Open AccessFunctional approach and agro‐climatic information to improve the estimation of olive oil fatty acid content from near‐infrared data
Author(s) -
SánchezRodríguez María Isabel,
SánchezLópez Elena M.,
Marinas Alberto,
Urbano Francisco José,
Caridad José M.
Publication year - 2020
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.1312
Subject(s) - principal component analysis , chemometrics , partial least squares regression , mathematics , mean squared error , food science , computer science , statistics , chemistry , machine learning
Abstract Extra virgin olive oil (EVOO) is very appreciated by its taste, flavor, and benefits for health, and so, it has a high price of commercialization. This fact makes it necessary to provide reliable and cost‐effective analytical procedures, such as near‐infrared (NIR) spectroscopy, to analyze its traceability and purity, in combination with chemometrics. Fatty acids profile of EVOO, considered as a quality parameter, is estimated, firstly, from NIR data and, secondly, by adding agro‐climatic information. NIR and agro‐climatic data sets are summarized by using principal component analysis (PCA) and treated by both scalar and functional approaches. The corresponding PCA and FPCA are progressively introduced in regression models, whose goodness of fit is evaluated by the dimensionless root‐mean‐square error. In general, SFAs, MUFAs, and PUFAs (and disaggregated fatty acids) estimations are improved by adding agro‐climatic besides NIR information (mainly, temperature or evapotranspiration) and considering a functional point of view for both NIR and agro‐climatic data.