The impact of biological variation on postharvest behaviour: The case of dynamic temperature conditions

The postharvest handling chain is characterised by temperature conditions that are generally not constant throughout the chain. On top of this, postharvest research is dealing with large sources of biological variation. This paper outlines a general model approach of how to account for the propagation of biological variation when analysing experimental postharvest data gathered at dynamic temperature conditions. The proposed technique was applied to experimental data on colour change of tomatoes cv. ‘Tradiro’. The previously developed multivariate stochastic approach was combined with a transformation of real time into physiological time to account for the effect of dynamic temperature conditions. The developed model has the capability to describe the complex batch behaviour showing propagation of biological variation in tomato colour as a function of time and dynamically changing temperature during postharvest storage. This makes the current approach extremely suitable for application in real life situations when evaluating and optimising a logistic handling chain.     

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.     

The impact of biological variation on postharvest behaviour of Belgian endive: The case of multiple stochastic variables

Data generated in postharvest research is often characterised by its large biological variation. This variation generally obscures the behaviour of interest, complicating both the statistical and conceptual interpretation of the data. This paper outlines a general model approach of how to account for the propagation of biological variation when analysing experimental postharvest data. Previous work on the case of a single stochastic variable was extended to the situation of two stochastic variables. The proposed technique was applied to experimental data on stem growth of Belgian endive. By explicitly using the information on biological variation, the number of model parameters to be estimated (only two) did not change while the fitness of the model to describe the experimental data was improved tremendously as compared to the traditional fitting of a model without stochastic variables.The model could describe the complex behaviour of a batch showing propagation of biological variation in stem length of Belgian endive as a function of time and temperature during postharvest storage. Differences between batches could not be related directly to either grower, harvest or culture type but probably depends on a complex interaction with preharvest factors.     

Ethanol vapor prior to processing extends fresh-cut mango storage by decreasing spoilage,but does not always delay ripening

This study was undertaken to optimize ethanol vapor application as a ripening inhibitor on whole mangoes to extend fresh-cut mango shelf life. Freshly harvested mangoes were first subjected to hot water (+HW) at 46 °C for 60 or 90 min to simulate quarantine heat treatments, or remained untreated (−HW). Fruit of each batch (+ or −HW) were then held at 20–25 °C for 4 or 7 d (D4 and D7) after the hot water treatment before being exposed to ethanol vapors [0 h (E0), 10 h (E10), or 20 h (E20)]. Fruit were then peeled and cut into slices, packed in plastic clamshells, and stored at 7 °C for 15 d. Only slices from +HW-D4-E20-treated fruit maintained higher firmness, hue angle, and titratable acidity (TA) in storage. The +HW-D7-E10- or E20-treated fruit had higher hue angle than E0, but firmness, total soluble solids, TA, pH, and respiration rate did not differ. Internal ethanol and acetaldehyde were very high in slices from +HW, D4 and D7, E20 and −HW-D7-E20-treated fruit. A sensory panel could perceive higher firmness and acidity in slices from fruit treated with ethanol. However, E20 induced off-flavor, and these fruit were least preferred.Ethanol exposure on fruit was repeated with purchased mangoes that had been subjected to a commercial quarantine heat treatment. A second heat treatment of 18 h at 38 °C and 98% relative humidity was added to one batch of fruit in this experiment. Ethanol vapors did not result in delayed ripening in those mangoes. However, this treatment inhibited microbial growth. The second heat treatment did not improve fresh-cut mango shelf life, and further, microbial growth increased compared to other treatments. It is concluded that, due to inconsistent results, ethanol vapor applied for 20 h to whole mangoes prior to processing for fresh-cut is not a practical approach to delay ripening; however, at lower doses (10 h), it could be used as a safe microbial control in a fresh-cut production sanitation system.     

