Development And Application Of Electronic Tongue System With Automatic Sampling And Constant Temperature Control

In recent years, people pay more and more attention to the food problem, the demand for food detection equipment is also increasing. As a new type of detection device, electronic tongue, which has the advantages of fast detection speed, high efficiency, multi-use and fully reflected the information of the measured objects, has been gradually applied in the field of food detection. At present, the research of electronic tongue is mainly focused on development of sensors. The degree of automation and integration of equipment is low. Experimenter need to feed samples, clean the electrode and control the temperature of samples manually. The detection process is cumbersome, heavy workload, and operator need to be on duty for a long time. In view of the existing problems, this paper designed a voltammetric electronic tongue system with automatic sampling, temperature controlling, detection, electrode cleaning.The electronic tongue was tested to verify the performance of the main modules and the electronic tongue was used in the detection of different kinds of milk, water, beer and tea to test the performance of detection. The main research contents and conclusions of this paper are as follows:(1) A voltammetric electronic tongue system is developed, which includes electrode and signal acquisition module, automatic sampling module, thermostatic bath module, input and display module, control module and PC computer. The host machine is the computer which runs on the LabVIEW program. It is the display and control terminal of the equipment. STC89 MCU is the core of the control module, which control of a number of other modules directly by the host computer serial command. Electrode and signal acquisition module is the core of the electronic tongue, using the three electrode system, which is composed of precious metal electrode array, signal circuit and collection card. The host computer transmits the excitation signal to the electrode and signal acquisition module and collects the feedback signal through the USB.(2) In order to explore the performance of the main modules of electronic tongue, the performance test of the automatic sampling module, the thermostatic bath module and the electrode and signal acquisition module was carried out. Test results show that the sampling, cleaning process is accurate and stable.25? is a good working temperature for thermostatic bath. The temperature field in tank is uniform. The temperature controlling precision of samples in all position is 25?. Electrode signal drift with time is very small. But the sample temperature has a significant influence on the signal amplitude and the characteristic value, and controlling sample temperature can effectively improve the detection accuracy.(3) In order to verify the basic detection performance of electronic tongue, the electronic tongue is used to detect different kinds of milks, beers and waters. The area surrounded by the X axis and detection signal curve is used as a characteristic value and data was analyzed with LDA, PCA. The results show that the electronic tongue can distinguish them. In addition, it also found that increasing the number of washing cups can significantly improve the cleaning effect of the electrode. The bubbles in the sample will affect the detection stability. Removing the bubbles can effectively improve the detection effect.(4) In view of the West Lake Longjing tea adulteration flooding the market and adulteration being difficult to identify, this paper used the electronic tongue to analysis of different grades tea and adulterated samples. The detection data of PCA and LDA analysis chart shows that the various types of tea can effectively distinguish. Three types of data were modeled and predicted by multiple linear regression. The results show that the prediction accuracy rate of tea grade is high. The prediction accuracy rate of percentage of low grade tea in high grade tea is high too. But the prediction accuracy rate of percentage of old-tea in new-tea is low. In view of the above situation, neural network modeling is used for processing the two kinds of adulteration data. It shows that the prediction accuracy of the extent of two kinds of adulterations have reached a high level.