMSE of 0.639 g/l, 0.232 mmol/l, 0.210 mmol/l, 0.241 mmol/l for total lipid, triglyceride, total cholesterol and HDL cholesterol, respectively, during independent validation. Classical analytical techniques focus on single constituents and often require chemicals, timeconsuming measurements, and experienced technicians. NIR technique provides a quick, cost effective, non-hazardous alternative method for analysis of several constituents based on one single spectrum of each sample, and it also offers the possibility for looking at the laboratory reference data critically. Evaluation of reference data to identify and exclude falsely analyzed samples can provide warning feedback to the reference laboratory, especially in the case of analyses where laboratory methods are not perfectly suited to the subjected material and there is an increased chance of laboratory error.

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“…The preprocessing methods are intended to enhance the spatialspectral data without changing its original space. Common preprocessing methods for spectral analysis are Multplicative Scatter Correction (MSC) and its variants [12], Standard Normal Variate (SNV) and De-Trending (DT) [13]. Feature extraction methods are used to form the spatial-spectral data to emphasis its characteristics for better prediction of the targeted classes.…”

Section: Data Handlingmentioning

confidence: 99%

Honey dataset standard using hyperspectral imaging for machine learning problems

Noviyanto

Abdullah

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Hyperspectral imaging has been rarely investigated for honey analyses, on the contrary to the optical spectroscopy which is widely investigated. The essential missing component to kick start this research is a standard honey hyperspectral images, called hypercubes, dataset. This paper proposes a systematic procedure for the preparation of the first honey hypercube dataset using hyperspectral imaging. Moreover, a scalable and flexible dataset module is introduced to ease the interaction between raw hypercube data and machine learning software. The developed dataset greatly benefits researchers to progress on the research of honey analysis including constituents prediction and types classification using hyperspectral imaging and machine learning.

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Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy

Cited by 441 publications

References 2 publications

Vibrational microspectroscopy enables chemical characterization of single pollen grains as well as comparative analysis of plant species based on pollen ultrastructure

Vibrational microspectroscopy enables chemical characterization of single pollen grains as well as comparative analysis of plant species based on pollen ultrastructure

Multicomponent blood lipid analysis by means of near infrared spectroscopy, in geese

Honey dataset standard using hyperspectral imaging for machine learning problems

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