Measuring and predicting mango quality from harvest at Brazil till RTE stage in the Netherlands : GreenCHAINge WP1 – Mango

The general objective in GreenCHAINge Work package 1, is to develop a more generic quality control system for the AH supply chain that will improve the assurances for consistent quality. One of the subprojects is the study of mangoes, being one of the exotic products delivered to Albert Heijn and serving as a model for other exotic products with the AH fresh food logistics. Mangoes produced in Brazil are transported in reefer containers to the Netherlands. To obtain uniform and RTE (Ready to Eat) mangoes on the shelf in supermarkets, it is essential to:• Harvest mangoes at an optimal maturity stage• Transport mangoes at optimal conditions• Ripe mangoes at optimal temperature and time• Deliver uniform and RTE mangoes at the right momentThe aim of this study is to predict mango quality based on several destructive and nondestructive measurements. This will enable Albert Heijn, Bakker Barendrecht, MAERSK LINE and VEZET to define optimal harvest, transport and ripening conditions, and to select the best raw material for the processing of cut fruit salads. The results of this study are promising:• Measuring firmness at different moments in the mango supply chain enabled us to develop a model to predict firmness in a future stage• Quality measurements over time allow prediction of RTE stage to a certain extent • Quality characteristics like internal color and internal defects are measured using “classical subjective phenotyping” as well as using “novel objective phenotyping” methods. Measuring in an objective way reduces variation due to human error and allows standardization of measurements in a continuous scale, throughout the whole world wide supply chain• Non-destructive measurements of firmness and NIR (Near-infrared) spectra correlate to quality, the capture of NIR spectra in a value might enable the use of each NIR spectrum as a marker to track maturity• Volatile esters may be used as non-destructive biomarkers to detect ripe mangoes • Quality measurements over time allow acquirement of suitable raw material for making cut fruit salads• Precooling has a positive effect on quality of mangoes, while transport to the harbour with or without genset has no significant effectAccurate prediction of quality allows sorting of mangoes during the chain to finally deliver uniform and RTE mangoes to the supermarkets. To allow proper sorting of mangoes, further optimization of predictive models is required

Bruin verkleuren en slap worden van sperziebonen : September 2016

Browning is considered to be the most important quality-limiting factor in green beans for both the fresh and cut bean markets. For the fresh market, we determined the duration at which green beans can be stored at optimal and suboptimal temperatures while retaining an acceptable quality. Furthermore we assessed the effects of bean surface moisture and a decontaminating washing step on bean browning during storage. For cut bean market, we determined the duration green beans can be stored at 3°C prior to cutting and packaging and still retain an acceptable quality after a 7 day shelf life period. The experiments were carried out using both the fresh market cultivar ‘Domino’ and the cut market cultivar ‘Stanley’. Besides browning, the occurrence of loss of firmness limited the quality of fresh beans considerably. Both cultivars were able to cope with 2 days storage at 3 - 9°C while retaining an acceptable quality. Despite ‘Domino’ having a better quality score than ‘Stanley’, both cultivars obtained an acceptable score. For ‘Domino’ loss of firmness was the most important factor limiting quality, while in ‘Stanley’ browning was more limiting. Both cultivars could not withstand 4 days of storage without loss of quality. Bean surface moisture due to condensation or washing limited browning, but increased loss of firmness. Decontaminating the beans did not influence the quality of fresh green beans. Neither washing, condensation or decontamination affected the percentage of intact beans. Concerning cut and packaged beans, the maximum storage time of green beans at 3°C prior to cutting and packing, followed by a shelf life of 3 days at 4°C plus 4 days at 7°C, was determined. ‘Domino’ could be stored 2 days at a maximum with an acceptable loss of quality; ‘Stanley’ resisted 4 days storage without quality loss and 7 days with an acceptable quality loss according to the standards of Bakker Barendrecht

Effect of 12% CO2 during storage on quality of Allison seedless table grapes from Spain: GreenCHAINge WP1 - Table Grapes

Table grapes produced in South Africa and South America are transported in Reefer containers to the Netherlands. This takes two to three weeks’ time, depending on the route and the transport company.Usually SO2-pads are used to suppress development of fungal infection during long term transport. The use of SO2-pads is increasingly under pressure. In literature several alternatives have been presented.Previous research (ExperiCo, 2015) showed elevated levels of CO2 to give promising results. However, finding the right concentration is a trade-off between Botrytis suppression and the evolution of an off taste.The goal of this study:To investigate whether elevated levels of CO2 can suppress Botrytis infection in Allison seedless table grapes from Spain during storage without causing off flavour. Allison seedless table grapes from Spain (week 45 in 2016), suffered a lot from fungal infection. This fungal infection was already visible in the starting material.High CO2 applied in a flow through system (12% CO2 and 18% O2) could supress fungal infection during storage to certain extent.The results of this experiment are promising. However, since infection levels were very high already at the start of the experiment, it has to be repeated before final conclusions can be drawn concerning the effectivity of the treatment and the effect on taste

Validation of Australian food quality traceability technology (Smart-r-tag) : Quality development of two perishable fruits; Strawberry and Avocado

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