Synchronous fluorescence spectroscopy (SFS) is used for quantitative analysis as well as for qualitative analysis, such as with classification methods. With SFS, determination of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is required. There are a multitude of Δλ intervals that can be evaluated and optimization of the best one is complex. Presented here is a fusion approach for combining Δλ intervals, thereby negating the need to perform the selection by a skilled operator. To demonstrate the feasibility of omitting selection of the best Δλ interval, adulterated argan oil samples are studied. Argan oil is made from the argan tree, endemic to southwestern Morocco, and is well-known for its cosmetic, pharmaceutical, and nutritional applications. It is considered a luxury product and exported from Morocco around the world. Consequently, detection of argan oil adulteration followed by quantitative analysis of the adulterant concentration is important. This study uses fusion of SFS spectra obtained at ten Δλ intervals to first detect adulteration of argan oil by corn oil and then determination of the corn oil content. For detection of adulteration, 15 one-class classification methods were used simultaneously over the ten Δλ sets of SFS spectra. For tuning parameter dependent classifiers such as Mahalanobis distance, non-optimized classifiers are used. Raw classification values are used, removing the need to set classifier-dependent threshold values, albeit, ultimately, a fusion decision rule is needed for classification. For quantitative analysis, two calibration approaches are evaluated with fusion of these ten Δλ SFS spectral data sets. One is multivariate calibration by partial least squares (PLS). The second approach is a univariate calibration process where the SFS spectra are summed over respective SFS spectral ranges, also known as the area under the curve (AUC). For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the ten Δλ interval SFS specific data sets. For this argan oil data set, the AUC method generally provides equivalent prediction errors to PLS.
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