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

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.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

“…The input data are added using a propagation function a single response is generated as the out value in output layer. The output value will be compared to the experimental value and the error made by the ANN can be estimated ( Suárez et al, 2015 ).…”

Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes

“…Accordingly, ANN look for a mathematical formulation in the training dataset used for model development to achieve the closest result to the expected value. The ANN technique is especially helpful for complicated problems involving numerous variables with restricted knowledge of the interactions between variables and their variation ( Suárez et al, 2015 ). Hence, considering the importance of predicting phenolic contents in agricultural wastes without expensive analyses, this study aims to evaluate and validate accuracy of the ANN technique to predict phenolics levels and composition in grapevine vegetative parts as waste material of pruning and other viticultural practices due to its capability of learning complex, non-linear relationships between the input and output.…”

Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits

“…The ANN approach is particularly useful for complicated issues involving several parameters with limited information on the interactions between variables and their variation [23]. Being able to predict the chemical composition of fruit juice without using expensive analyses is important for determining fruit quality in the fruit industry.…”