Abstract

When synchronous fluorescence (SyF) spectroscopy is used for quantitative and qualitative analysis, selection of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is needed. Presented is a fusion approach to combine Δλ intervals thereby negating the selection process. This study uses the fusion of SyF spectra to detect adulteration of argan oil by corn oil and quantitative analysis of the corn oil content. The SyF spectra were acquired by varying the excitation wavelength in the region 300-800 nm using Δλ wavelength intervals from 10 to 100 nm in steps of 10 nm producing 10 sets of SyF spectra. For quantitative analysis, two calibration approaches are evaluated with these 10 SyF spectral datasets. Multivariate calibration by partial least squares (PLS) and a univariate calibration process where the SyF spectra are summed over respective SyF spectral ranges, the area under the curve (AUC) method. For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the 10 Δλ interval SyF spectral data sets. For this data set, the AUC method generally provides smaller prediction errors than PLS at individual Δλ intervals as well as with fusion of all 10 Δλ intervals.

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Fusion of Synchronous Fluorescence Spectra with Application to Argan Oil for Adulteration Analysis

When synchronous fluorescence (SyF) spectroscopy is used for quantitative and qualitative analysis, selection of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is needed. Presented is a fusion approach to combine Δλ intervals thereby negating the selection process. This study uses the fusion of SyF spectra to detect adulteration of argan oil by corn oil and quantitative analysis of the corn oil content. The SyF spectra were acquired by varying the excitation wavelength in the region 300-800 nm using Δλ wavelength intervals from 10 to 100 nm in steps of 10 nm producing 10 sets of SyF spectra. For quantitative analysis, two calibration approaches are evaluated with these 10 SyF spectral datasets. Multivariate calibration by partial least squares (PLS) and a univariate calibration process where the SyF spectra are summed over respective SyF spectral ranges, the area under the curve (AUC) method. For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the 10 Δλ interval SyF spectral data sets. For this data set, the AUC method generally provides smaller prediction errors than PLS at individual Δλ intervals as well as with fusion of all 10 Δλ intervals.