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Peaches (Prunus persica) were evaluated for storability after dipping in a 2% calcium chloride (CaCl2) solution at 20 °C for 30 min and storing them at 4 °C for 2 weeks in boxes uncovered or covered with polyethylene bags. Generally, there were significant improvements in storability resulting from the CaCl2 and the polyethylene barrier. The CaCl2 treatment improved firmness, largely maintained the soluble solids content, and increased the ratio of soluble solids-to-titratable acid ratio. Polyethylene bags minimized weight loss, and two out of three times, bagged fruit had lower acids after storage than did the control.

Most current research is on technical issues and does not adequately integrate modern business management principles. Integration is also limited with respect to: the number and types of disciplines; interactions among research, outreach, and instruction personnel; and collaboration of researchers with industrial partners. Efforts should be made to expedite deliveries in addition to research on extending shelf life. Expanded use is needed of theoretical frameworks such as systems thinking and simulation models. Teams should include researchers from business schools who specialize in systems thinking, quality management, and supply chain management. Funding agencies should require a systems specialist on teams rather than require that all projects incorporate detailed systems content. Future improvements in food supply chains will require: creative pedagogical methods for instruction at the undergraduate and graduate levels, expanded integration of business leaders with researchers, and collaborations among business schools with technologists. Expanded interactions are needed with professional societies and associations specializing in systems and supply chains. Projects should be developed with input from business leaders from supply chains. Symposia such as MQUIC should devote sessions to developing projects using systems thinking.

The firmness of blueberries and other small fruits can be measured rapidly with a FirmTech 2 Fruit Firmness Tester. The output of the instrument is the slope of a chord joining selected minimum and maximum forces on a force/deformation curve. The overall objective of this study was to improve the firmness measurements obtained with the Firm Tech 2. In this study, both simulations and tests with rubber balls showed that firmness measurements of a single ball increased as minimum or maximum force was increased. If repeatable results are to be obtained from the FirmTech instrument, standard values for the force settings must be established. Equations from ASABE Standard S368.4 were adapted for calculating the apparent modulus of elasticity (Ea) for both parallel plate and single plate compression using FirmTech 2 output. Ea values for a simulated force/deformation curve with deformation taken to the 3/2 power were the same when calculated using the ASABE equations and when calculated using the equations developed, regardless of which combination of minimum and maximum force was selected. When rubber balls with three diameters were compressed with a universal testing machine, the Ea values calculated from the ASABE equations were similar to those calculated using the equations developed in this study. The firmness of blueberries was measured using both the FirmTech 2 and the universal testing machine. The Ea values were calculated using the equations developed in this article and compared to values calculated from the universal testing machine curves using the ASABE equations. Results indicated the loading applied by the FirmTech 2 support plate was somewhere between a parallel plate and a single plate compression test. Additional research could show benefits from using the equations developed in this study for calculating Ea from loading curves obtained using universal testing machines.

Postharvest bruising of peaches might be reduced by increasing fruit firmness. This study evaluated changes in firmness resulting from dipping peaches in a calcium solution soon after harvest. Cartons of 'Harvester' and 'Juneprince' peaches were obtained from a commercial packinghouse from three harvests each. Half were dipped for 30 minutes at room temperature in a 1% calcium chloride solution and held at 0, 4, and 10°C and 95% relative humidity. Non-destructive tests (firmness, mass, and color) were made five or more times during 21 days of storage on 180 fruits from both varieties (30 fruits x 3 temperature x 2 calcium levels). Firmness was from parallel plate deformations at 10 Newtons. Mass was measured to a resolution of 0.001 grams. Destructive tests were made on 15 fruits from each treatment: total soluble solids (% Brix), acidity (pH), firmness (Magness-Taylor), and taste. Firmness (non-destructive and destructive) during storage was higher for fruit treated with calcium than for untreated fruit. However, temperature affected firmness more than the calcium treatment. Calcium treatments increased mass loss only on 'Harvester' fruit stored at 10°C. The main differences in TSS were due to days of storage with little influence caused by the calcium treatments. An undesirable taste was noted in the fruit that was treated with calcium. A study is needed to determine if a lower concentration of calcium chloride could provide beneficial increases in firmness without changing the taste of the fruit. The effect of temperature on calcium absorption should also be studied.

Research was conducted to understand the effect of harvesting method and postharvest temperature on fruit quality of ‘Brightwell’ and ‘Tifblue’ rabbiteye blueberries (Vaccinium ashei Reade). Mass loss, firmness and fruit respiration were measured under refrigerated (1°C) and ambient (22°C) conditions for both machine harvested (MH) and hand harvested (HH) berries of each cultivar. Few differences were found between cultivars for the various postharvest quality parameters. Firmness of MH blueberries was diminished by an overall average of 36.2% as compared to HH berries. MH berries placed at 22°C had a mass loss rate of 2.6% per day as compared to 0.2% per day at 1°C during the first two days of storage. Respiration rate of MH berries at ambient temperature was 31.1% higher than HH berries. Refrigeration reduced respiration rates by an overall average of 79%. Refrigerated HH blueberries had the best overall fruit quality after storage, and firmness measures suggested that HH berries had an extende...

A three-factor, three-by-three-by-two-level factorial designs were used for studying the effects of air pressure, sprayer orifice size and electrostatic charge of a spray gun on pH, oxidation-reduction potential (ORP), electric conductivity and residual chlorine of electrolysed oxidizing (EO) waters with either low (9 mg L -1 ) or high concentration (88 mg L -1 ) of chlorine. Results indicated that a smaller orifice produced higher reduction in ORP and chlorine concentration than larger orifices. Electrostatic charge, in general, did not cause a significant reduction in chlorine concentration. High air pressure spray retained more chlorine and gave a higher ORP than low air pressure. EO water with high initial chlorine concentration achieved at least a 3-4 log 10 CFU mL -1 reduction in Listeria monocytogenes populations when sprayed with the spray gun, while spraying with a commercial backpack sprayer or a poly-tank sprayer eliminated Listeria population (9.4 log 10 CFU mL -1 reductions) completely. These results demonstrated that although spraying reduced the chlorine in EO water by 20-97%, application of EO water through spraying has potential for reducing bacteria in food-processing operations.

Blueberry mass and firmness losses were measured to determine the effect of cooling delays at selected temperatures in postharvest environments. Models were developed from the slopes of firmness and mass values plotted against time for four temperatures (4°C, 21°C, 27°C, and 32°C) and 95% relative humidity. The models were validated by holding other sets of blueberries at three temperatures (21°C, 27°C, and 32°C) and at 95% relative humidity for cooling delays of 0, 2, 4, 8, 12 and 24 h before storage at 4°C and 95% RH up to 96 h. The models developed were suitable for evaluating the benefit of lowering temperature during cooling delays of selected durations or the benefit of reducing cooling delays at specified temperatures. The impact of cooling delays was less than expected when compared with losses caused by low-temperature storage for only four days. Additional studies are needed to model the effect of various relative humidity levels and to learn the extent of variation in the rate constants for different cultivars and different crops.

