U.S.-Turkey Cooperative Research: Food Quality and Safety by Kernel Classification

美国-土耳其合作研究：按内核分类的食品质量和安全

• 批准号: 0352965
• 负责人: Ahmed Tewfik
• 金额: --
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352965&HistoricalAwards=false
• 关键词: U.S.; Turkey; Cooperative; Research; Food

0352965 TewfikDescription: This project supports collaborative research between Dr. Ahmed Tewfik, Department of Electrical Engineering, the University of Minnesota, Minneapolis, Minnesota and a team headed by Dr. Enis Cetin Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey. They propose to study the development of methods for detection and classification of contaminated or defective kernels by using analysis of impact acoustics. The PIs plan to develop contaminated or defective kernel detection and classification based on the analysis of impact acoustics, or the sound created when a kernel strikes a hard plate, as this mode of acoustic excitation is easily adapted to high throughput sorting systems. Demonstrating the effectiveness of the method in applications to hazelnuts, wheat, and corn, could achieve a breakthrough in food safety by enabling the non-invasive, rapid inspection of large quantities of food items. Despite increasingly rigorous quality control stand in food alerts, current approaches are mostly based on invasive chemical analysis of some selected food items or sorting food items according to their color. Although chemical analysis gives the most accurate results, it is impossible to analyze large quantities of food items or to apply it to certain items. Signal processing techniques have become attractive with the advances in computer technology, and it is now possible to integrate low cost, non-invasive signal processing systems providing quality control into the food supply chain. The approach uses substantial extensions of speech and speaker recognition techniques to deal with the variability that results from different kernel orientation at the instant of impact. The project focuses more specifically on the detection of aflatoxins (a carcinogenic material) and insect tunnels in hazelnuts, wheat kernels damaged by insects, and aflatoxins and insect tunnels in corn. Detection and classification results will be verified with high performance liquid chromatography. Scope: The project will lead to the development of real-time, economically feasible, systems to remove whole intact corn and hazelnut kernels that are contaminated with aflatoxins or wheat kernels infested with insects; interdisciplinary training programs for undergraduate and graduate students and the food industry in Turkey and the USA in signal processing, agricultural engineering and molecular biology; the involvement of students in data gathering and analysis; and algorithm development. With some modifications, the algorithms and methods developed in this project can be also used for other nuts and produce such as almonds and figs. The algorithms and other fundamental knowledge to be developed may be applicable to multivariate signal processing in general, beyond acoustics. Turkey has a wealth of expertise in hazelnut production and processing, while the United States is the source of expertise in grain production and processing. The research team reflects the interdisciplinary nature of the project, and several of the investigators bring to the project a strong background in the analysis and processing of speech and will crossover such methods to food safety.

0352965 Tewfik 描述：该项目支持明尼苏达州明尼阿波利斯市明尼苏达大学电气工程系 Ahmed Tewfik 博士和土耳其安卡拉 Bilkent 大学电气与电子工程系 Enis Cetin 博士领导的团队之间的合作研究。 他们建议研究开发通过使用冲击声学分析来检测和分类受污染或有缺陷的谷粒的方法。 PI 计划根据冲击声学或谷粒撞击硬板时产生的声音的分析来开发受污染或有缺陷的谷粒检测和分类，因为这种声学激励模式很容易适应高通量分选系统。 证明该方法在榛子、小麦和玉米应用中的有效性，可以通过对大量食品进行非侵入式快速检测来实现食品安全方面的突破。 尽管食品警报中的质量控制日益严格，但目前的方法主要基于对某些选定食品进行侵入性化学分析或根据颜色对食品进行分类。 尽管化学分析可以提供最准确的结果，但不可能分析大量食品或将其应用于某些物品。 随着计算机技术的进步，信号处理技术变得越来越有吸引力，现在可以将低成本、非侵入性的信号处理系统集成到食品供应链中，提供质量控制。 该方法使用语音和说话人识别技术的大量扩展来处理撞击瞬间不同内核方向导致的可变性。该项目更具体地侧重于检测榛子中的黄曲霉毒素（一种致癌物质）和昆虫隧道、被昆虫损坏的小麦籽粒以及玉米中的黄曲霉毒素和昆虫隧道。 检测和分类结果将通过高效液相色谱法进行验证。 范围：该项目将开发实时、经济可行的系统，以去除受黄曲霉毒素污染的完整玉米和榛子仁或受昆虫侵染的小麦仁；为土耳其和美国的本科生和研究生以及食品工业提供信号处理、农业工程和分子生物学方面的跨学科培训项目；学生参与数据收集和分析；和算法开发。 经过一些修改，该项目开发的算法和方法也可用于其他坚果和农产品，例如杏仁和无花果。待开发的算法和其他基础知识可能适用于声学之外的一般多元信号处理。 土耳其在榛子生产和加工方面拥有丰富的专业知识，而美国是粮食生产和加工方面的专业知识的来源。 研究团队体现了该项目的跨学科性质，几位研究人员为该项目带来了语音分析和处理方面的强大背景，并将这些方法交叉应用于食品安全。