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Postharvest 1-methylcyclopropene (1-MCP) applications are commercially used on 'Conference' pears to obtain an improved fruit quality after storage for up to 11 months. Treatment with 1-MCP may result in firmer and greener fruit at the end of storage. During subsequent shelf life, 1-MCP treated pears may show slower ripening, including a reduced rate of softening and a reduced production of aroma volatiles. The lower levels of aroma volatiles and consumer complaints of reduced flavour suggest that flavour is negatively affected by 1-MCP treatments, which has raised concern within in the Dutch fruit industry.In the present study, the effect of pre-storage 1-MCP treatment on post-storage ripening and flavour perception was studied. Untreated and 1-MCP-treated pears (325 nL L − 1 ) were stored for 8 months at -0.8 • C under controlled atmosphere conditions of 3 kPa O 2 and 0.6 kPa CO 2 according to commercially used protocols. At day 7 and 9 of the subsequent shelf life at 10 • C, 1-MCP-treated fruit showed decreased yellowing and ethylene production, whereas firmness was similar to that of untreated fruit. The production of aroma volatiles was significantly reduced in 1-MCP-treated fruit; this was especially observed for different acetate esters, ethanol and butanol. Despite the reduction in aroma volatiles, a consumer panel could not distinguish (in a Tetrad test) between samples from untreated and 1-MCP-treated fruit with similar firmness. This indicates that the important aroma volatiles, although reduced in abundance, were still above threshold levels and did not affect overall flavour perception. We conclude that 1-MCP does not affect flavour when pears within equal firmness classes are compared.

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000–1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390–1420 nm contributes most to the model’s final decision.

This study was conducted with the aim of providing the latest situation on banana genotypic diversity present in the market places, their cultivations, their market chain and trading facilities in Maluku Province, Indonesia. A survey method was used, in which different markets, farmers and government institutions were visited and interviewed. Seventeen genotypes of three different species and different genome and ploidy levels were found at the market places with two highly demanded genotypes, Pisang Raja Hitam and Pisang 40 Hari. The major suppliers of banana commodities in Ambon markets were Ceram, Ambon, Buru, Obi and Bacan Islands. Lack of knowledge in implementing proper cultural practices, lack of capital, lack of aid provided by government and several other obstacles have been the reasons for low banana production in Maluku Province. Lack of sufficient infrastructure for large scale cultivations, storage and transport, and the use of harmful chemicals in post-harvest handling were some of the factors potentially hindering international trading of banana products. However, there were development plans by the government, which possibly improve banana export situation in the future. As an initial study in terms of value chain analysis in the province, the study should be a reference for further studies of such.

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