The Compositional HJ-Biplot-A New Approach to Identifying the Links among Bioactive Compounds of Tomatoes.

Tomatoes have been described as a functional food because of their particular composition of different bioactive compounds. In this study, the proximate composition, minerals and trace elements, and antioxidant compounds were determined in two tomato cultivars (Mariana and Dunkan) that were grown in Gran Canaria (Spain) either conventionally or hydroponically. Although compositional data of this type require being subjected to the specific statistical techniques of compositional analysis, this approach has not usually been considered in this context. In the present case, a compositional Mann-Whitney U test of the data showed significant differences for each factor (cultivar and cultivation system) in several of the compositional variables studied. For the differences between cultivars, these parameters were the protein, Mg, lycopene, ascorbic acid, citric acid, and fumaric acid contents. For the differences between cultivation systems, they were mainly those of the mineral and trace elements group. Although one-year data are insufficient to make clear relationship among compounds because more repetitions in several localities and years are necessary, the compositional HJ-biplot (in which the links provide estimates of the linear relationship among variables) results agreed with other scientific results about linear relationship among some compounds analyzed.

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).

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.