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首页> 外文学位 >Nondestructive evaluation of fresh chestnut internal quality using x-ray computed tomography (CT).
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Nondestructive evaluation of fresh chestnut internal quality using x-ray computed tomography (CT).

机译：使用X射线计算机断层扫描（CT）对新鲜栗子内部质量进行无损评估。

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摘要

Internal decay is an important quality attribute in chestnuts (Castanea spp.). Worldwide, internal decay is mainly caused by microorganism attack and physiological cell breakdown. It is problematic for the industry, and impacts consumer satisfaction, shelf life, and proper storage. Currently, destructive techniques can be employed to evaluate fresh chestnut internal quality. However, clearly not all produce can be evaluated. In commercial situations, decayed chestnuts are eliminated by their proclivity to float in water. Nonetheless, performance significantly varies between species and throughput, making this floating method unreliable for sorting purposes. Thus, the overall objective of the study is to develop the methods to nondestructively visualize and automatically classify fresh chestnuts, based on their internal quality, using X-ray CT imaging.;In this study, medical grade computed tomography (CT) was used to obtain transversal two-dimensional (2D) images from fresh chestnuts (cv. ‘Colossal’ and Chinese seedlings). If the information obtained by the CT scanning of fresh chestnuts is to be used in an industrial setting for in-line sorting, automated interpretation of CT images is essential. For this purpose: (1) Chestnut CT image quality was optimized by studying the combined effect of image acquisition parameters (voltage – 120 kV, current – 170 mA and slice thickness – 2.5 mm) using response surface methodology, (2) effective image visualization techniques to infer fresh chestnut internal quality attributes were established, and (3) an image analysis algorithm for the automatic classification of CT images obtained from 2848 fresh chestnuts, during the harvesting years from 2009 to 2012, was developed and tested.;The CT imaging system provided high-resolution and high-contrast images of the internal structure and components of fresh chestnuts. Approximately 50 original CT image slices (stack) were obtained per chestnut, from three different planes (angular orientations) across the longitudinal (Z) (XY-plane-slice), horizontal (YZ-plane-slice) and vertical (XZ-plane-slice) axes. From this image stack, 6 secondary CT images per chestnut sample, including mean and maximum intensity value images for each of the planes were extracted. Thereafter, a total of 1194 grayscale intensity, and textural features were extracted from the 6 secondary CT images per sample. Ultimately, 86, 155 and 126 features were found to be effective in designing a quadratic discriminant analysis classifier with an overall performance accuracy of 85.9 %, 91.2 % and 96.1 % for 5, 3 and 2 classes, respectively.;This study provides a powerful tool to accurately visualize and sort chestnuts based on their internal quality, leading to the improved marketability of attractive, safe, high quality chestnuts. Results show that this method is accurate, reliable, and objective and it is applicable to an automatic noninvasive in-line CT sorting system.
机译：内部腐烂是栗子（Castanea spp。）的重要品质属性。在世界范围内，内部衰变主要是由微生物攻击和生理细胞分解引起的。这对于行业来说是有问题的，并且会影响消费者的满意度，保质期和正确的存储。目前，可以采用破坏性技术来评估新鲜栗子的内部质量。但是，显然不是所有产品都可以评估。在商业情况下，腐烂的栗子因其易于漂浮在水中而被消除。但是，性能在种类和通量之间显着变化，使得这种浮动方法对于分类目的而言并不可靠。因此，本研究的总体目标是开发使用X射线CT成像技术基于其内部质量进行无损可视化和自动分类的新鲜栗子的方法;在本研究中，使用医学级计算机断层扫描（CT）从新鲜栗子（cv。'巨大的'和中国的幼苗）中获得横向二维（2D）图像。如果要在工业环境中使用通过新鲜栗子CT扫描获得的信息进行在线分选，则必须对CT图像进行自动解释。为此：（1）通过使用响应面方法研究图像采集参数（电压– 120 kV，电流– 170 mA和切片厚度– 2.5 mm）的综合效果来优化栗子CT图像质量，（2）有效的图像可视化建立了推断新鲜栗子内部质量属性的技术，（3）开发并测试了一种图像分析算法，用于对2009年至2012年收获期间从2848个新鲜栗子获得的CT图像进行自动分类。该系统提供了新鲜栗子内部结构和成分的高分辨率和高对比度图像。每个栗子从纵向（Z）（XY平面切片），水平（YZ平面切片）和垂直（XZ平面）的三个不同平面（角度方向）获得了大约50个原始CT图像切片（堆栈） -slice）轴。从该图像堆栈中，每个栗子样本提取了6个次要CT图像，包括每个平面的均值和最大强度值图像。此后，从每个样本的6张次要CT图像中提取了总计1194的灰度强度和纹理特征。最终，发现在设计二次判别分析分类器时有效地使用了86、155和126个功能，对于5、3和2类，总体性能准确度分别为85.9％，91.2％和96.1％。一种基于栗子内部质量准确可视化和分类栗子的工具，从而提高了有吸引力，安全，高质量的栗子的适销性。结果表明，该方法准确，可靠，客观，适用于自动无创在线CT分拣系统。

著录项

  • 作者

    Donis-Gonzalez, Irwin R.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Agriculture Food Science and Technology.;Engineering Agricultural.;Computer Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 210 p.
  • 总页数 210
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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