Kinetic models for apple quality indicators throughout a sustainable postharvest chain

Once harvested, apples, like every other fresh fruit and vegetable, undergo continuous changes in quality as part of the normal ripening and senescence. Ripening may be desirable in that it makes the fruit palatable, but it limits the storage life of the fruit. To ensure year-round supply of apples, considerable effort is made to slow down ripening. The most common practice is to store apples at low temperatures, under an atmosphere with reduced oxygen levels and elevated carbon dioxide level - often referred to as controlled atmosphere (CA) storage. CA storage prevents quality losses by inhibiting respiration and ethylene production, two vital reactions involved in quality degradation during ripening. Different apple cultivars have different optimal CA conditions, depending on their sensitivity to chilling, low oxygen, and high carbon dioxide injury. Although CA storage is extensively used commercially to prolong the storage life of many apple cultivars, its effect on quality deterioration, from a quantitative point of view, is poorly understood. Mathematical models offer an important tool that can assist in optimizing CA storage. Moreover, mathematical models may help to improve our understanding of the underlying mechanisms of an observed phenomenon.The first objective of this dissertation was to develop kinetic models describing the postharvest evolution of two important apple quality indicators, namely flesh firmness and skin green background colour. A mathematical model for firmness loss was developed by assuming that ethylene production, the synthesis of pectin degrading enzymes, and pectin degradation are the main reactions involved in softening. Appropriate kinetic equations for these reactions were proposed. To estimate the parameters of the model, firmness and ethylene emission data were collected from Jonagold, Braeburn, and Kanzi apples harvested at three different stages of maturity and stored at different temperatures and CA conditions, followed by exposure to ambient shelf life conditions. Moreover, stochastic model parameters were identified, and by treating these as fruit-specific model parameters, the Monte Carlo method was used to model the fruit-to-fruit biological variability in flesh firmness within a batch of apple. This modelling concept was extended to model the fruit-to-fruit variability in skin background colour of Jonagold apples. Colour loss was assumed to be due to chlorophyll breakdown, the rate of which was dependent on the endogenous ethylene concentration. Stochastic model parameters were identified, and by treating the stochastic model parameters as random factors, the Monte Carlo method was again used to model and describe the propagation of the fruit-to-fruit variability of the background colour within a population of fruit. Practical storage management applications of these models were discussed. Software code for predicting apple quality evolution, based on the quality models, was developed, and integrated in a newly developed software tool, the FRISBEE tool, for cold chain optimisation. This software tool was developed within the framework of the European Union FP7 project, FRISBEE (Food Refrigeration Innovations for Safety, consumers Benefit, Environmental impact and Energy optimisation along the cold chain in Europe). Quality models for other food products (pork neck cutlet, ready-to-eat pork meal, salmon, spinach and ice cream) were also integrated in the FRISBEE tool. The objective of developing the FRISBEE tool was to provide a software tool for evaluating cold chains with respect to three important sustainability indicators: product quality, energy use and global warming impact.To get an in depth understanding on the mechanism of apple softening, pectin modifications and the evolution of pectin-modifying enzymes during postharvest storage and ripening were investigated. Jonagold apples were harvested at commercial maturity and stored at different temperatures and CA conditions for six months, followed by exposure to ambient shelf life conditions (18 20°C under air) for 2 weeks. The composition of the pectic material was analysed. Furthermore, firmness and ethylene production of the apples were assessed. Generally, the main changes in pectin composition associated with the loss of firmness during ripening in Jonagold apples were a loss of side chain neutral sugars, increased water solubility and decreased molar mass. Also, the activities of four important enzymes possibly involved in apple softening, namely beta-galactosidase (BG), alpha-arabinofuranosidase (AF), polygalacturonase (PG) and pectin methylesterase (PME), were measured. Pectin-related enzyme activities were highly correlated with ethylene production, but not always with pectin modifications. BG was found to be the cell wall enzyme most related to apple softening, as its activity was more correlated to softening. In addition, loss of side chain neutral sugars (particularly galactose, arabinose and xylose) was strongly correlated to loss in flesh firmness. PG activity was poorly correlated to softening, and no significant pectin depolymerisation was observed, except for apples that had undergone extensive softening. AF activity showed poor correlation to softening, although loss of arabinose was correlated to softening. PME activity was high, but the degree of methoxylation of the pectin polysaccharide remained unchanged during ripening. In addition, the expression profiles of the genes encoding these four cell wall enzymes were investigated by qRT-PCR. MdPG4, MdBG1 and MdBG6 genes were identified as key ripening-related, and could serve as molecular