Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds

Abstract

A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.

Application of Extended Multiplicative Signal Correction to Short-Wavelength near Infrared Spectra of Moisture in Marzipan

“…An alternative procedure proposed to correct for multiplicative light scattering, that is similar to MSC, is the inverted scatter correction (ISC), more recently is the Extended multiplicative signal correction (EMSC) is a modification of the standard MSC pre-processing method [4,5,[15][16][17][18] that allows the separation of physical light scattering effects from chemical (vibrational) light absorbance effects in spectra. It was developed by Martens and Stark [19,20] the methodology to identify and separate various effect in multi-chanel measurements making the measurements suitable lives it goes multivariate calibration, improving robustness and predictive ability [4,5,19]. This approach is able to estimate and separate multiplicative physical effects (path length, light scattering, sample thickness, etc.)…”

Bagging for robust non-linear multivariate calibration of spectroscopy

“…The first dataset is related to the transmittance spectra of wheat kernels [53]. In this dataset, 523 samples from three different locations were analyzed at 100 wavelengths in the range of 850-1050nm.…”

Bayesian linear regression and variable selection for spectroscopic calibration

“…The first application is to develop calibration models for near-infrared (NIR) transmittance spectra recorded for the analysis of wheat kernels [7]. The objective of the study was to determine the percentage protein concentration in the wheat kernels, based on the NIR spectra recorded at 100 wavelengths across the region 850-1050 nm.…”

“…Furthermore, the test samples were stored for additional two months prior to analysis to evaluate the temporal effect on the samples and instruments. Further details about the data set can be found in [7]. The data set is publicly available from http://www.models.kvl.dk/ research/data/wheat_kernels/index.asp.…”

“…These research have materialized in both enhanced pre-processing algorithms (such as extended multiplicative signal correction [6], extended inverted signal correction [7] and direct orthogonal signal correction [8]) and improved regression models (including locally weighted regression [9], neural networks [9,10], support vector machine [11] and Gaussian process [12]). This paper is mainly focused on the development of advanced regression models for calibration, and thus the pre-processing techniques are not discussed further.…”