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Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics

机译:利用高光谱成像技术和化学计量学识别龙井鲜茶品种

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摘要

Hyperspectral imaging technology was applied to detect and recognize six different varieties of Longjing fresh tea. The data contained image and spectral information at 370-1042 nm; color and texture features were the foci of the image research. Spectral pre-processing was performed by multiplicative scatter correction (MSC) and standard normal variate (SNV), and then, we selected the corresponding position variable and vegetation indexes as spectral features. Representative features including the most information were chose by principal component analysis (PCA). A novel back propagation (BP) neural network, with a self-generated number of hidden layer neurons, was proposed. Using spectral features, image features, and spectral image fusion features as input, three fresh tea recognition models were established: the improved BP neural network, traditional BP neural network, and support vector machine (SVM). Results suggested that the improved BP neural network could promote performance of the model, especially for the spectral pre-processed data. Mixed-feature models did better than individual feature models, with 100% accuracy of the predictive set. This study shows that hyperspectral imaging technology can be a potential rapid and nondestructive approach to identify different varieties of Longjing fresh teas. Practical applications This article introduced application of hyperspectral imaging technology to identify Longjing fresh tea of different origins and varieties. Samples were analyzed using spectral and image characteristics. We provided a basis for full utilization of tea characteristics. At the same time, an improved BP neural network, with less calculation complexity and workload than the traditional BP neural network, was proposed. In summary, we outlined a convenient and reliable method for differentiation of Longjing fresh teas. Furthermore, we established a theoretical foundation for development of portable instruments to be used in similar studies.
机译:高光谱成像技术被用于检测和识别六种不同的龙井鲜茶。数据包含370-1042 nm的图像和光谱信息;颜色和纹理特征是图像研究的重点。通过乘法散射校正(MSC)和标准正态变量(SNV)进行光谱预处理,然后选择相应的位置变量和植被指数作为光谱特征。通过主成分分析(PCA)选择了包括最多信息的代表性功能。提出了一种新型的反向传播(BP)神经网络,该网络具有自生数量的隐藏层神经元。使用光谱特征,图像特征和光谱图像融合特征作为输入,建立了三个新鲜茶识别模型:改进的BP神经网络,传统BP神经网络和支持向量机(SVM)。结果表明,改进的BP神经网络可以提高模型的性能,特别是对于光谱预处理数据。混合特征模型比单个特征模型做得更好,预测集的准确性为100%。这项研究表明,高光谱成像技术可能是识别龙井鲜茶不同品种的一种潜在的快速且无损的方法。实际应用本文介绍了高光谱成像技术在鉴别不同产地和品种的龙井鲜茶中的应用。使用光谱和图像特征分析样品。我们为充分利用茶的特性提供了基础。同时,提出了一种改进的BP神经网络,其计算复杂度和工作量比传统的BP神经网络要少。总之,我们概述了一种方便,可靠的龙井鲜茶鉴别方法。此外,我们为在类似研究中使用的便携式仪器的开发奠定了理论基础。

著录项

  • 来源
    《Journal of food process engineering》 |2020年第4期|e13378.1-e13378.9|共9页
  • 作者

  • 作者单位

    Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China;

    Kungang Elect Informat Technol Co Ltd Kunming Yunnan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 05:21:10

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