markers for apple softening. Further studies on genetic regulation of apple fruit softening should focus on these cell wall-related genes. A conceptual model for pectin disassembly during softening in postharvest ripening of apple was proposed.Acknowledgements i Abstract v Samenvatting ix Abbreviations and Symbols xiii Contents xvii General Introduction 1 1.1. Apple cold chain and sustainability 1 1.2. Quality evolution and fruit ripening along a postharvest chain 3 1.2.1. Fruit quality factors 3 1.2.2. Quality changes during fruit ripening 4 1.2.3. The role of oxygen and carbon dioxide in fruit ripening 4 1.2.4. Ripening and the plant hormone ethylene 6 1.3. Softening and the cell wall 8 1.3.1. Plant cell walls 8 1.3.2. Cell wall modifications during fruit ripening 10 1.3.3. Cell wall enzymes and their role in ripening 12 1.4. Managing quality loss in postharvest chain 14 1.4.1. Postharvest handling techniques 14 1.4.2. Postharvest storage techniques 15 1.4.3. Quality modelling in postharvest biology 17 1.5. Main aim of thesis 20 1.6. Thesis objectives and outline 21 Kinetic modelling of firmness breakdown in apples along the postharvest chain 23 2.1. Introduction 24 2.2. Model Development 25 2.2.1. Kinetic basis of model 25 2.2.2. Model equations 27 2.2.3. Output relations 29 2.3. Materials and methods 30 2.3.1. Fruit 30 2.3.2. Storage experiments 30 2.3.3. Firmness measurements 30 2.3.4. Ethylene emission measurements 31 2.3.5. Initial values 31 2.3.6. Parameter estimation 32 2.4. Results and discussion 34 2.4.1. Model predictions 34 2.4.2. Sensitivity analysis 37 2.5. Conclusions 40 Stochastic modelling of postharvest biological variability in flesh firmness of apple 41 3.1. Introduction 42 3.2. Materials and methods 43 3.2.1. Fruit 43 3.3. Storage experiments 43 3.3.1. Measurements 44 3.4. Model development 45 3.4.1. Mathematical modelling of firmness breakdown 45 3.4.2. Introducing biological variations 46 3.4.3. Model calibration 47 3.5. Results and discussion 49 3.5.1. Average batch behaviour of firmness 49 3.5.2. Biological variation in firmness 53 3.5.3. Model validation 57 3.5.4. Practical applications 59 3.6. Conclusions 61 Managing postharvest biological variability in skin background colour 63 4.1. Introduction 64 4.2. Materials and methods 65 4.2.1. Fruit 65 4.2.2. Storage experiments 65 4.2.3. Measurements 66 4.3. Model development 66 4.3.1. Kinetic basis for postharvest loss in skin green background colour 66 4.3.2. Model equations 67 4.3.3. Model outputs 69 4.3.4. Model calibration 70 4.3.5. Identification of random model parameters 72 4.3.6. Monte Carlo simulations 73 4.4. Results and discussion 74 4.4.1. Relationship between chlorophyll content and colour measurements 74 4.4.2. General product behaviour 74 4.4.3. Model calibration 76 4.4.4. Random model parameters 76 4.4.5. Propagation of biological variation 77 4.4.6. Practical applications 83 4.5. Conclusions 86 The FRISBEE tool, a software for optimising the trade-off between food quality, energy use, and global warming impact of cold chains 87 5.1. Introduction 88 5.2. Development of the FRISBEE tool 89 5.2.1. Definition of reference cold chains 89 5.2.2. Kinetic models for food quality evolution 91 5.2.3. Energy use calculations 92 5.2.4. Global warming impact assessment 93 5.2.5. Software development process of the FRISBEE tool 93 5.3. Using the FRISBEE tool for Cold chain simulations 96 5.3.1. Examples of the FRISBEE tool simulations 98 5.4. Conclusions 103 Molecular basis of apple softening: pectin modifications and the role of pectin-modifying enzymes 105 6.1. Introduction 106 6.2. Materials and methods 107 6.2.1. Fruits 107 6.2.2. Storage experiments 107 6.2.3. Measurement of firmness 108 6.2.4. Measurement of ethylene production 108 6.2.5. Pectin characterization 108 6.2.6. Analysis of cell wall enzyme activity 110 6.2.7. Statistical analysis 113 6.3. Results and discussion 113 6.3.1. Effect of storage temperature and controlled atmosphere on the evolution of firmness and ethylene production 113 6.3.2. Changes in pectic polymers during softening of Jonagold apples stored under different conditions 115 6.4. Conclusions 123 Genetic regulation of pectin-modifying proteins during apple fruit softening 125 7.1. Introduction 126 7.2 Materials and methods 127 7.2.1 Plant material and storage experiments 127 7.2.2. Real-time quantitative PCR (qRT-PCR) 128 7.2.3. Evaluation of firmness and ethylene production measurements 129 7.2.4. Cell wall pectin characterization 129 7.2.5. Data analysis 129 7.3. Results 131 7.2.2. Detection of softening-related candidate genes in the apple genome 131 7.2.3. Expression analysis of softening-related candidate genes during storage under different conditions 131 7.2.4. Correlation analysis between cell wall gene expression, cell wall hydrolases, and flesh softening 134 7.2.5. 1-MCP treatment validates MdPG1, MdPG4, MdBG1 and MdBG6 as cell wall genes associated with ripening 136 7.4. Discussion 140 7.4.1. MdPG4 is the main ripening-related gene within the POLYGALACTURONASE gene family 140 7.4.2. PME is unlikely to play an important role in apple softening during ripening 141 7.4.3. Beta-galactosidase genes are the main cell wall genes regulating apple fruit softening 141 7.4.4. AF activities may be mediated by different cell wall genes 142 7.4.5. A conceptual model for cell wall disassembly during apple softening 143 7.4.6. Cell wall pectin degradation is important, but not sufficient, for apple fruit softening 145 7.5. Conclusion 146 7.6. Supporting Information 147 General conclusions and perspectives 151 8.1. General conclusions 151 8.2. Future perspectives 154 References 157 List of publications 173nrpages: 201status: publishe