荔枝采后病害的生物防治研究进展

简要介绍了国内外已筛选出的荔枝采后病害拮抗微生物的种类及其生物防治途径,并对拮抗菌的抑菌机理研究情况进行综述。同时,对我国荔枝采后病害生物防治的现状与存在的问题及今后该领域的发展方向也进行了阐述。     

Combining physical, chemical and biological methods for synergistic control of postharvest diseases: A case study of Black Root Rot of carrot

Combining different control methods can improve control efficacy, increase the spectrum of controlled pathogens and reduce the possibility of resistance development. To be successful, however, the different methods need to be compatible: the first treatment should not have any deleterious effect on the succeeding one; preferably, it should contribute to its efficacy. In the last few years, carrot growers in Israel have begun to brush carrots before storage to remove the outer peel of the root. In the present study we show that this practice enhances the appearance of Black Root Rot during storage, a postharvest disease caused by the fungus Thielaviopsis basicola. The chemical fungicide iprodione is usually applied before storage to reduce the development of postharvest diseases. We evaluated the efficacy of combining physical, low-residue chemical and biological control agents as an alternative to the conventional chemical control approach. A technology for the precise application of steam and combined application with stabilized hydrogen peroxide (Tsunami^® 100) or a yeast commercial product (Shemer™) were tested. Used alone, both the steam and Tsunami were highly effective at reducing disease decay but were phytotoxic to the roots. Application of combined treatments of sublethal steam followed by a sublethal dosage of Tsunami or Shemer improved efficacy and disease control by 80 and 86%, respectively. These combinations showed a synergistic effect as compared to each of the treatments alone. The same pattern, effecting up to 54% disease control, was observed with the non-compatible combination of applying Tsunami first, washing it off with water and then applying Shemer. Thus disease-control agents can potentially be used for a short period, then washed off, if needed, and efficiently followed by application of a biological control agent. The biological pathway and mode of action are still under investigation but to the best of our knowledge, this is the first study to mathematically demonstrate synergistic effects of sublethal treatments applied sequentially to control postharvest disease as a potential method to reduce the use of chemicals in fruit and vegetables.     

Managing biological variation in skin background colour along the postharvest chain of ‘Jonagold’ apples

Skin background colour is an 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 was 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 and twenty ‘Jonagold’ apple fruit were harvested and stored at 1 °C or 4 °C, under different controlled atmosphere (CA) conditions for 6 months, followed by 2 weeks exposure to shelf-life conditions, during which the background colour and ethylene production of the individual fruit were measured. A kinetic model was developed to describe the postharvest loss of skin greenness, by assuming that the loss was principally due to chlorophyll breakdown, the rate of which was dependent on the endogenous ethylene concentration. Stochastic model parameters were identified, and by treating these parameters as fruit-specific, the model could account for more than 95% of the variability of the data. By treating the stochastic model parameters as random factors, the Monte Carlo method was used to model and describe the propagation of the fruit-to-fruit variability of the background colour within a population of fruit. The model developed in this study might allow better management of variability in quality along the postharvest chain, by predicting how the initial fruit-to-fruit variability within a batch of apples will propagate throughout the postharvest chain, as a function of storage and shelf-life conditions.     

The Influence of Genotype–Environment Interaction on the Grain Yields of 10 Pigeonpea Cultivars Grown in Kenya

To study the importance of the effects of genotype–environment interactions on the yield of pigeonpea ( Cajanus cajan L. Millsp.), 10 early-maturing genotypes were grown in a randomized complete block design with three replications in a total of seven environments spread over five regions of Kenya between 1987 and 1988. Results indicated the presence of a substantial genotype–environment interaction effect on grain yield. The observed significant genotype–environment interaction effect is discussed in relation to its importance in pigeonpea grain yield evaluation studies. It is noted that the best genotype in one environment is not always so in other environments. Results from regression analysis indicated that this method of analysis is appropriate for describing the response of pigeonpea genotypes grown in a number of locations. Analysis of variance showed significant additive and multiplicative genotype–environment interaction effects. Only the first interaction principal component axis (IPCA) was found to be important in describing the multiplicative interaction effects. The additive main effects and multiplicative effects (AMMI) model allowed the partitioning of interaction variance into agronomically important sources (genotype groups), and the specific genotype × environment patterns that are the basis of these sources of variance were examined.     

