...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of food properties >Rapid Detection of Adulteration in Extra-Virgin Olive Oil using Three-Dimensional Fluorescence Spectra Technology with Selected Multivariate Calibrations
【24h】

Rapid Detection of Adulteration in Extra-Virgin Olive Oil using Three-Dimensional Fluorescence Spectra Technology with Selected Multivariate Calibrations

机译：使用三维荧光光谱技术和选定的多元校准技术快速检测初榨橄榄油中的掺假

获取原文
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To rapidly and efficiently detect the presence of adulterants in extra-virgin olive oil, 3D fluorescence spectra technology was employed with the help of multivariate calibration. Parallel factor analysis and characteristic parameters method were comparatively employed to compress and extract the data of 3D fluorescence spectra. Then, three different non-linear and linear classification tools (i.e., back-propagation artificial neural network, least-square support vector machine and k -nearest neighbor) were systemically studied and compared in developing the model. The number of principle components and parameters of models were optimized by cross-validation. Compared with parallel factor analysis, characteristic parameters method, in this article, has its own superiority. Experimental results also showed that the performance of least-square support vector machine model is the best among the three models. The optimal least-square support vector machine model was achieved when seven principle components were used, with the discrimination rate of 98.96% in calibration set and 96.88% in prediction set, respectively. The misclassified samples are adulterated extra-virgin olive oil, and their adulterated concentrations were lower than 2.5% (wt/wt). The overall results sufficiently demonstrated that 3D fluorescence spectroscopy technology coupled with characteristic parameters method and least-square support vector machine classification tool has the potential to detect adulterated extra-virgin olive oil products when their adulterant concentrations are more than 2.5% (wt/wt).
机译：为了快速有效地检测初榨橄榄油中掺假物的存在，在多元校准的帮助下采用了3D荧光光谱技术。比较地采用并行因子分析和特征参数方法来压缩和提取3D荧光光谱数据。然后，系统地研究了三种不同的非线性和线性分类工具（即，反向传播人工神经网络，最小二乘支持向量机和近邻），并在模型开发中进行了比较。通过交叉验证优化了模型的主成分和参数的数量。与并行因子分析相比，本文的特征参数法具有其自身的优势。实验结果还表明，最小二乘支持向量机模型的性能在这三个模型中是最好的。当使用七个主要成分时，获得了最优的最小二乘支持向量机模型，在校准集中的辨别率为98.96％，在预测集中的辨别率为96.88％。错误分类的样品是掺假的特级初榨橄榄油，其掺假浓度低于2.5％（wt / wt）。总体结果充分证明，当3D荧光光谱技术与特征参数方法和最小二乘支持向量机分类工具结合使用时，当掺假浓度超过2.5％（wt / wt）时，有可能检测出掺假的初榨橄榄油产品。 。

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