A mechanistic model to describe the effects of time, temperature and exogenous ethylene levels on softening of kiwifruit

Early harvested kiwifruit (Actinidia deliciosa (A Chev) Liang et Ferguson cv ‘Hayward’), from 14 growers and two seasons were stored under a wide range of storage temperatures (0–10 ?C) and exogenous ethylene levels (0–200 mL L?1) followed by an ethylene free shelf life period at 0–20?C. Firmness levels were monitored using a non-destructive compression technique. A mechanistic model, based on a simplified representation of the physiology underlying fruit softening, explained 97% of the observed variation. The kinetic model parameters appeared to be generic for the 14 grower lines studied. Differences between the grower lines could be explained based on differences in the initial firmness levels and the initial amounts of active enzyme system present. The model was validated with independent experimental data on the softening of 70 batches of main harvest kiwifruit stored at 0?C, with more than 99% of the variation explained for each of the 70 grower lines. A further validation was done using literature data on shipping of “Kiwistart” fruit under dynamic temperature and ethylene conditions.status: publishe

A mechanistic modelling approach to understand 1-MCP inhibition of ethylene action and quality changes during ripening of apples

BACKGROUND: 1-Methylcyclopropene (1-MCP) inhibits ripening in climacteric fruit by blocking ethylene receptors, preventing ethylene from binding and eliciting its action. The objective of the current study was to use mathematical models to describe 1-MCP inhibition of apple fruit ripening, and to provide a tool for predicting ethylene production, and two important quality indicators of apple fruit, firmness and background colour. RESULTS: A model consisting of coupled differential equations describing 1-MCP inhibition of apple ripening was developed. Data on ethylene production, expression of ethylene receptors, firmness, and background colour during ripening of untreated and 1-MCP treated apples were used to calibrate the model. An overall adjusted R2 of 95% was obtained. The impact of time from harvest to treatment, and harvest maturity on 1-MCP efficacy was modelled. Different hypotheses on the partial response of 'Jonagold' apple to 1-MCP treatment were tested using the model. The model was validated using an independent dataset. CONCLUSIONS: Low 1-MCP blocking efficacy was shown to be the most likely cause of partial response for delayed 1-MCP treatment, and 1-MCP treatment of late-picked apples. Time from harvest to treatment was a more important factor than maturity for 1-MCP efficacy in 'Jonagold' apples. © 2017 Society of Chemical Industry.status: publishe

