• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Postharvest Biology and Technology >A TSS classification study of 'Rocha' pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra
【24h】

A TSS classification study of 'Rocha' pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra

机译：基于非侵入性可见/近红外线反射光谱的“罗氏”梨（Pyrus Communis L.）的TSS分类研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The study focuses on the application of machine learning techniques for classifying the internal quality of 'Rocha' Pear (Pyrus communis L.), i.e., the total soluble solids (TSS), using the non-invasive technique of visible/near infra-red reflectance spectroscopy. Six representative classifiers were evaluated under realistic experimental conditions. The classifiers include representatives of classic parametric (logistic and multiple linear regression), non-parametric distance based methods (K-nearest neighbors), correlation-based (partial least squares), ensemble methods (random forests) and maximum margin classifiers (support vector machines). The classifiers were assessed against metrics such as accuracy, Cohen's Kappa, F-Measure, and the area under the precision recall curve (AUC) in a 10 x 10-fold cross-validation plan. For result analysis non-parametric statistical test of hypotheses were employed. A total of 4880 fruit samples from different origins, maturation states, and harvest years were considered. The main conclusion is that the maximum margin classifier outperforms all the others studied ones, including the commonly used partial least squares. The conclusion holds for both a reflectance spectrum with 1024 features and for a 128 subsample of these. An estimate of the out-of-sample performance for the best classifier is also provided.
机译：该研究侧重于使用可见/靠近红外线的非侵入性技术来对机器学习技术进行分类的应用程序学习技术反射光谱。在现实的实验条件下评估六分类剂。分类器包括经典参数（逻辑和多个线性回归）的代表，非参数距离的方法（K-Collest邻居），基于相关的（偏最小二乘），集合方法（随机林）和最大裕度分类器（支持向量）机器）。分类器被评估，例如精度，科恩的Kappa，F测量和精密召回曲线（AUC）中的区域，如10×10倍交叉验证计划。对于结果分析，使用假设的非参数统计测试。考虑了来自不同起源，成熟状态和收获年份的共有4880个水果样本。主要结论是，最大边际分类器优于所研究的所有其他人，包括常用的部分最小二乘法。结论适用于具有1024个特征的反射光谱和128个子。还提供了对最佳分类器的采样外部性能的估计。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号