Models for predicting blueberry mass and firmness losses during distribution through supply chains were developed from storage studies. The models were then applied to each link in a typical supply chain for fresh produce. The initial mass and firmness at harvest and the time and temperature in the field were the values used for calculating the loss percentages for the first link. Inputs to subsequent links were the outputs from the previous links. Models and calculations were made in a computerized spreadsheet (Microsoft Excel). A noteworthy result of the model is the visibility of rapid mass losses when simulating short times at high temperature at an open dock and in a car. Also, the rapid initial decrease in mass and firmness indicates the importance of reducing delays before cooling. Finding the horizontal distance between the two curves shows the benefit of low temperatures. Similar models are being developed for other crops. INTRODUCTION In commercial handling, shelf life of produce may vary greatly from conditions found as ideal in laboratory studies. Supply chains rarely have the facilities to store each commodity under ideal conditions and requires handlers to make compromises. Inventory management and marketing determines how a product will be handled more than the conditions that would be best for the product (Prussia and Shewfelt, 1993). Fresh produce receives temperature abuse at varying degrees and various time periods across the different links of the supply chain. The temperature abuse makes the shelf life of the produce different than what is predicted based on laboratory studies at the recommended storage temperature of that particular fruit or vegetable. Paull (1999) questions the value of laboratory studies that do not approximate commercial practices and have led to unattainable recommendations for shelf life. Blueberries like other fresh produce pass through various links of a refrigerated or non-refrigerated supply chain from the farmer's field to the consumer's plate. Managers making decisions on storage times and temperatures do not have the tools necessary for determining how conditions at their link of the supply chain will affect the quality of blueberries when purchased or consumed. Quality management of fresh fruits and vegetables has been limited by the lack of mathematical models to predict quality changes during expected storage life (Thai et. al. 1991). Paull (1999) states the research needed for shelf life predictions is to include the knowledge of temperatures at which products are exposed. Nunes and Emond (2002) have developed quality curves for ‘Patriot’ cultivar blueberries as a function of the storage temperature. We have reached a time when managers of links in supply chains could have a simple tool where they can input the temperature histories of the product to predict changes in quality. Several software packages are available commercially to simulate production and distribution chains of food products. However most of them simulate a small part of the supply chain. Models, which could predict the mass, firmness or other shelf-life quality parameters of blueberries over the whole chain, based upon its temperature-time history, could not be found in literature. The objective of the present study was to develop a computerized model, which can simulate the mass and firmness of several cultivars of blueberries based upon their temperature histories. The model should be user friendly and Proc. Int. Conf. Quality in Chains Eds. Tijskens & Vollebregt Acta Hort. 604, ISHS 2003 180 run on widely available and affordable software for personal computers. MATERIAL AND METHODS The overall approach was to find reaction rates for changes in mass and firmness with one set of blueberries and then validate the resulting models with a second set. The resulting equations were then the basis of the simulation program for the whole supply chain. Modeling Mass and Firmness Changes for Selected Constant Temperatures Seven cultivars of blueberries were hand-harvested at typical commercial harvesting maturity level at the Horticulture Experimental Farms in Griffin, Georgia. The cultivars were Yadkin, Baldwin, Climax, Brightwell, Tifblue, Powderblue and Premier. Experiments began within one hour of harvest. The blueberries from a cultivar were randomly sorted into 4 treatments of 24 blueberries. Each measurement was of mass and firmness. Initial firmness and mass were measured and the treatments were placed into 1°C, 12°C, 22°C, and 32°C storage. The treatment stored at 32°C was measured every 12 hours, the treatment stored at 22°C was measured every 24 hours, 12°C was measured every 48 hours, and 1°C every 72 hours. Measurements continued until over 30% of a treatment became inedible or until firmness began increasing due to high water loss and the formation of raisin-like blueberries. The blueberries were stored in walk-in coolers, which were set at the temperatures desired for this study. All storage temperatures were monitored by data loggers and found to be within a range of +1°C of set temperature. Relative humidity of the air surrounding the fruit was maintained at 95% for all temperatures. Firmness (g/mm, force per unit deformation from force-deformation curves) of each fruit was measured non-destructively with a compression testing instrument, FirmTech II (Bioworks, Inc.) (Perkins et al, 2002). The instrument is designed to rapidly provide firmness values for small fruits such as cherries, blueberries and grapes. The instrument was set at a maximum compressive force of 150g and a minimum force of 50g. Load cell and table speed was set to 7mm/second and 0.28 rotations/minute respectively. Tests showed that the average coefficient of variation of the instrument was 1.7% for seven rubber balls whose firmness was measured 33 times under identical conditions. The berries were weighed to determine mass with a Mettler-Toledo PR 503 balance (Max mass 510g and resolution 0.001g). Mass measurements were taken before firmness measurements with the balance inside the 12°C walk-in cooler to minimize error from condensation. Linear regressions were applied to plots (not shown) of percentage mass and firmness verses time, for each storage temperature. These provided slopes for the percent rate of change for each temperature. The obtained slopes are given in Table 1 and Table 2. These slopes were then plotted against various respective temperatures (Figure 1 and Figure 2). Exponential regressions were applied to these plots and equations for modeling of mass and firmness loss were developed. A general linear equation (Thai et al., 1990) was adapted to model percent mass and firmness loss. The equation for mass can be written as: Mass (t) = Initial Mass (t=0) – ρ t (1) Where: t = time in hours ρ = rate of change Validation of the model Blueberries of all the seven cultivars were hand harvested at the UGA experiment station in Griffin. After harvesting, the blueberries were immediately placed in 1°C storage for transport to the laboratory. Experiments began within one hour of harvest. The blueberries from Climax and Premier cultivar were randomly sorted into two

The public benefits from a basic understanding not just of science but of engineering. However, people understand the scientific method better than the engineering approach, although they sense that the two are different. Simple memory tools could help teachers and students not only understand but remember and appreciate the different uses of the two approaches in understanding our world and in changing it. Checkland's concise description of the scientific method using three key words of reduce, repeat, and refute is proposed as a tool for remembering key components of the scientific method. A similar memory tool for the engineering approach could help teachers and students understand and remember the basic differences between and the appropriate uses of the two approaches. A three-word memory tool is proposed: determine, develop, and deploy. Our purpose is to elicit reaction to the conceptual learning tools proposed.

X-ray linescanning is beginning to be established as an alternative tool for non-destructive produce inspection when internal defects are expected. Image analysis based techniques are being increasingly used for quality evaluation and classification of fresh fruits and vegetables. However, noise removal and feature extraction techniques suitable for particular applications need to be identified before a classification system can be developed. This study focuses on the design of prefiltering steps which will lead to optimal defect detection. Autocorrelation and variogram analyses were performed on water CT scans, water linescans, and plexiglass linescan images for purposes of quantifying instrument related noise. Little correlation was observed in water CT and plexiglass linescan images, which implied that a Gaussian filter may be appropriate for noise removal from linescan images of fruits prior to removal of edge features associated with defects such as bruises. A 5 iA5 Gaussian kernel gave significantly (P iU 0.05) better discrimination based on experimental results for enhancing bruise features in apples and disease features in onions. This result confirmed a rule of thumb in terms of filter design based on image statistics. In instances where morphological operators were used, prefiltering masks were not found to be significant at the 5% level. There was a strong correlation between the image signal and pixel location in linescans of water with a free surface. This correlation is believed to be caused by waves due to motion, suggesting a need for frequency domain filtering in the case of water transport.

Much has changed in postharvest handling in the two decades since the first edition of this book was published, and even in the few years since the second edition appeared. Important trends for the next decade in postharvest handling of fruits and vegetables include the growing recognition of the beneficial nutritional properties of fresh fruits and vegetables, greater attention to marketing and consumer desires by produce businesses, increasing incomes and information flows, more sophistication of international distribution chains, greater attention to prevention of food-borne outbreaks and recognition of the need for more sustainable practices throughout production and handling. With all of these opportunities, the future looks bright for handlers of fresh produce, and a systems approach is becoming even more critical to meet the new challenges during postharvest handling.

Since the publication of the first edition of this book, more researchers in postharvest handling of fresh fruits and vegetables are gaining an appreciation of the need to use a systems approach. A common handling system proceeds from crop production to harvest, packing, transport, storage and retail distribution. Much progress has been made in integrating handling operations, but increased demands for higher quality are forcing the need for the further integration of handling steps. The greatest challenges in postharvest systems research include a better understanding of the effect of stress conditions on quality, development of more effective means to manage quality, connecting marketing efforts to handling, ensuring safe fresh fruits and vegetables, more research needed for handling systems with limited infrastructure and more expansive studies at the preharvest/postharvest and postharvest/consumer interfaces.

In any agricultural supply chain, a proportion of the product grown does not meet the quality demanded by the primary buyer. Nonconforming product must be left in the field, disposed of, or sold to a different outlet. While in some cases, pickers can perform quality determinations in the field and only harvest saleable product, generally the product is subjected to a subsequent sorting operation. This is the primary quality determining step in the supply chain. Establishing optimum design and operating conditions for a sorting process is recognized as being critical in supply chains. Sorting controls the quality of product that is supplied to the market and therefore influences potential pricing. Sorting decisions also affect latent (downstream) quality as product selected determines the potential coolchain and transport performance of the products shipped (e.g., if all rots are not removed). Finally, sorting involves the removal of some product from the supply chain and the removal of marketable product, or misclassification of product can have economic consequences, particularly for growers. The analysis of the sorting operation involves establishing the effectiveness of machines or humans to inspect and remove defective product. Sorter productivity and accuracy are a function of the design and operational parameters of the process, the quality of the incoming product, and the grade standards required.

Instrumental measurement of color places hue in a 360° continuum, but sensory description of color usually refers to distinct hues - red, green, yellow etc. Color evaluation of «Golden Delicious » apples by both an instrumental method and an experienced sensory panel was conducted to determine if sensory panelists perceive changes of golden apple color in green and yellow hues separately or on a hue continuum from green to yellow. Apple color changed from green to yellow during storage. Two different sensory scoring ballots were used to determine if sensory panelists view color in discrete green and yellow hues or on a green-to-yellow continuum. Data collected from this sandy demonstrated that sensory panelists can detect the color changes of decreasing greenness, increasing yellowness, and color change from green to yellow in an equal fashion. Hue angle represented the best predictor of sensory color perception

X-ray computed tomography (CT) was used to image interior regions of ‘Red Delicious’ apples under varying moisture and, to a limited extent, density states. Images were actually maps of x-ray absorption of fruit cross-sections. X-ray absorption properties of ‘Red Delicious’ apples were evaluated using normal apples alternately scanned and sequentially freeze dried, fruit affected by watercore disorder, and normal apples freeze-dried to varying levels. The studies were designed to allow quantification of the x-ray absorption coefficient associated with the dry solids portion of the fruit and the x-ray absorption coefficient associated with moisture. The coefficients associated with moisture were in the vicinity of 0.0191 mm–1 and 0.0182 mm–1, the expected value for water and ice, respectively. The coefficient associated with the dry solids was not significant from zero, due in part to scanner resolution limits, limited dynamic range in density values, and to variation in the physical density measurements. The results of this study suggest that internal differences in x-ray absorption within scans of fruit cross-sections are largely associated with differences in volumetric water content.

Publisher Summary This chapter discusses the importance of the sorting operation to postharvest supply chains and also mentions visual quality attributes. The sorting operation must be viewed in the context of overall postharvest supply chains. It is important to understand how cultural practices and uncontrolled inputs, such as weather, cause variation of quality in products that enter the packing house. Likewise, at the time of shipping, it is important to be able to predict the quality of shipments as they progress through the rest of a postharvest supply chain. A major function of a packing house is to transform the highly variable product received from the harvesting operation into uniform lots of product for shipments that comply with the requirements of the buyer. The importance of the sorting operation cannot be overstated, since variations in this operation will affect returns for most other parts of postharvest supply chains. The ability of humans to perceive a visual image depends on both physical and cognitive factors. Changes in the color and intensity of light alter the image received by the eye. The method of presenting the product to the sorters also has an important effect on perception. If product speed is too fast, it is not possible to fixate properly on a defect and hence, it is not possible to reach a decision about whether or not the item should be rejected.

AN expert system prototype (LETTMAN) was developed to simulate the decision-making process of a produce manager in a retail store. Lettuce was the commodity studied and a produce manager served as the domain expert. From several interview sessions with the domain expert, a flowchart was developed to illustrate the decisions considered, and a knowledge base was created using the production rule language of the expert system shell Insight2+. Results of the study showed that quality determination, price adjustment, potential sales and inventory control were the primary decision areas. Evaluation of LETTMAN indicated the need to incorporate reasoning schemes other than confidence factors in modeling lettuce quality decisions.

The delivery of high quality agricultural products is more essential than ever before due to increasing consciousness of quality by consumers. The best way for successful marketing is to offer products of superior quality and consistency. Quality is defined as conformance to a customer's price limited anticipated needs. The needs of customers are dynamic, so strategies must involve continuous improvement and decreasing variability. Agricultural production and marketing can be considered an industry, so appropriate industrial engineering (IE) techniques can be practiced for improving quality. It is necessary to control principal stages by following a systematic approach which includes: clearly defining specifications, providing measurement methods, and recording and analyzing data for decreasing variability through Statistical Process Control (SPC) technique. The prime method for controlling the quality of agricultural products as they flow through the marketing system is with statistically valid sampling at changeover points to determine if processes are in control and for identifying corrective actions. Flow chart techniques are very useful for selecting the best sampling points in the flow of fresh fruits and vegetables to market. Use of other SPC techniques may be considered such as: sampling plans, frequency distributions and histograms, graphs, Pareto analysis, cause and effect analysis, scatter diagrams, and charts. Results may be used for improving processes, corrective actions, or pricing decisions according to quality evaluation.

The theory of signal detection was applied to existing data to assess possible applications for inspecting of agricultural products. A computer program was developed that successfully determined the value of hit rate, Z score of the hit rate, false alarm rate, Z score of the false alarm rate, detectability, bias, and an average value of each given only the number of hits and false alarms. Receiver Operator Characteristic curves were generated showing a significant increase in inspector performance when the product moved directly towards the inspector compared to moving laterally.

