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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/160897
High-performance liquid chromatography with fluorescence detection fingerprinting combined with chemometrics for nut classification and the detection and quantitation of almond-based product adulterations
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Economically motivated food fraud has increased in recent years, with adulterations and substitutions of high-quality products being common practice. Moreover, this issue can affect food safety and pose a risk to human health by causing allergies through nut product adulterations. Therefore, in this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints were used for classification of ten types of nuts, using partial least squares regression-discriminant analysis (PLS-DA), as well as for the detection and quantitation of almond-based product (almond flour and almond custard cream) adulterations with hazelnut and peanut, using partial least squares regression (PLS). A satisfactory global nut classification was achieved with PLS-DA. Paired PLS-DA models of almonds in front of their adulterants were also evaluated, producing a classification rate of 100%. Moreover, PLS regression produced low prediction errors (below 6.1%) for the studied adulterant levels, with no significant matrix effect observed.
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CAMPMAJÓ GALVÁN, Guillem, et al. High-performance liquid chromatography with fluorescence detection fingerprinting combined with chemometrics for nut classification and the detection and quantitation of almond-based product adulterations. Food Control. 2020. Vol. 114, num. 107265. ISSN 0956-7135. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/160897