Bryan W. Maw

CLASSIFICATION OF SWEET ONIONS BASED ON INTERNAL DEFECTS USING IMAGE PROCESSING AND NEURAL NETWORK TECHNIQUES

Maintaining product quality is the key to success in the fresh fruit and vegetable market. Quality assessment with computer vision techniques is possible; however, two basic issues need to be addressed before an automatic sorting system can be developed: (1) which image features best correlate with the product quality, and (2) which classifier should be used for optimal classification. To address these issues, sweet onions were line–scanned for internal defects using x–ray imaging. Spatial and transform features were evaluated for their contributions to product classification based on internal defects. The Bayesian method was used for selecting the salient features. Spatial edge features combined with selected discrete cosine transform (DCT) coefficients proved to be good indicators of internal defects. A neural classifier performed better than the Bayesian classifier for sorting onions into two classes (good or defective) by achieving an overall accuracy of 90%. Losses and false positives were limited to 6% and 10%, respectively. The accuracy, losses, and false positives for the Bayesian classifier were 80%, 16%, and 17%, respectively.

Physical and mechanical properties of fresh and stored sweet onions

Some physical and mechanical properties of a sample of ‘Granex-Grano’ type sweet onions were examined in an attempt to more deeply understand the conditions under which those onions may most appropriately be grown, harvested, stored, processed, shipped, and marketed. Those onions evaluated had a mean mass, surface area, volume, and density of 98 g, 111 cm2, 95 cm3, and 1 100 kg/m3, respectively. The overall mean equatorial diameter was 62 mm and the mean polar diameter was 42 mm. Of the mature onions examined, 59% of the onions were oblate and 41% were prolate. Crushing load and puncture force are indications of mechanical strength of the onions to withstand mechanical harvesting and postharvest handling. Onions evaluated from this study had a mean crushing load of 26.4 N and puncture resistance of 25.0 N.

EXPERIENCES WITH A FOOD PRODUCT X-RAY INSPECTION SYSTEM FOR CLASSIFYING ONIONS

Maintaining product quality is critical for success in fresh fruit and vegetable marketing. Some onion packinghouses are considering the addition of x-ray inspection systems to their existing optical inspection systems. X-ray systems enable detection of voids that are likely to be associated with the presence of various bacterial or fungal rots in onions. A series of tests were conducted at the University of Georgia Vegetable and Vidalia Onion research and education center in Toombs County, Georgia, with a commercially available x-ray inspection machine. In 2001, two 100-onion batches of medium-sized onions and 100 jumbo onions were machine-inspected, and then halved for a visual internal evaluation. In each series of tests, the accuracy rate was greater than 93% and the false positives were less than 6%. In 2002, two 100-onion batches were run on a similar machine as in 2001. Additionally, in 2002 and 2004, multiple onions with slight to severe defects were each passed through the inspection machine 50 times, respectively, with orientation not controlled to ascertain consistency in defect detection. The machine passed onions with no to slight defect presence (based on subsequent internal visual evaluation of onion halves) nearly 100% of the time. Onions with severe defects were rejected 100% of the time. In 2004, a center rot disease (caused by Pantoea ananztis) study showed that 80% of bulbs that had passed a routine surface inspection and had been deemed to be diseased by the machine in fact exhibited the disorder on halving. False positives were in the 10% to 15% range. In the 2002 and 2004 studies, the machine detected bulbs with disease that passed human visual inspection (HVI) and (in 2004 only) individual tactile grading. These accuracy and false positive rates are very close to the 90% and 10% levels generally accepted for these respective statistics. With appropriate addition of multiple lanes, commercially viable throughputs are possible.

ARTIFICIALLY CURING SWEET ONIONS

An index of cure was developed and used to evaluate the extent of curing of sweet onions as influenced by different treatment effects of the maturity of onions at harvest, the duration of cure and the depth of onions in the stack during curing. In order to obtain a complete cure, the required duration of cure varied most of all with harvest maturity. For onions harvested at an optimal maturity, 48 h was necessary to obtain a good level of curing. A small benefit was observed when curing was extended to 72 h. For onions harvested after the optimal maturity, 24 h was sufficient. However, onions harvested before the optimal maturity were not fully cured after 72 h. Depth of onions influenced curing because the drying front moved through the stack in the direction of the airflow and curing was not complete for the whole stack of onions until the drying front had completely moved through the onions. A similar value of curing index may be procured from different combinations of harvest maturity, depth of cure and period of cure.

