2006 Annual Report 1.What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? Techniques for detecting and removing undesirable product from agricultural product streams are a major concern for the food industry. The main objective of this project is to develop such techniques and use them as a basis for the sorting of agricultural commodities. The goal may be either mass inspection of the entire product stream, or sampling for quality control. Assessment for quality may be based on any of a number of physical or chemical techniques including x-ray, acoustics, visible light transmission or reflection, near infrared (NIR) spectroscopy, and laser light transmission. Image analysis and algorithm programming are a key element in the development of these sorting devices, as is material handling. The specific objectives over the five year life of the project are: (1) Develop a device to detect pits in dried plums for sampling purposes; Extend the principle to a non-destructive on-line method. (2) Develop and implement an x-ray system capable of detecting and removing apples infested with codling moth [Cydia pomenella (L.)] from the processing stream. (3) Develop and implement an x-ray system capable of detecting and removing apples afflicted with bitter pit disorder. This program falls within Component 1 (Quality Characterization, Preservation, and Enhancement) of NP306 (Quality and Utilization of Agricultural Products). The project focuses primarily on problem area 1b (Methods to Evaluate and Predict Quality). However, the project also includes elements of problem area 1c (Factors and Processes that Affect Quality) and 1d (Preservation and/or Enhancement of Quality and Marketability). 2.List by year the currently approved milestones (indicators of research progress) Year 1 (FY 2004) Complete imaging trials for detection of pits / fragments in prunes for sampling. Assemble and test prototype device for non-destructive real-time on-line detection of whole pits in prunes. Establish insect colonies for studies on detection of codling moth in apples using x-ray imaging. Develop scrolling program which uses x-ray images to simulate apple processing line. Acquire x-ray images of apples infested with codling moth for use with scrolling program. Year 2 (FY 2005) Development of prototype device for monitoring alignment of pitting needles on prune pitting machines. Development of device for sampling prunes for pits and pit fragments. Commercial testing of non-destructive prune pit detection device / implementation. Automatic recognition algorithm development - Codling moth in apples. Human recognition trials (Codling moth) using scrolling program. Acquire x-ray images of apples with bitter pit disorder and perform human recognition trials with the scrolling program. Year 3 (FY 2006) Testing of prune pit / fragment detection device (sampling). Testing of prototype device for monitoring alignment of pitting needles on prune pitting machines. Testing of automatic recognition algorithm (Codling moth). Development and testing of automatic recognition algorithm for detection of bitter pit disorder in apples. Year 4 (FY 2007) Commercial testing and implementation of prototype device for monitoring alignment of pitting needles on prune pitting machines. Commercial testing and implementation of prune pit / fragment detection device (sampling). Development of x-ray inspection unit for automatic inspection of apples for codling moth infestation and installation in processing plant. Development of x-ray inspection unit for automatic inspection of apples for bitter pit disorder. Year 5 (FY 2008) Commercial testing of x-ray inspection unit for automatic inspection of apples for codling moth infestation and installation in processing plant. Commercial testing of x-ray inspection unit for automatic inspection of apples for bitter pit disorder and installation in processing plant. 4a.List the single most significant research accomplishment during FY 2006. This accomplishment falls under NP306 Component 1, Quality Characterization, Preservation and Enhancement, Problem area 1b, Methods to evaluate and predict quality. Automated Detection of Infested Food Commodities Using X-ray Imaging Algorithm development: An automatic recognition algorithm has been developed to detect insect infestations in x-ray images of food products. This addresses the problem of the inability of human inspectors to inspect images at processing line speeds. The algorithm was tested on a variety of infestation types in various food products and performs well. The algorithm could potentially be used to inspect x-ray images of agricultural commodities at processing line speeds to remove insect damaged product, which is a food quality and food safety issue. 4b.List other significant research accomplishment(s), if any. This accomplishment falls under NP306 Component 1, Quality Characterization, Preservation and Enhancement, Problem area 1b, Methods to evaluate and predict quality. Pit detection: The device previously reported for the real-time non-destructive detection of pits in prunes has been completed and patented. The device addresses the problem of unwanted pits remaining in pitted product, which is both a quality and food safety issue. This work was done at USDA-ARS-WRRC under the subordinate project 5325-44000-004-04T in cooperation with the Dried Fruit Association of California. Pending licensing of the patent by an industry partner, the invention has the potential to reduce the amount of unwanted pits in prunes. 4c.List significant activities that support special target populations. None. 4d.Progress report. None. 5.Describe the major accomplishments to date and their predicted or actual impact. This accomplishment falls under NP306 Component 1, Quality Characterization, Preservation and Enhancement, Problem area 1b, Methods to evaluate and predict quality. Under a CRADA with the Dried Fruit Association of California (DFA), a device for real-time non-destructive detection of pits in prunes has been developed and patented. Laboratory testing indicates that the device can identify approximately 70% of residual pits in the product while rejecting less than 1% of the total as false positive results (good product rejected as bad). It is expected that the simplicity and economy of the device increase the likelihood of adoption by the industry over more expensive imaging technologies. The feasibility of detection of both codling moth and bitter pit damage in apples using real-time x-ray inspection has been established. An automatic recognition algorithm has been developed to identify codling moth infestations. The algorithm has been modified to be applicable to other types of infestations and food products, and has been successfully tested for codling moth in apples, Olive fly in olives, and weevil infested grain. Scrolling program experiments have been completed which have demonstrated that human observers cannot identify infested or damaged apples from real-time x-ray images at a speed required for a processing plant environment. 6.What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end-user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? The development of the non-destructive device for pit detection was developed under a CRADA with a local producer group (DFA) for the benefit of dried fruit processors. The invention has been patented and we are searching for an industry partner interested in licensing the device. Unfortunately, DFA is no longer testing prunes for the local industry, and is therefore no longer in a position to license the patent. According to the project plan, the x-ray technology for apple inspection is planned to become available to apple processors during FY 2008. Constraints to the adoption of the technology include the relatively high cost of x-ray equipment and, in the case of insect infestations, the difficulty of identifying internal insects in their earliest stages of larval development, i.e., during the egg and first instar stages. Review Publications Hansen, J.D., Haff, R.P., Schlaman, D.W., Yee, W.L. 2005. Potential postharvest use of radiography to detect internal pests in deciduous tree fruits. Journal of Entomological Science. 40(3):255-262. Haff, R.P., Jackson, E.S., Pearson, T.C. 2005. Non-destructive detection of pits and pit fragments in dried plums. Applied Engineering in Agriculture.21(6):1021-1026. Jackson, E.S., Haff, R.P. 2006. Method and apparatus for non-destructive detection of pits and seed fragments in fruit. U.S. Patent 7,024,942 B1. Jackson, E.S., Haff, R.P. 2004. Sensor for detection of pits in dried plums. ASABE Annual International Meeting. ASABE PAPER #046123. Jackson, E.S., Haff, R.P. 2006. X-ray detection of and sorting of olives damaged by fruit fly. Transactions of the ASABE. ASABE Paper #06-6062. Spanjer, M.C., Scholten, J.M., Kastrup, S., Jorissen, U., Schatzki, T.F., Toyofuku, N. 2006. Sample comminution for mycotoxin analysis: dry milling or slurry mixing?. Journal of Food Additives & Contaminants. 23(1):73-83.