Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/160897
Title: High-performance liquid chromatography with fluorescence detection fingerprinting combined with chemometrics for nut classification and the detection and quantitation of almond-based product adulterations
Author: Campmajó Galván, Guillem
Saez-Vigo, Ruben
Saurina, Javier
Núñez Burcio, Oscar
Keywords: Ametlles
Àcid oleic
Quimiometria
Inspecció dels aliments
Cromatografia de líquids d'alta resolució
Almond
Oleic acid
Chemometrics
Food inspection
High performance liquid chromatography
Issue Date: 18-Mar-2020
Publisher: Elsevier B.V.
Abstract: 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.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.foodcont.2020.107265
It is part of: Food Control, 2020, vol. 114, p. 107265
URI: http://hdl.handle.net/2445/160897
Related resource: https://doi.org/10.1016/j.foodcont.2020.107265
ISSN: 0956-7135
Appears in Collections:Articles publicats en revistes (Enginyeria Química i Química Analítica)

Files in This Item:
File Description SizeFormat 
699976.pdf1.53 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons