Detection Of Goldfish Water Based On Electronic Tongue And Several Artificial Neural Networks

Nowadays, with the rapid development of aquaculture, it is crucial to find a fast and accurate method to detect aquacultural water quality qualitatively and quantitatively. Electronic tongue is a novel instrument, which can mimic human taste to analyze the characterization of liquid samples. It has been established that the electronic tongue was capable of obtain the comprehensive information of liquid both qualitatively and quantitatively in many fields, such as food area, medical area as well as environmental engineering area and so on. Pattern recognition plays an important role in electronic tongue system. The BP neural network, as a kind of artificial neural network, has been used widely with electronic tongue for liquid analysis. However, there was no literature on the application of detection of aquacultural water quality with electronic tongue, as well as the capability of other artificial neural networks with electronic tongue for aquacultural water quality. In this work, a α-ASTREE electronic tongue by Alpha.MOS (France) was employed with six kinds of artificial neural network, which was BP neural network (BP), Radial basis function neural network(RBFNN), Generalized regression neural network(GRNN), Particle swarm optimization BP neural network(PSOBP), Genetic algorithm to optimize BP neural network(GABP) and Takagi-Sugeno Fuzzy neural network (TSFNN), to detect goldfish water from different cultivation conditions (cultivation densities, cultivation temperatures, food supply, aerating conditions and pH conditions) qualitatively and quantitatively. Both the qualitative and quantitative evaluation systems for aquacultural water quality were established. Both the internal relations between the sensors and different cultivation conditions and the internal relations between the sensors and four main chemical parameters were revealed, and the sensors array was optimized. Both the classified capability and prediction capabiltiy based on original sensors array and optimal sensors array for different goldfish water was evaluated qualitatively and quantitatively. The main conclusions are as follows.(1) Significant differences between samples from different cultivation conditions and all sensors were significant to four main chemical parameters (content of nitrate, ammonia and dissolve oxygen and pH value). And the optimal sensors array (BA、BB、CA、HA and JB) was obtained for both qualitative and quantitative detection of different goldfish water.(2) Samples from different levels of same cultivation condition could be classified by PCA based on response of original sensors array. And the classification result was improved when sensors array was optimized.(3) The relation between sensors response and chemical contents was complex and nonlinear. Samples with one of four chemical parameters belong to different value range from same cultiviation conditions could be classified by PCA based on response of original sensors array. And the classification result was improved when sensors array was optimized.(4) According to the developed qualitative evaluation system for aquacultural water, the developed BP network was most suitable for comprehensive and qualitative detection of cultivation conditions of goldfish water fast and accurately.(4) According to the developed quantitative evaluation system for aquacultural water, the developed GRNN and RBFNN networks were most suitable for quantitative detection of goldfish water fast and accurately.