Towards Versatile Electronic Nose Pattern Classifier for Black Tea Quality Evaluation: An Incremental Fuzzy Approach
Towards Versatile Electronic Nose Pattern Classifier for Black Tea Quality Evaluation: An Incremental Fuzzy Approach
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DOI：
10.1109/tim.2009.2016874
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发表时间：
2009-05
影响因子：
5.6
通讯作者：
B. Tudu;A. Metla;Barun Das;N. Bhattacharyya;A. Jana;D. Ghosh;R. Bandyopadhyay
中科院分区：
文献类型：
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作者：
B. Tudu;A. Metla;Barun Das;N. Bhattacharyya;A. Jana;D. Ghosh;R. Bandyopadhyay
常用的分类算法不能进行增量学习。当一个新的模式被呈现给这样的计算模型时，它可以基于其传统训练对未知模式进行分类，或者如果这样的规定被内置到相关联的算法中，则将该模式声明为离群值。在模式是现有训练模型的离群值的情况下，期望的是，可以将其无缝地包括在具有适当类别标签的训练模型中，使得可以递增地演进通用计算模型。为此，具有增量学习能力的分类器可以通过自动地将新呈现的模式包括在训练数据集中而不影响先前训练的系统的类完整性而具有很大的益处。在本论文中，提出了一个增量学习模糊模型的红茶分类使用电子鼻测量。为了应用于红茶等级的鉴别，尝试将电子鼻的多传感器香气模式与感官小组（品茶员）的评价相关联。然而，这个问题与2-D复杂性相关联。一方面，茶叶的香气取决于特定地点的农业气候条件，特定的潮汛季节和茶树的无性系变异。另一方面，感官评价是完全依赖于人的，往往遭受主观性和不可重复性。在我们追求开发一个通用的计算模型，能够客观地分配茶品尝者样的分数，在测试中的茶样品，它已经被认为是一个增量的方法可能是非常有益的电子鼻为基础的茶叶质量估计。为此，建议的增量学习模糊模型有望成为一个通用的模式分类算法红茶等级的歧视，使用电子鼻。该算法已在印度东北部的一些茶园进行了测试，并取得了令人鼓舞的结果。
Commonly used classification algorithms are not capable of incremental learning. When a new pattern is presented to such a computational model, it can either classify the unknown pattern based on its legacy training or declare the pattern as an outlier if such a provision is built into the associated algorithm. In the case of the pattern being an outlier to the existing training model, it is desirable that the same could be seamlessly included in the training model with appropriate class labels so that a universal computational model may be evolved incrementally. To this end, classifiers having the incremental-learning ability can be of great benefit by automatically including the newly presented patterns in the training data set without affecting class integrity of the previously trained system. In the present treatise, an incremental-learning fuzzy model for classification of black tea using electronic nose measurement is proposed. For application in black tea grade discrimination, an attempt has been made to correlate the multisensor aroma pattern of electronic nose with sensory panel (tea tasters) evaluation. However, this problem is associated with 2-D complexities. On one hand, the aroma of tea depends on the agroclimatic condition of a particular location, the specific season of flush, and the clonal variation for the tea plant. On the other hand, the sensory evaluation is completely human dependent that often suffers from subjectivity and nonrepeatability. In our pursuit of developing a universal computational model capable of objectively assigning tea-taster-like scores to tea samples under test, it has been felt that an incremental approach could be extremely beneficial for electronic-nose-based tea quality estimation. To this end, the proposed incremental-learning fuzzy model promises to be a versatile pattern classification algorithm for black tea grade discrimination using electronic nose. The algorithm has been tested in some tea gardens of northeast India, and encouraging results have been obtained.