青椒果实采后病害及控制技术研究进展

青椒果实因水分含量高和采后代谢旺盛,贮藏期间极易受到病原菌的侵染,严重影响了青椒的贮运品质和商品价值。总结了青椒果实采后的细菌性和真菌性病害,分别从物理、化学和生物保鲜技术3个方面介绍了青椒采后病害的主要防控手段,并进一步阐述了各类防控技术的作用机制,以期为青椒采后病害控制提供参考借鉴。     

Texture profiling of blueberries (Vaccinium spp.) during fruit development,ripening and storage

Blueberries are ‘soft fruit’, a name which underlies the impact that fruit texture plays in their marketability. Worldwide blueberry production has been increasing in the last years, and they are now the second most economically important soft fruit after strawberry. During pre- and postharvest ripening, fruit softening represents the most dramatic change, leading to important losses in quality during storage. To support the FEM breeding program toward the creation of new blueberry accessions with superior texture, we used a novel texture analyzer TAXTplus to improve the phenotyping resolution, a fundamental step for a more reliable and precise selection of the most suitable ideotypes. In this work change in the mechanical profile of the blueberry cultivar ‘Brigitta Blue’ was initially assessed during berry development, maturation and postharvest ripening, revealing important textural variations. In addition, this novel equipment was exploited to compare the fruit texture behavior at two specific stages, maturity and after postharvest cold storage, on a set of 49 different blueberry and hybrids cultivars and selections. A multivariate statistical approach, including principal component analysis, was employed to analyze the dataset related to the textural parameters, according to their performance. Finally a storage index, originally defined for apple fruit texture, was adopted to depict the different storage potential of a set of blueberry cultivars.     

Where systems biology meets postharvest

According to the dictionary, a system is something like “a group or combination of interrelated, interdependent, or interacting elements forming a collective entity”. In postharvest, fresh harvested food crops can be considered isolated small scale systems. Postharvest research aims to understand the quality of these ‘systems’ as influenced by postharvest conditions. The phenotypic quality of horticultural produce is based on genetic traits that are expressed through a cascade of reactions subject to complex regulatory mechanisms and diverse environmental conditions. Ultimately, to fully understand postharvest phenomena, a systemic approach that links genetic and environmental responses and identifies the underlying biological networks is required. Thanks to the development of high throughput omics techniques such system-wide approaches have become a viable option to support traditional postharvest research. This review provides an overview of systems biology and how it can lead postharvest research into a new era.     

The fruit cuticle as a modulator of postharvest quality

The composition and structure of fruit surface tissues have a noticeable influence on the postharvest storage potential of fruit, inasmuch as they behave as a barrier against drying, chemical attack, mechanical injuries and microbial infection. The cuticle is made of cutin, a biological insoluble polyester, embedded in an impermeable wax complex, and its inner side interacts intimately with the underlying epidermal cell walls. The cuticle plays a decisive role in plant development, being the first communication system with the surrounding biotic and abiotic environment. Published reports on the composition and biosynthesis of fruit cuticles are comparatively scarce, and many knowledge gaps exist on the part cuticles play in quality determination and postharvest performance. This review aims at collecting available information in relation to the role of the fruit cuticle as a determinant factor of some important traits related to postharvest quality, including water loss, susceptibility to physical and biological stresses, and decreased fruit firmness. To the best of our knowledge, this is the first published work focusing on the fruit cuticle as a major modulator of postharvest quality and interlinking existing dispersed literature on this topic. A deeper understanding of cuticle structure and function will be of help in understanding postharvest biology and in designing new technological solutions.     

Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials

Varietal selection for yield from a series of multi-environment trials can be regarded as a multi-trait selection problem in which the yields in different environments are synonymous with traits. As such an analysis of the data combined across environments should be conducted in order to form an index for selection. Analytical methods that include appropriate models for both the genetic variance structure (that is, the variances and covariances of genotype effects from different environments) and the residual variance structure (which typically comprises spatial covariance models for each trial) have been published previously. In the case of perennial crops, yields are often obtained from multiple harvests which implies that the data comprise short sequences of repeated measurements. Varietal performance in individual harvests is important for selection so that a combined analysis across both trials and harvests is required. The repeated measures nature of the data provides additional modelling challenges. In this paper we propose an approach for the analysis of multi-environment, multi-harvest data that accommodates the major sources of variation and correlation (including temporal). The approach is illustrated using two examples from sugarcane breeding programmes. The proposed models were found to provide a superior fit to the data and thence more accurate selection decisions than the common practice of conducting separate analyses of individual trials and harvests.     

