UNASSIGNED: There are a large number of @entity523 cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of @entity523 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 ( @entity11 ) 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 @entity11 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 @entity523 samples from Tenerife (Spain). Each @entity523 sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to @entity11 ( @entity11 ) tree-structured was organized into 3 levels according to the number of factors. @entity25371 was the compound the allowed to distinguish the @entity523 samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the @entity523 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 @entity523 cultivars.

Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection ( XXXX ) and Artificial Neural Network Models (ANNs).

@entity11 :: ('MESH:D005910', 'Disease') :: ['AID', 'GLM-AID'] @entity523 :: ('4081', 'Species') :: ['tomato'] @entity25371 :: ('MESH:C032171', 'Chemical') :: ['p-Coumaric acid']
@entity11 :: (MESH:D005910,Disease) :: ['GLM-AID']
