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Correlation structure between agroclimatic big data and EVOO fatty acid profile determined by GC and NMR spectra
Author(s) -
SánchezRodríguez María Isabel,
SánchezLópez Elena M.,
Marinas Alberto,
Caridad José M.,
Urbano Francisco J.
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3273
Subject(s) - polyunsaturated fatty acid , chemistry , fatty acid , partial least squares regression , food science , mathematics , chromatography , organic chemistry , statistics
Fatty acids are the major compounds in olive oils, and the determination of their profile is very useful in order for the authentication of the high quality of extra virgin olive oil (EVOO) and the evaluation of its purity and traceability. This work considers the estimations of saturated (SAFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids obtained by gas chromatography (GC), 13 C (carbonyl, C‐16, and aliphatic carbons regions), and 1 H NMR spectra. The aim of this paper is to analyze the correlation structure being between the abovementioned fatty acids estimations and some long‐run agroclimatic measurements (temperature, humidity, wind speed and direction, radiation, precipitation, and evapotranspiration), which were downloaded from the official website of the Andalusian Automatic Weather Stations (AWSs). The versatile graphical possibilities of the free software R‐project allow the design of computational programs providing interesting conclusions about the described correlations as a function of month, variety of olive, and protected designation of origin (PDO). The statistically significant correlations could determine relevant agroclimatic information to be included, in a further research, among the explanatory variables in regression models in order to improve the estimation of fatty acid profile of EVOOs, considered as a quality parameter in their authentication.