Irwin R. Donis-González

Classification of processing asparagus sections using color images

Computer vision and pattern recognition tool to determine asparagus sections.Essential information to sort asparagus based on their section is offered.Method that could be applied in an in-line quality sorter of asparagus. Impartial classification of Asparagus sections (Asparagus officinalis L.), for the purpose of obtaining desired tip to stem pieces ratio in final product, is extremely important to the processing industry. Thus, there is a need to develop a technique that is able to objectively discern between tip and stem pieces, after asparagus has been processed (cut). In this article, a computer vision methodology is proposed to sort asparagus into three classes: tips, mid-stem pieces and bottom-stem pieces. Nine hundred and fifty-five color images from 50mm length asparagus pieces (cuts) for the three different classes were acquired, using a flat panel scanner. After preprocessing, a total of 1931 color, textural, and geometric features were extracted from each color image. The most relevant features were selected using a sequential forward selection algorithm. Forty-three features were found to be effective in designing a neural-network classifier with a 4-fold cross-validated overall performance accuracy of 90.2% (ź2.2%). Results showed that this method is an accurate, reliable, and objective tool to discern between asparagus tips, mid-stem and bottom pieces, and might be applicable to in-line sorting systems.

Quantification and identification of microorganisms found on shell and kernel of fresh edible chestnuts in Michigan

BACKGROUND Chestnut is a relatively new cultivated crop for Michigan, and postharvest loss due to decay has been problematic as production has increased each year. In 2007, more than 25% of chestnuts were lost to postharvest decay, equivalent to approximately 5300 kg of fresh product. To determine the organisms responsible for decay, a microbiological survey was performed in 2006 and 2007 to identify microorganisms involved in postharvest shell (external surface) mold and internal kernel (edible portion) decay of chestnuts. RESULTS Filamentous fungi including Penicillium expansum, Penicillium griseofulvum, Penicillium chrysogenum, Coniophora puteana, Acrospeira mirabilis, Botryosphaeria ribis, Sclerotinia sclerotiorum, Botryotinia fuckeliana (anamorph Botrytis cinerea) and Gibberella sp. (anamorph Fusarium sp.) were the predominant microorganisms that negatively impacted fresh chestnuts. Populations of microorganisms varied between farms, harvesting methods and chestnut parts. CONCLUSION Chestnuts harvested from the orchard floor were significantly (P < 0.05) more contaminated than chestnuts harvested directly from the tree, by more than 2 log colony-forming units (CFU) g(-1) . In addition, a significant difference (P < 0.05) in the microbial population was seen between chestnuts submitted by different growers, with average count ranges of fungi, mesophilic aerobic bacteria (MAB) and yeasts equal to 4.75, 4.59 and 4.75 log CFU g(-1) respectively.

Differentiation of decayed and healthy tissue in fresh Michigan chestnuts using X-ray computed tomography (CT) imaging

In Michigan where chestnut (Castanea spp.) cultivation is a pioneering industry, mold and physiological kernel decay, commonly referred to as internal disorders, are responsible for significant economic and quality losses. A floating technique using differences in specific gravity is currently used by growers for the non-destructive separation of decayed and healthy chestnuts; however it is not a reliable procedure. Thus, there is a need to develop an accurate nondestructive technique that is able to assess internal chestnut disorders. Computed tomography (CT) was used to obtain transversal two-dimensional (2D) images from the interior region of decayed and healthy fresh chestnuts, from the hybrid cultivar ‘Colossal’ and ‘Chinese seedlings’. Attenuation coefficients, referred to as Hounsfield-units (HU) or CT numbers, were acquired from air, decayed, and healthy tissue, as well as from various imperfections such as pellicle invagination and void spaces from different 2D-images. Results suggest that HU-measurements of fresh intact nuts can be used as a nondestructive indicator of the internal quality of chestnuts.