Ripening of plums, from South Africa (SA), was studied following reefer transportation. In experiment 1, plum cultivars ‘Pioneer’ and ‘African Rose’ were ripened using different temperature scenarios. Both cultivars showed increased coloration during ripening, but this was much more pronounced in ‘Pioneer’ compared to ‘African Rose’. In ‘Pioneer’ there was a clear decrease in firmness during ripening; this was correlated with the temperature sum. At a temperature sum of 100-120 degree-days, the fruit had soften completely and reached the “ready–to-eat” stage. Increasing the temperature sum did not further soften the fruit. Irrespective the temperature scenario, ‘African Rose’ showed no softening and did not become “ready-to-eat”. In experiment 2, plum cultivars ‘ So ngold’ and ‘Southern Belle’ were harvested three times during their production season, transported to the Netherlands (NL), and ripening was studied at three temperatures (16, 20 and 24°C for 2 days). ‘So ngold’ was always less firm at arrival compared to ‘Southern Belle’. The applied temperatures did not have a clear effect on the speed of ripening. Fruit firmness at arrival was similar for the different batches of each cultivar; fruit from later harvest batches showed slightly more softening during storage and shelf life than fruit from first harvestbatch. Ethylene treatment (100 ppm, 24h) had effect on firmness loss in ‘Southern Belle’ when plums were ripened at higher temperature (ethylene effect was not tested in ‘So ngold’). Ethylene production in ‘Songold’ was about ten times higher than in ‘Southern Belle’ and increased during ripening in both cultivars. No clear effect of ripening temperature on ethylene production was observed.

Food quality is influenced by abiotic conditions such as: temperature, relative humidity, gasses (oxygen, carbon dioxide, etc.). These were monitored in experiments with strawberry and avocado by Smart-r-tag sensors manufactured by SensaData and provided to WUR. The data from the sensors were used as input for prediction of fruit quality and shelf life with a quality loss model. The objective of this research is to test if SensaDatas sensor tags are able to capture abiotic conditions as input for quality prediction for strawberry and avocado. The study did not include developing new quality models based on the acquired data. Models described in literature and developed by WUR are used for quality prediction. The sensors used in the study are the Smart-r-tag Ver1, capturing temperature and relative humidity information and the Smart-r-tag Ver2, recording temperature, relative humidity, oxygen - and carbon dioxide concentration. In two experiments, one with strawberry and one with avocado, the quality of the produce was evaluated and the abiotic storage conditions were monitored using the Smart-r-tags. During storage the strawberries showed different levels of decay depending on the storage temperature, especially the storage condition at 20 °C affected the fruit severely. Avocados stored at different temperatures showed different levels of firmness loss. During the periods in which temperature was high (22 °C and 18 °C) the decrease in firmness was the highest. The Smart-r-tags are able to measure and log the abiotic conditions (temperature, oxygen and carbon dioxide) in which the produce was stored. However, for relative humidity there are also some nonrealistic readings, readings above 100%. Furthermore concerning the monitoring of oxygen and carbon dioxide contents (inside modified atmosphere packaging), the carbon dioxide measurements are inaccurate when the actual carbon dioxide contents are higher than 10% and/or when relative humidity in the packaging headspace is saturated. For quality modelling purpose, the parameter temperature was used as input variable. This data was as input useful for quality prediction. The quality prediction did not exactly match the observed quality, as the quality models were not optimised for these specific produces and abiotic conditions. The models can be adapted or other models could be used to fit the data better. The following recommendations can be given based on the work that was performed: 1. Validate relative humidity sensors when measuring at high humidity. In supply chains with perishable product like fruit and vegetables humidity is commonly above 90% RH and often higher than 95 % RH. It is important that the sensors operate well in this RH range for them to be useful in practice. Certainly a humidity cannot be higher than 100% RH. 2. Validate if the carbon dioxide sensor is measuring the correct concentration when measuring under high humidity. We found a discrepancy between our reference and the output of the Smart-r-tag ver2 sensor. 3. Select and use a quality prediction model that fits the need and support the decision making of the intended customer for the tags. There are many models described in literature, but they serve a certain purpose. Generic models are relatively easy to use, but might be too general for the case on hand. This has to be evaluated in a follow-up project, with practical pilots. In a possible follow-up WUR is willing to assist SensaData with selecting and setting up the best quality prediction models in combination with the needs of the customer.