A laser–puff firmness tester (puff of air with deformation read by laser beam) has been found to differentiate over time, non–melting–flesh from melting–flesh peaches. Non–melting–flesh peaches maintain flesh integrity during ripening rather than exhibit the characteristic softening or ‘melting.’ Peaches of various cultivars and of flesh type melting or non–melting, were harvested on 28 May, 5 June, 10 June, 17 June, and 30 June 1999, according to when they were ripe. Each fruit was successively examined over approximately 20 days, being held in air–conditioned storage (20.C, 70%RH) between examinations. When examined with the laser–puff tester, non–melting peaches exhibited an increasingly greater surface deformation over time compared with the deformation of melting–flesh peaches. Even though the deformation increased, the natural rate of decay was observed to be the same as for melting–flesh peaches and the increase was attributed to the non–melting–flesh peaches having a resilient or rubbery surface texture. By having this texture, non–melting–flesh peaches may experience less bruising during harvesting and post–harvest handling than occurs in melting–flesh peaches.

Despite the progress in the development of simulation games and models for training in many areas, the post harvest produce sector seldom has been a subject of instructional tools. The game developed with produce sector in mind accounted for the complexity of the sector combining the systems dynamics and the systems thinking to model and simulate the essential elements responsible for meeting consumer demand. The game provides a participatory, hands-on experience to understand the concepts and develop skills desired in the post harvest fresh produce industry. A computer-mediated simulation game for this objective was developed in Windows environment. It uses the Stella software (a systems thinking software) to model the system and to develop the simulation equations. The game combines systems thinking and system dynamics to simulate essential relationships between links in the produce delivery. It models the critical elements of the post harvest system of fresh produce retailing faced by the manager of the produce department in a large retail outlet. The simulation emphasis the importance of correct ordering based upon deterministic and stochastic consumer demand, the delays in produce delivery by the system, perishability and a measure of financial assessment of the retailer performance. It can be used as a teaching tool by marketing, postharvest engineering and biology students and as a training tool by the produce department personnel in retail outlets to understand the dynamics of the post harvest marketing system.

Blueberry mass and firmness losses in postharvest environments were measured to determine the effect of cooling delays at selected temperatures. Models were developed from the slopes of mass and firmness values plotted against time for four temperatures (4, 21, 27 and 32 °C). The models were evaluated by holding other sets of blueberries at three temperatures (21, 27 and 32 °C) and at high relative humidity (95 %) for cooling delays of 0, 2, 4, 8, 12 and 24 hours before storage at 4 °C for 24 or 96 hours. Good correlations among predicted versus observed data showed that it was possible to model the mass and firmness losses of blueberries at different temperatures and cooling delays. The impact of cooling delays was less than expected when compared with losses caused by low temperature storage for only four days.

Postharvest market decisions for apples could be improved if nondestructive tests were available for accurately predicting eating texture which is the ultimate test. Texture is commonly predicted by human touch and color evaluation and by an instrument to measure penetration force. In this study the nondestructive methods of CT-Scan X-Ray and coefficient of restitution along with eight other quality measures were correlated to eating texture and to each other. The best correlations to eating texture were for the puncture test without skin (0.70) followed by firmness to the touch (0.54). Poor correlations were obtained for the new methods (CT-Scan = 0.31, coefficient of restitution = 0.028). The highest overall correlation was between instrument and sensory hue angle (0.87), however neither correlated well with eating texture (0.54 and 0.47). The results indicate that more research is needed.

Experiments were conducted during 1999 at the Univ. of Georgia Research Farm near Alapaha with the rabbiteye blueberry (Vaccinium ashei Reade) cultivar Brightwell to determine how various harvesting and handling tactics influenced firmness. The research was facilitated by availability of a mechanical harvester and a commercial packing line. Firmness was determined with a FirmTech II firmness tester on fruit samples before and after cold storage. Fruit harvesting methods included machine harvesting in bulk, hand harvesting in bulk, and hand harvesting directly into clam shell containers. Assessment of precooling effects were made by comparing firmness of fruit that were placed immediately over ice after harvest to fruit that remained at ambient temperatures for 24 hours after harvest. Additional measurements were made to discern the effects of grading and sorting on fruit firmness. The data overall indicated that `Brightwell' fruit firmness was “acceptable” regardless of the harvesting and handling methods experienced. However, there were considerable firmness losses caused by the various procedures. The greatest loss in fruit firmness (20% to 25%) was caused by machine harvesting. This was followed by a 15% to 18% loss of firmness due to grading and sorting. Immediate cooling of fruit after harvest resulted in only a 8% to 12% increase in firmness as compared to keeping fruit at ambient temperature for 24 hours. These findings should be useful to growers and packers in targeting segments of their operations that can be manipulated to improve berry firmness and quality for fresh market sales.

