Abstract
The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.
Highlights
Tea is one of the most popular non-alcoholic beverages in the world
(1) Sensor array optimization based on correlation analysis and the distinguishing performance value (DPV)
This paper proposed an optimization of an electronic nose sensor array to detect tea aroma based on correlation coefficient and cluster analysis
Summary
Tea is one of the most popular non-alcoholic beverages in the world. Aroma is an important attribute of tea, which contains rich information such as quality and type.There are approximately 600 aromatic compounds in tea aroma [1,2]. Aroma is an important attribute of tea, which contains rich information such as quality and type. Electronic nose systems have played an increasingly important role in the field of gas detection. Electronic nose can closely mimic the organization of human olfactory system for obtaining the fingerprints of gas signals from samples through a sensor array, and pattern recognition methods have the ability to identify ‘fingerprints’ in a given dataset [3]. Electronic nose has played an important role in many fields of food engineering, including food classification [4,5], quality assessment [6,7], freshness prediction [8] and identification authenticity [9]. Research objects include various foods such as fruit [10], vegetables [11], meat [12], beverage [13,14,15], herb [16] and especially tea [17,18,19,20,21,22]
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