External quality measurements reveal internal processes

With the present developments in CA technology it becomes possible to fine tune the storage conditions to the specific needs of the product. This generates the need to know the exact quality conditions of the product before storage starts. By measuring the initial quality we can determine these conditions optimally. At present the most likely candidates to assess the initial quality with fast and non-destructive measurements are colour, chlorophyll fluorescence, and maybe NIR spectroscopy. Two examples are presented where initial colour measurements on all products in a batch can be shown to be indicative for the keeping quality of that batch. The first example focuses on how initial colour measurements using a 3CCD video camera can be utilised to predict the keeping quality of a batch of cucumbers where colour itself is regarded as the most important quality attribute. The second example focuses on how colour measurements can be used to predict the keeping quality of a batch of strawberries where the ability to suppress a Botrytis cinerea infection is the most important quality attribute. Furthermore, attention is given to the use of modulated chlorophyll fluorescence imaging as a possible initial quality indicator for rose leafy stem cuttings. The level of inhomogeneity in the quantum yield of photochmistry od PSII of leaves of rose cuttings may be an indictor of the capability of the cutting to recover from severance, and to form roots and generate regrowt

Effects of ca treatments and temperature on broccoli colour development

Broccoli combines high contents of vitamins, fibres and glucosinolates with a low calorie count and is sometimes referred to as the ‘crown jewel of nutrition’. Colour is one of the most important quality attributes of broccoli, and yellowing due to senescence of broccoli florets is the main external quality problem in the broccoli supply chain. Controlled Atmosphere (CA) is a very effective method to maintain broccoli quality but the effects of CA on colour retention have not been studied extensively. The aim of this paper is to characterise the colour behaviour (measured by RGB colour image analysis) of broccoli as affected by CA and temperature. Data on colour behaviour and gas exchange were gathered for broccoli heads that were stored in containers at three temperatures and subjected to four levels of O2 and three levels of CO2. Gas conditions and temperature have a clear effect on the colour change of broccoli especially at low O2 in combination with high CO2. An integrated colour model is proposed that combines a colour model with a standard gas exchange model. The colour model is based on three differential equations describing the formation of (blue/green) chlorophyllide from the colourless precursor, the bidirectional conversion of chlorophyllide into (blue/green) chlorophyll, and the decay of chlorophyllide. During the first step of building the integrated model, gas exchange data were analysed simultaneously using multi response regression analysis. No fermentation was encountered for this batch of broccoli. During the second step it was found that only one of the reactions of the colour model, the decay of chlorophyllide, is affected by the gas conditions. In the final step, a multi-response approach was applied where gas exchange parameters were estimated using the gas exchange model, the colour parameters were estimated using the colour model with both models linked via the reaction rate constant affected by the gas conditions. Such a calibrated, integrated, model could be used as a tool for predicting colour change in the postharvest chain

Ready to Eat Nectarines - Assuring Quality in the Chain

Time-resolved reflectance spectroscopy, coupled to the modelling of firmness decrease, was used to predict at harvest softening behaviour of nectarines. Selected fruit were used in an export trial from Italy to The Netherlands. Quality assessed after shelf life was in agreement with the predicted firmness for fruit of different stages of maturity, showing that it is possible to select fruit at harvest for different market destinations and prevent transportation of fruit unsuitable for consumption

First attempts of linking modelling, Postharvest behaviour and Melon Genetics

The onset of climacteric is associated with the end of melon fruit shelf-life. The aim of this research was to develop practical and applicable models of fruit ripening changes (hardness, moisture loss) also able to discriminate between climacteric and non-climacteric behaviour. The decrease in firmness was measured non-destructively by flat-plate compression; moisture loss was measured by weight loss. A set of 13-15 near-isogenic lines (NILs) derived from the climacteric line SC3-5 was used to verify the relationship among the climacteric behaviour and ripening related changes (weight loss, softening and color) during two consecutive seasons. The biological variance models for moisture loss and firmness followed a simple exponential behaviour that explained more than 90% of the total variance. Results of the analyses using these models could not be linked to properties of near-isogenic lines like climacteric behaviour, ethylene production or skin thickness. The results suggest that the phenotype is more important than genotype, when considering mean values. These results seem to suggest that relations may exist between the different processes and properties of NILs on an individual basis, not on mean values

