Zakaria Nur Zawatil Isqi, Masnan Maz Jamilah, Zakaria Ammar, Shakaff Ali Yeon Md
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Jejawi 02600, Perlis, Malaysia.
Sensors (Basel). 2014 Jul 9;14(7):12233-55. doi: 10.3390/s140712233.
Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers' performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied.
基于草药的产品正成为国内外市场制造商中一种广泛的生产趋势。随着产量增加以满足市场需求，制造商确保其产品符合特定标准并达到质量控制人员确定的预期质量至关重要。一种著名的基于草药的产品是花草茶。本文研究了在涉及电子鼻和电子舌的数据融合框架中受生物启发的风味评估。目标是实现对不同类型和品牌花草茶的良好分类、对不同风味掩盖效果的分类以及最终对不同浓度花草茶的分类。本研究采用了两个数据融合级别，即低级数据融合和中级数据融合。研究了四种分类方法；线性判别分析（LDA）、支持向量机（SVM）、k近邻（KNN）和概率神经网络（PNN），以寻找实现研究目标的最佳分类器。为了评估分类器的性能，应用了基于k折交叉验证和留一法的误差估计器。基于气相色谱 - 质谱总离子流色谱图（GC - MS TIC）数据的分类也作为与使用融合方法的分类性能的比较而纳入。总体而言，在低级数据融合和中级数据融合中的三种风味评估中，KNN的表现优于其他分类技术。然而，基于GC - MS TIC数据的分类结果各不相同。
