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首页> 外文学位 >Multivariate image analysis: An optimization tool for characterizing damage-related attributes in magnetic resonance images of processing tomatoes .
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Multivariate image analysis: An optimization tool for characterizing damage-related attributes in magnetic resonance images of processing tomatoes .

机译：多元图像分析：一种用于在加工番茄的磁共振图像中表征损伤相关属性的优化工具。

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摘要

Processors desire in-line, nondestructive methods for characterizing whole tomatoes. During peeling, severely damaged tomatoes will disintegrate, leading to loss of product, increased need for wastewater treatment, and other costly problems. Thus, the purpose of this research was to develop an in-line method to detect damaged processing tomatoes, focusing on the pericarp tissue of the fruit. Magnetic Resonance (MR) imaging characterizes the environment of water protons in plant tissue, resulting in contrast between image pixels corresponding to damaged and sound tissue. Many types of MR imaging sequences are available; in this research, the Multivariate Image Analysis (MIA) methods of Partial Least Squares, Partial Least Squares-Discriminant Analysis, and Soft Independent Modeling of Class Analogy were used to determine the optimal MR pulse sequences for tomato pericarp damage assessment. This work encompasses three studies: one performed in the 2007 tomato harvest season, in which pericarp damage was measured off-line using conductivity score only, and two performed in 2008, in which pilot peeling outcomes, texture measurements, and acoustic measurements were used in addition to conductivity score. The multivariate images were created by varying key parameters in different MR pulse sequences; the average pixel intensities in regions of interest corresponding to the pericarp were used as the independent variables in the MIA analyses. The numeric (conductivity score, texture measurement, and acoustic impact measurement) and categorical (peeling outcome) response values were used as the dependent variables. With low error rates under cross-validation, MIA of MR images was successful at quantitatively predicting conductivity score and peeling outcomes. While the numeric values of the texture and acoustic impact measurements could not be predicted by MIA of MR images with error rates low enough for industrial implementation, these values could be predicted in a semi-quantitative manner (i.e. MIA predicts whether the value would be relatively high or relatively low). The MIA approach also identified multiple MR pulse sequences that are key to predicting the off-line pericarp measurements. This work has demonstrated that damaged tomato pericarp tissue can be efficiently characterized in-line using multivariate MRI, thus furthering knowledge in the field of postharvest quality assurance of processing tomatoes.
机译：加工者需要在线，无损的方法来表征整个西红柿。在削皮过程中，严重受损的西红柿会崩解，导致产品损失，废水处理需求增加以及其他昂贵的问题。因此，本研究的目的是开发一种在线方法来检测受损的加工番茄，重点是水果的果皮组织。磁共振（MR）成像可表征植物组织中水质子的环境，从而导致与受损组织和声音组织相对应的图像像素之间形成对比。多种类型的MR成像序列可用。在这项研究中，使用偏最小二乘，偏最小二乘判别分析和类比法软独立建模的多元图像分析（MIA）方法来确定番茄果皮损伤评估的最佳MR脉冲序列。这项工作包括三项研究：一项是在2007年番茄收获季节进行的，其中果皮的损伤仅通过电导率评分离线测量；另一项是在2008年进行的，其中将果皮的剥离结果，质地测量和声学测量用于除了电导率分数。通过在不同的MR脉冲序列中改变关键参数来创建多元图像。 MIA分析中将与果皮对应的目标区域中的平均像素强度用作自变量。数值（电导率得分，纹理测量和声学冲击测量）和分类（剥离结果）响应值用作因变量。由于交叉验证下的错误率低，MR图像的MIA成功地定量预测了电导率得分和剥离结果。虽然MR图像的MIA不能预测纹理和声学冲击测量的数值，而错误率对于工业应用来说足够低，但是可以半定量方式预测这些值（即MIA预测该值是否相对高或相对低）。 MIA方法还确定了多个MR脉冲序列，这是预测离线果皮测量的关键。这项工作表明，使用多变量MRI可以有效地在线表征受损的番茄果皮组织，从而进一步丰富了番茄加工后收获质量保证领域的知识。

著录项

  • 作者

    Milczarek, Rebecca Rose.;

  • 作者单位

    University of California, Davis.;

  • 授予单位 University of California, Davis.;
  • 学科 Agriculture Food Science and Technology.;Engineering Agricultural.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 148 p.
  • 总页数 148
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农产品收获、加工及贮藏;农业工程;
  • 关键词

  • 入库时间 2022-08-17 11:38:28

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