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中国精品科技期刊2020
陈书媛,张友超,杨杰,等. 基于高光谱成像技术的白茶储藏年份判别[J]. 食品工业科技,2021,42(18):276−283. doi: 10.13386/j.issn1002-0306.2020110299.
引用本文: 陈书媛,张友超,杨杰,等. 基于高光谱成像技术的白茶储藏年份判别[J]. 食品工业科技,2021,42(18):276−283. doi: 10.13386/j.issn1002-0306.2020110299.
CHEN Shuyuan, ZHANG Youchao, YANG Jie, et al. Discrimination of Storage Time of White Tea Using Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2021, 42(18): 276−283. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020110299.
Citation: CHEN Shuyuan, ZHANG Youchao, YANG Jie, et al. Discrimination of Storage Time of White Tea Using Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2021, 42(18): 276−283. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020110299.

基于高光谱成像技术的白茶储藏年份判别

Discrimination of Storage Time of White Tea Using Hyperspectral Imaging

  • 摘要: 储藏年份是决定白茶经济价值的一大因素。为了实现快速便捷地判别白茶储藏年份,本文提出了基于高光谱成像技术判别分析白茶储藏年份的无损检测方法。通过对3、6、10年寿眉高光谱图像感兴趣区域光谱数据的提取,采用最小二乘平滑滤波、标准正态变换、归一化、多元散射校正预处理算法,并用支持向量机、偏最小二乘联合线性判定法、逻辑回归建模对不同预处理后的光谱数据进行判别分析。最后,通过分析混淆矩阵、精确率、召回率来评估模型性能。分析结果表明,经过标准正态变换预处理结合支持向量机所建立的模型判别效果最佳,训练集和测试集的精确率分别为90.83%和86.02%。由此可见,利用高光谱成像技术对白茶储藏年份进行快速无损的判别具有一定的可行性。

     

    Abstract: The storage time is a main factor determining the value of white tea. To discriminate the storage time rapidly and nondestructively, a new method was applied in this paper. First, hyperspectral image data were captured from Shoumei of 3, 6 and 10 years storage time. Second, four kinds of algorithms were applied to preprocess the original data, savitzky-golay Filter, standard normal variate, minmaxscaler, and multiplicative scatter correction. Third, support vector machine, partial least squares with linear discriminant analysis and logistic regression were built based on the data of the full spectra. Finally, the best combination of preprocessing algorithm and model could be found by comparing the confusion matrix, precision and recall. The results showed that the best classification performances were obtained with the support vector machine after the pretreatment of standard normal variate. The precision of the calibration sets was 90.83%, and that of the prediction sets was 86.02%. Therefore, it is possible to use hyperspectral imaging to discriminate white tea of different storage time in tea industry.

     

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