Physiology of biological variation

In agricultural products, variation exists in quality attributes between batches. Examples of this biological variation are well known and the general response is trying to suppress it as much as possible; to create uniformity using pre- andpostharvestmethods. This thesis shows a methodology that takes advantage of the biological variation, instead of treating it as a nuisance. This biological variation methodology was applied to understand the expected keeping quality of batches.The methodology currently consists of three steps. Firstly, repeated non-destructive measurements of quality properties of individuals need to be applied to find out how the quality attribute changes over time without having to worry about biological variation. Secondly, kinetic models need to be constructed that show the quality attribute changing over time as a combination of simultaneously occurring processes that, ideally, have a strong physiological background. The last step consists of translating the kinetic model that describes the behaviour of the quality attribute of individuals to batches usingstochastics. This methodology is applied for cucumbers and strawberries.Cucumber. The keeping quality for a cucumber, defined as the time the colour remains acceptable to the consumer, depends on the state of the chlorophyll metabolism. A generic model was build that describes thepostharvestcolour behaviour in time and temperature for individual cucumbers, irrespective of growing conditions and cultivar. The model enables prediction on the batch keeping quality, on the basis of initial colour measurements only. Strawberry. Postharvestlife of strawberries is largely limited by Botrytiscinereainfection. A colour modelwasbuilt that describes the simultaneous development of the red colour and the anti-fungal function of individual strawberries over time. Batch keeping quality predictions could be derived on the basis of initial colour measurements or from the time between harvest dates.Batch model. The batch model describes the influence of one source of biological variation, here assumed to be variation in light conditions during thepreharvestperiod, on the distribution of precursor concentrations by combining (product specific) kinetic models and a generic stochastic part. The batch model described batch behaviour in terms of current maturity, biological variation and maximal maturity towards keeping quality of cucumbers and strawberries. Applications of biological methodology may be numerous: proposing protocols for keeping quality predictions, characterisation of cultivar specific influences on keeping quality or, in general, starting of a new field that is concerned with the 'hidden' information that is present in all biological batches

Water loss in horticultural products. Modelling, data analysis and theoretical considerations

The water loss of individual fruit (melon, plum and mandarin) was analysed using the traditional diffusion based approach and a kinetic approach. Applying simple non linear regression, both approaches are the same, resulting in a quite acceptable analysis. However, by applying mixed effects non linear regression analysis, explicitly including the variation over the individuals, the kinetic approach was found to reflect the processes occurring during mass loss better than the diffusion approach. All the variation between the individuals in a batch could be attributed to the initial mass or size of the individuals. The fraction of the fruit mass that is available for transpiration is the key item in the water loss process, rather than the skin resistance and fruit area. Obtained explained parts are well over 99%

Techniques to assess biological variation in destructive data

Variation is present in all measured data, due to variation between individuals (biological variation) and variation induced by the measuring system (technical variation). Biological variation present in experimental data is not the result of a random process but strictly subject to deterministic rules as found on non-destructive data. The majority of data obtained in research are obtained by destructive techniques. The rules on behaviour and magnitude of variation should however, also apply to these cross sectional data. New techniques have been developed for analysing cross sectional data including the assessment of variation: 1) Probelation. In a set of cross-sectional data, the individual with the highest value at some point in time will resemble the individual with the highest value at previous or future times, and the second highest the second highest at previous times, and so on. One can assign an identification number based on the sorted order of the measured values per measuring point in time. This number can be used as a pseudo fruit number in indexed or mixed effects regression analysis, similar to the data analysis of longitudinal data; 2) Density assessment. For not too complex kinetic processes the density function can be deduced. Measuring a large number of individuals (on a single point in time) provides the possibility to assess directly the variation in the data; 3) Quantile regression. This technique also relies on ranking the data per measuring time. The probelation number is now converted into a probability, and the mean and standard deviation is estimated directly along with the kinetic parameter, using simple non-linear regression. Based on simulated data sets, all three techniques are demonstrated, and the results compared with the input values. Explained parts (R2 adj) obtained are generally well over 90%, provided that the technical variation is not excessively large