This report is the follow-up of the exploration study concerning the potential impact of post-harvest technology on protein supply chains with special focus on the effects of post-harvest conditions on protein retention in sugar beet leaves. The research was performed independently by researchers from Wageningen Food & Biobased Research and funded by the Ministry of Agriculture, Nature and Food Safety through DFI- R&D budget, within the strategic WUR-KB theme of Healthy and Safe Food. The study has been divided into two main experiments: in a first part the effects of temperature and storage time on protein retention and on RuBisCo stability in sugar beet leaves were investigated. In asecond experiment, the effect of big volume storage on temperature and RuBisCo retention was examined.In the first experiment, sugar beet leaves were stored at 1, 20, 30 and 45 °C and sampled after 0, 6, 24, 30, 48, 72 and 168 h. The protein content, measured absolutely with both BCA method and relatively on protein gel, was stable during the first part of the storage, i.e. the latent phase. Depending to storage temperature, a decline in total protein content and in RuBisCo stability was observed. Based on these results, it was concluded that sugar beet leave can be stored up to168 hours at 1 °C without major effects in RuBisCo degradation. When sugar beet leaves were stored at 20 °C, loss of RuBisCo was initiated after 72 h of storage, 48 h at 30 °C and between 24 and 30 h when leaves were subjected to a temperature of 45 °C.In the second experiment, the effects of storing whole sugar beet leaf material in a big box at two temperature conditions was investigated. Sugar beet leaves were piled up in a big box to a height of 120 cm and left for 68 hours at 5 °C for the first box and at outside temperature for 24 hours followed by 44 hours at 20 °C for the second box. Each box was divided in 5 layers. Within each layer,temperature and gas contents were monitored over the complete storage period. At the end of this period and based on the temperature recordings, sugar beet leaves located at layers 30 and 120 cm at5 °C and 0 cm, 30, 60, 90 and 120 cm at 20 °C were sampled for protein analysis. Protein extraction was done directly on both leaf material and juice extracted from 1 kg of raw leaf material. The first extraction method allowed comparison of protein content results to the first experiment and the second extraction method was comparable to standard extraction methods applied at industrial level.After 68 hours of storage (44 hours at 20 °C), a mild rise in temperature was observed at the centre of the box stored at 20 °C. The temperature recordings showed that the warmest spot was located at 60 cm depth (centre of the pile) and reached a maximum temperature of 31.9 °C for a short period oftime (5.5 hours above 30 °C). Concerning the total protein content and RuBisCo stability analysis on samples extracted from the sugar beet leaf material, no significant difference between the two storage conditions (5 °C and 20 °C) and the different layers in the boxes was found. These results were consistent with the first experiment; the storage duration and the temperature rise observed in the boxes matched with the latent phase identified in the first experiment. Based on the results, it is possible to conclude that sugar beet leaves can be stored for a period of 68 hours without major effects on the RuBisCo stability. Storage in a big box in a cold room set up at5 °C helped to avoid the rise in temperature. RuBisCo was stable when the temperature inside the box remained under 30 °C for the majority of the storage period (62.5 hours). Finally, predictive models based on dry matter and on non-destructive sensing technologies were investigated. The correlation between dry matter and protein content was judged poor and indicated that it is not possible to extrapolate the protein content of the sugar beet leaves on basis of their drymatter content. A model based on NIR-measurements was also built to non-destructively predict dry matter and total protein content of sugar beet leaves. A relatively good performance was achieved for the prediction of dry matter on basis of a 13-wavelength measurement. Unfortunately, the predictionof protein content was less sensitive when using a NIR measurement of 10 wavelengths. Further measurements are required to optimise this model improve its prediction score

A series of experiments was performed to gain more insight in the dynamics of ripening of avocado fruit as part of the GreenCHAINge Fruit & Vegetables program (GreenCHAINge). The main aim of work package 6 was to develop tools to better monitor the ripening of batches of fruit resulting in less variation and a higher percentage of fruit reaching the Ready to Eat (RTE) stage. In addition, it was investigated if the further ripening of RTE fruit could be slowed down to prolong the shelf life of ripe fruit.

Starchy, a Lebanese start up, has elaborated a starch-based coating to preserve and extend the quality of fresh fruit such as apples or pears. In order to get insight in the performance of the coating the company has been interested in testing its coating on an European apple variety. Subsequently Starchy has received financial support of Agrytech to set up a project with Wageningen Food & Biobased Research (WFBR). The goal of the project was to study the effects of the Starchy edible coating on shelf life of apples (Elstar). The research was conducted independently. Wageningen Food & Biobased Research (WFBR) has drawn an experiment set-up in order to test on a scientific basis the effect of such a new coating on apple quality. The starch-based coating was tested at two dilutions on four batches of apples and under two shelf life conditions to simulate a normal and a more challenging chain with more risk of dehydration. The present document reports the set-up and results observed on the coated and uncoated apples, as well as the main conclusions. Although the test was designed to find effects of the coating on fruit quality by for instance reducing humidity during shelf life (to be able to show protective effects on water loss) and using stored mature fruits (more sensitive to develop quality issues in the subsequent chain) the differences between the coated and uncoated apples are very limited or not present al all: - Apples coated with the thicker coating receipt remained statistically firmer than the apples coated with the most fluid coating or uncoated, but this effect was very limited (