Measuring ethylene in postharvest biology research using the laser-based ETD-300 ethylene detector

Abstract Background Ability to measure ethylene is an important aspect of postharvest management, as knowledge of endogenous ethylene production is used in assessing physiological status, while response of crops to exogenous ethylene informs efforts needed to control unwanted ripening. An ethylene monitoring device with a laser-based photoacoustic detector, ETD-300, was recently developed by Sensor Sense B.V., Nijmegen, The Netherlands. In terms of performance, the ETD-300 is superior to all other current ethylene measurement devices, with a sensitivity of 0.3 nL L−1, a response time of 5 s, and an ability to monitor ethylene in real time. Although the ETD-300 is relatively easy to operate, the performance and correctness of the data obtained depends on the choice of settings, which depends on the application. Results This article provides a description of different ways in which the ETD-300 can be used in postharvest research for monitoring ethylene production and ethylene presence in an environment. We provided guidelines on selecting the appropriate method (Continuous Flow, Stop and Flow, and Sample methods), and operational curves for deciding on suitable combination of free volume, flow rates, and period for the different measurement methods. Conclusions Using these guidelines and operational curves, ETD-300 users can considerably reduce the measurement effort by limiting trial and error in establishing appropriate methodologies for their application. The guidelines also comment on accurate use of the ETD-300, as using the inappropriate settings could lead to erroneous measurements. Although these methodologies were developed primarily for postharvest application, they can be applied in other plant science research

Modelling biological variation in the skin background colour of ‘Jonagold’ apples during controlled atmosphere storage

Skin background colour is a very important quality aspect in the grading of 'Jonagold' apples, with consumers usually preferring fruit with a green background colour. However, apple handlers are usually faced with large fruit-to-fruit variability of background colour within a population of fruit. In this study, a stochastic modelling approach is used to describe how the initial fruit-to-fruit variability in the background colour of 'Jonagold' apples present at harvest, propagates throughout the postharvest chain. Two hundred fruit were harvested and stored at 1 or 4°C, under different controlled atmosphere (CA) conditions for six months. The fruit were taken out of storage every two months, and the background colour and the ethylene production of individual fruit were measured. At the end of the six months storage, the fruit were placed in shelf life conditions for 15 days, during which skin background colour and ethylene production were measured every five days. A mathematical model was developed to describe the postharvest loss of the skin greenness of apples during CA storage, by assuming that the loss is principally due to chlorophyll breakdown, the rate of which is dependent on the endogenous ethylene concentration within the fruit. The stochastic model parameters in the model were identified, and by treating these parameters as fruitspecific parameters, the model could describe more than 93% of the data for the individual fruit. By considering these fruit-specific parameters as stochastic parameters, the Monte Carlo method was used to describe the propagation of the fruit-to-fruit variability of the background colour of 'Jonagold' apples within a population. The model developed in this study can be used to predict how the initial fruit-to-fruit variability within a batch of apple will propagate throughout the postharvest chain.status: publishe

Slow softening of Kanzi apples (Malus x domestica L.) is associated with preservation of pectin integrity in middle lamella

Kanzi is a recently developed apple cultivar that has an extremely low ethylene production, and maintains its crispiness during ripening. To identify key determinants of the slow softening behaviour of Kanzi apples, a comparative analysis of pectin biochemistry and tissue fracture pattern during different ripening stages of Kanzi apples was performed against Golden Delicious, a rapid softening cultivar. While substantial pectin depolymerisation and solubilisation was observed during softening in Golden Delicious apples, no depolymerisation or increased solubilisation was observed in Kanzi apples. Moreover, tissue failure during ripening was mainly by cell breakage in Kanzi apples and, in contrast, by cell separation in Golden Delicious apples. Kanzi apples had lower activity of beta-galactosidase, with no decline in the extent of branching of the pectin chain. A sudden decrease in firmness observed during senescence in Kanzi apples was not due to middle lamella dissolution, as tissue failure still occurred by cell breakage.publisher: Elsevier articletitle: Slow softening of Kanzi apples (Malus×domestica L.) is associated with preservation of pectin integrity in middle lamella journaltitle: Food Chemistry articlelink: http://dx.doi.org/10.1016/j.foodchem.2016.05.138 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved.status: publishe

