Shelf Life of Extra Virgin Olive Oil and Its Prediction Models

Table 10

Summary of statistical analysis performed by Rodrigues et al. (2017).

Objective	Statistical	Statistical analysis ref.
Compare the impact of dark/light storage conditions on olive oils for each storage time	Student’s -test	[48]
Assess the effect of the storage time on olive oils stored in dark/light	One-way ANOVA, Tukey’s post hoc multicomparison test	[48]
Evaluate the existence of bivariate correlations within the olive oil’s physicochemical parameters	Linear Pearson correlation coefficient (-Pearson)	[48]
Test the capability of the E-tongue to correctly classify the EVOO based on storage time or storage conditions as a supervised pattern recognition method	Linear discriminant analysis (LDA)	[49, 50]
Evaluate the qualitative classification capability of physicochemical and sensory data	LDA	[49, 50]
Select the best subsets of independent predictors among 40 E-tongue potentiometric signals	Metaheuristic simulated annealing (SA) variable selection algorithm	[51–53]
Compare the current and the new subsets of (⊆K) variables	Tau2 quality criterion	[51]
Evaluate the LDA classification models	Leave-one-out cross-validation (LOO-CV)	[54, 55]
Minimize the risk of overfitting from LOO-CV when sample size is large and generate a flat distribution of data for regression model development	24 olive oil samples used as “training set” for LOO-CV	[54, 55]
	12 olive oil samples used as “testing set” using Kennard-Stone algorithm	[56]

statistical analyses were performed using Subselect [51, 57] and MASS [58] packages of the statistical program R (version 2.15.1) at a significant level of 5%.