التفاصيل البيبلوغرافية
| العنوان: |
基于嗅觉可视化技术的眉茶等级分类方法. (Chinese) |
| Alternate Title: |
Classification Method for Mee Tea Grades Based on Olfactory Visualization Technology. (English) |
| المؤلفون: |
丁煜函, 葛东营, 荆 磊, SHAHZAD, Muhammad, 江 辉 |
| المصدر: |
Shipin Kexue/ Food Science; 2022, Vol. 43 Issue 24, p335-341, 7p |
| مصطلحات موضوعية: |
SWARM intelligence, SUPPORT vector machines, SENSOR arrays, PRINCIPAL components analysis, KERNEL functions, PARTICLE swarm optimization, CLASSIFICATION algorithms |
| Abstract (English): |
A rapid and accurate method for the classification of Mee tea grades based on olfactory visualization technology was proposed. First, 12 different porphyrin indicators which were found to have an obvious chromogenic effect in the preliminary experiment were selected to develop a color-sensitive gas sensor array. The sensor array was then used to test different grades of Mee tea infusion, and the characteristic images were acquired and analyzed by principal component analysis (PCA) for dimensionality reduction. The obtained PCA results with different dimensionalities were used as input variables to establish a classification model for Mee tea grades using support vector machine (SVM). Finally, three swarm intelligence algorithms, firefly algorithm (FA), gray wolf optimization (GWO) and cuckoo search (CS), were used to optimize the penalty factor (c) and the kernel function parameter (g) of the SVM model. Results showed that the classification accuracy of the original SVM model for the test set was 80% with 12 principal components needed, and increased after optimization with each of the three algorithms. The classification accuracy of the model optimized by CS was 93.3%, and the number of principal components needed was reduced to 6. Accordingly, olfactory visualization technology can be used to classify Mee tea grades, and the model performance can be enhanced and the classification accuracy can be improved by swarm intelligence optimization algorithms significantly. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
为了快速、准确地对眉茶等级进行分类, 提出了一种基于嗅觉可视化技术的眉茶等级快速分类方法。首 先, 根据卟啉显色反应预实验结果, 选定了12 种显色效果明显的卟啉指示剂制备嗅觉可视化传感器阵列, 通过该 传感器阵列与不同等级的眉茶茶汤进行反应, 获取不同的特征图像。然后, 对特征图像数据进行主成分分析和降 维, 将得到的不同维数的主成分分析结果作为输入变量, 构建支持向量机 (support vector machine, SVM) 眉茶等 级分类模型。最后, 引入3 种群体智能优化算法 (萤火虫算法、灰狼优化算法、布谷鸟算法) 对SVM分类模型的惩 罚因子c和核函数参数g进行优化。结果显示：未经优化的SVM分类模型对测试集的分类正确率为80%, 所需的主成 分个数为12 个；经过优化的SVM模型的分类正确率均有所提升, 其中经过布谷鸟算法优化的SVM模型对测试集的 分类正确率达到了93.3%, 且所需的主成分个数减少为6 个。这表明应用嗅觉可视化技术能够实现对眉茶等级的分 类, 而通过群体智能优化算法优化SVM分类模型可以显著增强模型的性能, 提高分类正确率。 [ABSTRACT FROM AUTHOR] |
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