Analytical Solution of Clamped Rectangular Thick Plate by Finite Integral Transform Method

简要介绍了国内外已筛选出的荔枝采后病害拮抗微生物的种类及其生物防治途径,并对拮抗菌的抑菌机理研究现状进行综述。同时,对我国果蔬采后病害拮抗微生物的商品化应用及今后该领域的发展方向也进行了阐述。     

A new method for modelling biological variation using quantile functions

We describe a general method for modelling biological variability as a function of time using fruit populations as examples. The method can incorporate variations in the biological age or maturity of fruit or other produce, as well as variations in the biological property being measured. Key developments are the use of quantile functions to describe the stochastic elements of the model, the assignment of probabilities to individual measurements based on their rank order within a sample, the use of individual measurements rather than means in the fitting process, and the fact that a single model equation with a single set of parameters describes the distribution of measurements across an entire population as a function of time. Using a representative fruit sample taken at a specific time and generalised model parameters, the technique allows the prediction of future fruit population distributions and the prediction of the date when a defined percentage of the fruit population meet a particular specification.The model development process demonstrates how to account for both biological age variability and measure (hue) variability simultaneously, the latter including components of measurement uncertainty and variability not related to biological age. Using quantile functions as the stochastic elements provides a wide range of distributional options.The method is described in detail using, as examples, a Complementary Log–Log sigmoid to model changes in ‘Hort16A’ kiwifruit hue angle preharvest, and a Logistic sigmoid to model changes in ‘Tradiro’ tomato skin hue data postharvest.The kiwifruit data comprised ten samples of 90 destructive hue angle measurements taken across the growing season from each of eight maturity areas (MAs). Allowing MA-specific parameter sets, the entire data set was modelled with an adjusted rsd of 0.46°. Further exploration of the sensitivity of model parameters showed that the model parameter t_m, which defines the timing of the ‘maturity’ of each MA, needed to be MA-specific.The tomato colour data comprised 120 fruit measured non-destructively on seven occasions postharvest. Initial model fits using a Normal distribution for the biological age component gave an rsd of 1.05°. The rsd was reduced to 0.61° using a four-parameter generalised lambda quantile function to describe the biological age variability and 0.63° when using a truncated Normal, suggesting that the underlying distribution was not Normal.The models are readily fitted using any statistical or computational package that offers non-linear optimisation including Microsoft Excel with Solver. The technique can be used as effectively with destructive as with non-destructive measurement data, in preharvest and postharvest situations, and can provide visualisation as well as computational tools. It can be applied to any populations that vary with time and where the units of the populations exhibit variability. These modelling techniques have formed the basis for decision support tools that have been operating commercially since 2007.     

草菇采后保鲜技术研究进展

草菇作为高温菇,不耐贮藏和运输。基于草菇采后贮藏保鲜研究存在的主要问题,从物理保鲜、化学保鲜和生物保鲜3个方面综述了国内外草菇保鲜技术的研究进展,并对其研究方向进行展望,以期为草菇采后贮藏保鲜的研究与生产应用提供一定的参考。     

Comparison of a one- and two-stage mixed model analysis of Australia’s National Variety Trial Southern Region wheat data

A one-stage analysis of a series of variety trials involves a combined analysis of the individual plot data across trials. Together with prudent modelling of the genetic effects across trials, this is considered to be the gold standard analysis of multi-environment field trial data. An alternative is a two-stage approach in which the variety means from an analysis of the individual trials in stage one are combined into a weighted mixed model analysis in stage two to give the full set of predicted variety by environment effects and an estimate of their associated variance structure. The two-stage analysis will exactly reproduce the one-stage analysis if the full variance-covariance matrix of the means from stage one is known and is utilised in stage two. Typically the full matrix is not stored and a diagonal approximation is used. This introduces a compromise to the full analysis. The impacts of a diagonal approximation are greater in the presence of sophisticated models for the genetic effects. A second compromise is through a loss of information in estimating the non-genetic variance parameters using the two-stage approach. In this paper we draw a direct link between the one and two-stage analysis approaches for crop variety evaluation data in Australia. We now have the computing power to analyse large and complex multi-environment variety trial data sets using the one-stage approach without the need for a two-stage approximation. This should motivate a move away from the two-stage approach in a range of contexts.     

草莓采后灰霉病控制研究进展

介绍了草莓灰霉病菌的生物学特性,从物理防治、化学防治和生物防治三个方面综述了草莓采后灰霉病控制方法的研究进展,以期为草莓采后灰霉病的控制方法和研究方向提供参考。