A transcriptomics-based kinetic model for enzyme-induced pectin degradation in apple (Malus x domestica) fruit

© 2017 Elsevier B.V. Modifications of cell wall pectin during apple softening involve the concerted action of several enzymes. A mathematical model was developed to describe the action of cell wall hydrolases resulting in specific modifications in cell wall pectin polysaccharides during softening of ‘Jonagold’ apple. A conceptual model of enzyme-induced pectin breakdown was formulated based on current knowledge. Activities of different cell wall enzymes were considered to be directly correlated to the expression levels of the respective genes. The first pectin modification to occur was assumed to be cleavage of side chain neutral sugars from the polymer backbone, by the action of side chain debranching enzymes, beta-galactosidase and alpha-L-arabinofuranosidase. It was considered that this increases cell wall porosity, facilitating the action of pectin methyl esterase, which de-methylates pectin polysaccharides, providing a substrate for polygalacturonase. Polygalacturonase degrades the de-methylated pectin chain, producing smaller pectin fragments which are easily solubilised in water. Coupled differential equations were written based on these reactions. The model was calibrated using data on expression of cell wall-related genes, activities of enzymes encoded by these genes, and the associated modifications in pectin polysaccharides during softening in ‘Jonagold’ apples stored at 1 °C under regular atmosphere or controlled atmosphere. The model could explain 79% of the variation in the data. Using the model, in silico investigations on the impact of downregulation of different key cell wall-related genes on softening were carried out, and the results were compared successfully with currently available literature data.status: publishe

Towards flexible management of postharvest variation in fruit firmness of three apple cultivars

Stochastic modeling provides a useful tool in managing biological variation in the postharvest chain. In the current study, the fruit-to-fruit variability in the postharvest firmness of apples was modeled. Apples from three cultivars ('Jonagold', 'Braeburn', and 'Kanzi') were harvested at different levels of maturity, and stored at different temperatures and controlled atmosphere (CA) conditions. By using a kinetic model describing firmness breakdown as a function of time, temperature, controlled atmosphere conditions and endogenous ethylene concentration, the main stochastic variables were identified as the initial firmness and the rate constants for firmness breakdown and ethylene production. Treating these variables as random model parameters, the Monte Carlo method was used to simulate the propagation of the fruit-to-fruit variability in flesh firmness within a batch of apples during storage under different CA conditions and subsequent shelf-life exposure. The model was validated using independent data sets from apples picked in a different season. The model developed in this study can be used to predict the probability of having apples of certain firmness after long term storage for different scenarios of temperatures and CA conditions. © 2013 Elsevier B.V.status: publishe

A numerical evaluation of adaptive on-off cooling strategies for energy savings during long-term storage of apples

The main energy costs of long-term apple storage are associated with cooling. Reducing these costs without compromising product quality may be possible with minor room temperature increases. This paper presents a transient CFD model to evaluate automatic on-off cooling control based on different temperature differentials (0.4, 0.5 and 0.7 °C around a setpoint). Effects on temperature uniformity, quality changes and energy consumption during long-term storage of apples were calculated. A model for apple firmness change kinetics was coupled to the CFD model and applied to calculate changes in quality uniformity in a coolstore affected by spatiotemporal fluctuations in temperature. Large temperature fluctuations were observed near the outer edges of the stack and were more pronounced with a higher cooling differential. Using a small cooling differential around the setpoint temperature showed a better overall performance in terms of energy consumption and final product quality.status: publishe

A CFD study of adapting on-off cooling cycles in apple cold stores for energy saving

Cooling during long-term storage of apples is a costly endeavour where electricity costs contribute up to 60% of the total variable costs at the end of storage. Here, the effect of controlling on-off cooling cycles on temperature and quality change uniformity and energy consumption is investigated with transient CFD models. The quality change of apples during the storage season as a function of position in the room is calculated with a firmness model using the FRISBEE tool. The on-off cooling cycle is regulated based on three different cooling differentials (0.4, 0.5 and 0.7°C). Temperature fluctuations were large near the edges of the stack. A larger cooling differential led to larger fluctuations in the middle of the stack compared to the smaller differentials. Although off-cooling cycles were longer with larger differential, overall this strategy consumed more energy. Differences in apple firmness changes between the three cooling differentials are found insignificant. The smaller cooling differential resulted in a better overall performance.status: publishe