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Ten traditional M1 (n=5) and M2 (n=5), and five stabilised M3 (n=5) retail soft cheeses, with different manufacturing process were studied using visible (VIS) and near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopies. VIS-NIR, MIR, tryptophan, riboflavin) and vitamin A spectra were recorded for two sampling zones (external (E) and central (C)) of the investigated cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectral region, the classification was not satisfactory. A slightly better classification was obtained from the VIS-NIR spectra. Better classifications were obtained using vitamin A fluorescence spectra, since 91.8% and 80.6% of the calibration and validation spectra, respectively, were correctly classified. The first 5 principal components (PCs) of the principal component analysis (PCA) extracted from each data (VIS-NIR, MIR, tryptophan, riboflavin and vitamin A fluorescence spectra, and physicochemical data) were pooled into a single matrix and analysed by FDA. The classification was considerably improved, obtaining CC of 100% of the calibration and 88.9% of the validation spectra. The discrimination of the investigated cheeses according to their manufacturing processes and their sampling zones was excellent. It was concluded that concatenation of the physico-chemical and spectroscopic information is an efficient technique for the identification of soft cheese varieties.