Design Of Electronic Nose For Food Quality Evaluation

An Electronic nose system was developedbased on metal oxide semiconductor gassensor array, the LabVIEW virtual instrument, PCA and LVQ neural network for foodquality evaluation. By detecting five different species of sauce, the "gas"fingerprintinformation were used to establish "odor fingerprint database", then the unknown saucecan be accurately distinguished between different species of sauce.Firstly, a typical electronic nose system consists of sensor arrays or samplingstructure, data acquisition system and pattern recognition algorithms. According to thestructure and working principle of electronic nose, framework of electronic nose wasdesignedfor food quality evaluation. System consists of sampling system, control systemand PC software.Secondly, design of the electronic nose system.By using six figaro metal oxidesemiconductor sensors,a gas sensors array was assembled. Sensors drive circuit andsensors array were integrated on a circuit board, which were placed in glass container(5L).In order to reduce the influence of external environment, a water bath heating system wasused to guarantee the test ambient temperature constant.The temperature was monitoredbyMicro Controller. Data acquisition system and sensors array driver circuit wereconnected by cable. By using24-bit high precision ADC AD7794as electronic nosesignal acquisition unit and32-bit microprocessor STM32F103ZE, control system for theelectronic noses undertakenall the peripherals and data collection. Based on microcontroller integrated USART (universal asynchronous receiver/transmitter) and RS232communication mode, data was uploadedto PC software. Software was built underLabVIEW platform which embedded Matlab scripts.By obtaining the response of thesensors array, sorting dataand through the principal component analysis (PCA) method orlearning vector quantization (LVQ) neural network pattern recognition algorithm toanalyze the data, e-nose got the fingerprint information and compared the characteristicsof the data in the database, then presented eventually results.Thirdly, the e-nose system tested five kinds of sauce. The original database was builtby detecting of five kinds of sauce, and then the unknown sauce was tested. By usingPCA and LVQ neural network analysis method, six kinds of sauce(five kinds of sauce andan unknown sauce)were analyzed. As a result, the unknown sauce can be evaluatedaccurately by e-nose. And the experiment hadproved that sensors array’ssensitivitycharacteristicis more convincible than resistanceresponse.In conclusion, an electronic nose for food quality evaluation was developed, whichcould work in learning mode or detection mode respectively, to be able to complete thelearning of standard sample, to establish "gas fingerprint database", and complete thequality evaluation of food. Electronic nosehas very important significance and prospectand needs more research.