Abstract
There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit’s goodness between 44 and 100%. The lowest fits were for the cultivar classification, this low percentage suggests that other kind of chemical parameter should be used to identify tomato cultivars.
Highlights
Wild tomatoes are native from western South America
47.6 56.7 57.3 36.2 38.3 53.9 50.3 58.8 52.2 59.3 59.3 53.3 50.4 49.9 69.8 37.8 37.5 59.8 75.0 48.2 36.0 50.7 53.9 57.2 73.6 a Values used to select the first predictor for the General Linear Model (GLM)-Automatic Interaction Detection (AID) analysis. b Expressed as galic acid doi:10.1371/journal.pone.0128566.t001
The AID analysis can be used with other statistical techniques to complete the analysis and determine the relative importance of the different independent variables
Summary
Wild tomatoes are native from western South America. The generic status of wild tomatoes within the family of Solanaceae has been a matter of controversy since the eighteen century. Tomato is classified as Solanum lycopersicum cv Mill. There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics [1]. Tomato is one of the most widely consumed fresh vegetables in the industrialized world. It is widely used by the food industries as raw material for the production of purees, ketchup and other products. Tomato is the most common vegetable in the Mediterranean diet, a diet known to have health benefits, especially to avoid the development of chronic degenerative diseases [2]
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