Estimation of Soluble Solids for Stone Fruit Varieties Based on Near-Infrared Spectra Using Machine Learning Techniques.

The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS−NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS−NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of 98.9% was obtained for the CNN classification model and a correlation coefficient of Rc>0.7109 for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.

Identification and Characterization of Diaporthe ambigua, D. australafricana, D. novem, and D. rudis Causing a Postharvest Fruit Rot in Kiwifruit.

Diaporthe spp. are important plant pathogens causing wood cankers, blight, dieback, and fruit rot in a wide range of hosts. During surveys conducted during the 2013 and 2014 seasons, a postharvest rot in Hayward kiwifruit (Actinidia deliciosa) was observed in Chile. In order to identify the species of Diaporthe associated with this fruit rot, symptomatic fruit were collected from seven kiwifruit packinghouses located between San Francisco de Mostazal and Curicó (central Chile). Twenty-four isolates of Diaporthe spp. were identified from infected fruit based on morphological and cultural characters and analyses of nucleotides sequences of three loci, including the internal transcribed spacer (ITS) region (ITS1-5.8S-ITS2), a partial sequences of the ß-tubulin, and translation elongation factor 1-α genes. The Diaporthe spp. identified were Diaporthe ambigua, D. australafricana, D. novem, and D. rudis. Multilocus phylogenetic analysis revealed that Chilean isolates were grouped in separate clades with their correspondent ex-types species. All species of Diaporthe were pathogenic on wounded kiwifruit after 30 days at 0°C under normal and controlled-atmosphere (2% O2 and 5% CO2) storage and they were sensitive to benomyl, pyraclostrobin, and tebuconazole fungicides. D. ambigua isolates were the most virulent based on the lesion length measured in inoculated Hayward and Jintao kiwifruit. These findings confirm D. ambigua, D. australafricana, D. novem, and D. rudis as the causal agents of kiwifruit rot during cold storage in Chile. The specie D. actinidiae, a common of Diaporthe sp. found associated with kiwifruit rot, was not identified in the present study.