Comparing quality parameters obtained using destructive and optical methods in grading tomatoes

Abstract

Optical methods for analysing fruit quality have various advantages compared to conventional methods, including not destroying the sample and the possibility of automating the quality control process. The aim of this study was to compare artifi cial neural networks developed from biological activity indices obtained using the biospeckle laser optical technique, and from physico-chemical variables obtained by conventional destructive techniques, through an evaluation of their precision in classifying ripe tomatoes, using as a reference an earlier classifi cation carried out by visual inspection. A total of 150 tomatoes were used in the experiment, divided into three ripening stages. Multivariate principal component analysis was used to evaluate interaction of the variance within the groups of data obtained using the biospeckle laser technique and destructive laboratory methods. Two artifi cial neural networks were developed, the fi rst generated using biological activity indices as input vectors, and the second using physico-chemical variables. The precision of the two neural networks was compared using the Kappa index and overall accuracy, and was based on a reference classifi cation. The variation in ripening as a function of the biological activity indices was explained by the fi rst principal component. The neural network generated from the biological activity indices showed the best performance in classifying the tomatoes into the three ripening stages, with a signifi cant Kappa index and an overall accuracy of 67.5%.

Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes

“…In this context, the selection intensity [63,86,87], percentage of genetic progress [5,65,88] and the effectiveness of secondary traits [66,67,89] genetic approaches were applied to enhance the precision of identifying optimal predictor in segregating tomato populations. Previous researches consistently underscored the effective shared ability of such genetic methodologies to compare secondary traits, particularly in F2 populations [6,14,90,91].…”