A laboratory study was conducted to determine the effect that moisture loss can have on the loss of green color in southern peas. Green peas were subjected to storage conditions that created four vapor pressure deficits. Measurements of mass loss, color difference, hue angle, color value, and chroma gave evidence that moisture loss significantly affects the color changes of peas. The greatest changes in color occurred during the first 6 to 10 hours of storage. The tests suggested that color loss in bulk shipments of peas from the field to the processor could be reduced by ventilating the load with humidified ambient air.

Peaches (cv ‘Redglobe’) were sampled at six points in the postharvest handling system to determine the effects of physiological maturity at harvest and identify the step(s) where alternate handling techniques would lead to improved quality at point of purchase. Samples were evaluated for maturity, color, firmness, soluble solids, and total acidity. Harvest maturity and temperature management were identified as the most critical factors in the postharvest system. Firmness was the most limiting quality attribute. Improvement of peach quality can be achieved within the existing handling system by strict control of harvest maturity and allowing peaches to ripen at ambient temperatures (18–24°C) at the retail outlet or in the home just prior to consumption.

Peaches (cv. Red Globe) were divided into three maturity groups and stored at 4°C for specified lengths of time before being bruised by a pendulum impactor at three energy levels. Bruise volume (BV) and susceptibility (BS) were measured in the bruised peaches. Mechanical properties (puncture force, failure stress, and failure energy) were determined by using an Instron Universal Testing Machine. No significant changes in BS and BV were observed for fruit impacted up to 14 days of storage, then both BS and BV increased with storage time. The most mature peaches were more susceptible to bruising and had larger bruise volumes than the least mature peaches; whereas, the differences between least and medium maturity peaches were not significant. Puncture force, failure stress, and energy generally decreased with storage time and maturity. Both tensile stress and puncture force showed promise as an indicator for evaluating the bruise susceptibility of peaches to impact..

Manual harvesting has many advantages compared with the mechanical harvesting of most fruit crops. The most important advantage is visual image processing ability which enables workers rapidly to detect fruit suitable for harvest and direct their hand to the fruit selected for detachment. Lacking the necessary computer based image processing equipment, designers of mechanical harvesters have settled for mass removal approaches that typically results in more damage than normal when fruit is harvested individually. Although manual harvesting has the disadvantage of low capacity, it is expected that much of the world's fruit will continue to be harvested by hand for the foreseeable future. Several ergonomics principles that relate to manual harvesting are discussed. Methods for improving worker conditions and productivity are presented. Worker positioners increase productivity by 20 to 40% and enable use of sun shades, fans, conveyors and other devices that increase comfort and reduce fatigue. Testing and training can yield substantial benefits from small inputs. Tests for visual acuity, colour sensitivity, strength, etc, can help managers assign tasks to the most suitable workers. Training programmes help workers to have a clear mental picture of acceptable fruit and encourage compliance with handling, safety and other procedures. Satisfaction of human drives such as thirst, hunger, thermal comfort and avoidance of pain results in long-range benefits.

AN electronic digital timer was developed for measuring the velocity of a high speed (30 m/s) projectile used in a recently invented edible nut cracker. Tests showed that two retroflective type PIN photodiode sensors located 7.62 cm apart could consistantly start and stop the timer at velocities up to 45.9 m/s (102.7 mph). Sufficient details are given for others to build similar systems. An oscilloscope was attached to both diodes to enable evaluation of the timer accuracy. Thirty replications of thirteen combinations of projectile mass and nominal velocity showed there were no differences between velocities calculated from time data obtained from: diodes 1 and 2, timer display and oscilliscope, or diodes and timer display. Agreement between diodes 1 and 2 indicated the projectile did not change velocity in the 7.62 cm distance between diodes.