HIGH-TEMPERATURE CONTINUOUS-FLOW CURING OF SWEET ONIONS

A study was undertaken to investigate the feasibility of heat-treating sweet onions under controlled commercial conditions. Three test runs were conducted whereby approximately 4 m3 of onions for each test were passed through a continuous-flow drier. Set-point temperatures of 43.C, 43.C, and 46.C and durations of heat treatment of 17, 24, and 24 h were used, respectively, during the three tests. Samples of heat-treated onions were taken from the dryer at regular intervals and, after prescribed storage intervals, were inspected for the presence of Botrytis allii with the aid of a dye. The increase in disease was calculated. There was a significantly (P < 0.01) less increase in disease during storage for those onions having received heat treatment compared with onions conventionally cured. In comparing the least square means, 24 h of heat treatment resulted in a lower incidence of disease than 17 h. Similarly, a set-point temperature of 46.C resulted in a lower incidence of disease than 43.C . Based upon the results of the study, a combination of heat treatment and conventional curing was recommended.

THE INFLUENCE OF HARVEST MATURITY, CURING AND STORAGE CONDITIONS UPON THE STORABILITY OF SWEET ONIONS

Granex onions were harvested during three seasons at three levels of maturity of either early, optimum or late, cured for three durations of either 24, 48, or 72 h and stored in an air-conditioned environment of either a high or low humidity under a common temperature. Storability was measured by the rate of decay of onions in storage. When stored in an air-conditioned room, sweet onions eventually decayed, but their rate of decay was influenced by the treatments. Overall storability: diminished more rapidly beyond 10 weeks than before 10 weeks; was higher for early harvested onions; was enhanced by 48 h of curing; and was enhanced by an environment of low humidity. Storability by harvest maturity: was not enhanced by curing beyond 24 h for late and 48 h for optimal harvest onions, but for early harvested onions storability was enhanced by curing as much as 72 h. Of all treatments, harvest maturity had the greatest influence upon storability. Even though onions of an early harvest maturity had a higher storability than other maturities of harvest, the onions had not reached their highest quality nor their maximum yield and so the optimal harvest maturity was a compromise between yield, quality and storability.

Nondestructive testing for identifying poor-quality onions

Methods of nondestructively examining Granex type sweet onions are needed to insure that only good quality onions are shipped at harvest and to avoid putting infected onions in controlled atmosphere (CA) storage where they occupy valuable space and can ruin surrounding onions. A Toshiba TCT 20Ax tomographic scanner operated in the line scan mode and an incandescent light box were used to evaluate the potential for detecting infected onions nondestructively. A study (CA storage study) involving 200 onions, 100 harvested early and 100 harvested late, one half destructively inspected before the remaining half were placed into CA storage was initiated May 1994. All onions were line scanned and scored with the light box before CA storage and those in CA storage were line (will be) scanned and optical scored on retrieval from the storage. An additional study (Disease storage study) involving 40 onions, late harvest, stored at 25C, 60% rh for three weeks with line scanning as above on a weekly interval. After the third week these fruit were assayed for visual damage and for decay organisms. Results from the incandescent light box scoring were not encouraging. From both studies the number of defects, average defect size and the difference image intensity as determined from line scanning were the major contributing parameters to a discriminant analyses model predicting about 70% or better accuracy.

Enhancing the Performance of the CPES Sweet Onion Harvester

Modifications have been made to the CPES sweet onion harvester in order to improve performance. Provision has been made for some accommodation of weeds in the crop, for preliminary grading and sorting of onions, and for handling harvested onions in correspondence with existing farming operations. Onions have been successfully cured in the same plastic pallet bins as were used for collecting onions on the harvester.

Technical Notes: Detecting Impact Damage of Sweet Onions Using Muriatic Acid and X-rays

Damage received by ‘Granex-Grano’ type sweet onion bulbs may not be clearly evident with the naked eye because the effect of damage may be internal rather than on the surface of the onion. Damage may result from disease, insects, or mechanical means. Considering mechanical damage, layers of dry skin on the surface of an onion may shield the surface while transmitting an impact force to within the onion bulb. Methods of nondestructively examining sweet onions are being sought. Both a muriatic acid dip test and an X-ray CT have been found to provide some indication of damage under certain conditions. For freshly harvested sweet onions, muriatic acid has been found to cause the flesh of a sweet onion to appear yellow and become a sticky gel in the vicinity of damage, thus enhancing the visibility of damage. X-rays have been found to aid in viewing the internal structure of a sweet onion and thus damage that may have occurred. These techniques may lead to a further understanding of how sweet onions should be cared for in order to keep damage to a minimum.

NON–MELTING–FLESH PEACHES RESPOND DIFFERENTLY FROM MELTING–FLESH PEACHES TO LASER–PUFF FIRMNESS EVALUATION

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.