Iot Based Data Acquisition And Modeling Methods For Food Quality Perception In Cold Chain

The cold chain quality perception technology is an important measure for ensuring the food quality and safety.There exists the characteristics of multi-source coupling,the quality parameters diversity and the information asymmetry in the actual cold chain,which hinder the development,applications and promotion of the cold chain quality perception technology.In this paper,based on the requirements analysis in the actual cold chain,the hardware and software of the IoT were developed and implemented,the data acquisition method based on compressed sensing were built and exampled to realize the sampling and transmission of the sensing data in the cold chain,the critical quality model was built based on the correlation analysis and kinetics equation and the traceability models were also built by adopting the statistical process control and QR code.Finally,an effective and practical IoT's data acquisition and modeling methods for the food quality perception in the cold chain were established.The main contributions of this study are as follows.Firstly,the workflow of the cold chain was detailed analyzed and the data acquisition and modeling formalization of Internet of Things for food quality perception in cold chain was also analyzed and built to provide the clear context for the further research work in this paper.Secondly,the hardware and software of the IoT were developed and implemented based on the analyzed characteristics and requirements of the cold chain to realize the real time sensing data acquisition and transmission in the actual cold chain,and provide the sensing data for further modeling?Thirdly,the data sparse sampling model based biorthogonal wavelet transformation,the data transmission model and the data reconstruction model were built according to the sensing data characteristic in the cold chain to improve the stability and transmission efficiency in the WSN,Fourthly,the critical quality parameters in the cold chain were determined by the correlation analysis among the other quality parameters,and the shelf-life prediction model was built based the critical quality.The example was given in the actual table grapes cold chain.Finally,two kinds of the traceability model were designed and implemented in the actual aquatic products cold chain.One is the traceability model based on the statistical process control,and the other is based on the QR code.They were all evaluated in the aquatic products cold chain.The results indicate that the control chart designed by statistical process control could real time monitor and control the temperature data in the cold chain.Once the abnormal condition occurred,the statistical process control would give an early warning and sending the abnormal message to the managers for solving the problem in time.The users could also inquiry the static information of the products and the real time temperature fluctuation in the food cold chain.