Quantitative Spectroscopic Analysis of Heterogeneous Mixtures: The Correction of Multiplicative Effects Caused by Variations in Physical Properties of Samples

Abstract

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g., particle size and shape, sample packing, and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum and, hence, mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical components in the same sample mixture. On the basis of this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e., near-infrared total diffuse transmittance spectra of four-component suspension samples and near-infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology.

Comprehensive multiphase NMR: a promising technology to study plants in their native state

“…[5,6] High Resolution Magic Angle Spinning (HR-MAS) NMR (sometimes termed gel-phase NMR) can overcome some of the limitations encountered in the analysis of intact samples. For instance, HR-MAS [7] can be used to study intact samples in situ such that information on both the soluble or gel-like materials can be elucidated, which holds great potential for plant physiology research.…”

Quantitative determination of ametryn in river water using surface-enhanced Raman spectroscopy coupled with an advanced chemometric model

“…Another 27 samples prepared with water from the Xiangjiang River were partitioned into calibration set 2 (No. 1-6, 10-12, [16][17][18][22][23][24][25][26][27] and test set 2 (No. 7-9, [13][14][15][19][20][21].…”

“…The multiplicative parameter b k explicitly accounts for the multiplicative confounding effects on SERS intensities caused by changes in variables other than analytes' concentrations in the kth calibration samples, such as physical properties of the enhancing substrates, the intensity and alignment/focusing of laser excitation source; d k is a composite term that represents background interference(s) and the nonmultiplicative effects caused by variations in physical properties of the enhancing substrates on the kth sample. Since the concentration of the internal standard p-thiocresol c p − thiocresol,k was constant across samples, the multiplicative parameters b k (k = 1, 2, …, K) for K calibration samples in the above MEM SERS model can be estimated from their SERS spectra by the modified optical length estimation and correction (OPLEC m ) method [27]. Then two calibration models (one is between x k and b k , the other is between x k and b k ⋅ c ametryn,k ) can then be built by multivariate linear calibration methods such as partial least squares regression (PLS).…”

Rapid Detection of Surface Color of Shatian Pomelo Using Vis-NIR Spectrometry for the Identification of Maturity

“…For multiplicative effect correction, MOPLEC is a simple and effective approach preprocessing method with an improvement of robustness (Jin et al 2012;Wang et al 2011;Chen et al 2013).…”

“…The noise elimination should be considered as another important part for the preprocessing on the raw measured spectra. As for this, a useful data preprocessing method realizes the full potential for quantitative spectroscopic analysis of complex systems by using a strategy of duel calibration, which is called modified optical path length estimation and correction (MOPLEC) (Jin et al 2012;Wang et al 2011;Chen et al 2013). The MOPLEC method is expected to be used in the noise elimination for the spectra of Shatian pomelo samples.…”