Research And Development Of Online Near Infrared Detection System For Main Grain Moisture

There is still a big gap between the processing level of agricultural products and the advanced countries in the world.Timely obtaining the information of main grain chemical component in the processing can help to choose better process parameters,improve the quality of agricultural products,and improve the economic efficiency of farmers and agricultural processing industries.Near infrared spectroscopy(NIR)has the characteristics of rapid and nondestructive testing,and is widely used in food fields.The project developed an online water detection system for main grains based on near infrared spectroscopy technology,which can collect spectral data in real time and predict physical and chemical indexes,and provide an information source of component content for the digital transformation of agricultural products processing.The main research contents are as follows:(1)Complete the hardware design and component selection of the online near infrared detection system with the moisture of rice as an example,simulate the belt conveyor mechanism in the main grain production and processing,the use of speed regulating motor drive sprocket rotation to provide power for the system,so that the system is more in line with the actual production;Based on the NIR-NT miniature near infrared spectrometer,the hardware selection and space installation structure of halogen tungsten lamp,optical fiber and reference board were rationally configured.The communication board circuit was developed to compose the near infrared spectrum detection device,and the diffuse reflection near infrared spectrum data collection of samples was realized.Select PLC as the lower machine to complete the design of electrical control components;The online near infrared detection system is also equipped with temperature and humidity sensors,weighing sensors,vibration sensors,encoders and other sensors to monitor the working status of the online detection system in real time,ensuring the digital and information requirements of the production process.(2)Completed the software design of online near infrared detection system.C#language was used to write the upper computer software,and realized the functions of serial communication,spectral data acquisition,spectral display,data file saving,online detection,system monitoring,motor speed setting and so on.Using ladder diagram to write the lower computer software,the communication mode setting,motor speed control,encoder pulse signal acquisition and other functions are realized.(3)The performance of the online near infrared detection system is tested.The results show that the baseline stability of the near infrared spectrum collected by the online near infrared detection system is less than 0.000 3;The standard deviation is less than 0.010 when the spectral stability of rice is tested in dynamic state.By optimizing the detection height of the online near infrared detection system and the horizontal conveyor belt speed used for detection,it is concluded that when the sampling height is designed as 4 cm and the motor speed is set as 500 r/min,the spectral data collected is more accurate.The vibration analysis of the on-line near infrared detection system is carried out,and the phenomenon that the motor speed and the standard deviation of absorbance are inconsistent is analyzed.The test shows that when the motor speed is500 r/min,the vibration speed of the beam with the intermediate installation of the spectral detection component is small,and the absorbance standard deviation is also small,indicating that vibration is one of the important factors affecting the accuracy of the absorbance.(4)The rice purchased from the market was used as the test sample,and the moisture content was used as the predicted physical and chemical value to establish a quantitative prediction model and test the practical application performance of the online near infrared detection system.After collecting the moisture content data of rice samples,the near-infrared diffuse reflection spectrum of rice was collected at the detection height of 4 cm and the static state of horizontal conveyor belt.The K-S method was used to divide 150 sample data into correction set and prediction set according to the ratio of 7:3.After the spectral pretreatment,principal component regression PCR and partial least squares regression PLSR were used to establish a quantitative prediction model.The results show that the model established by polynomial convolution smoothing SGF combined with PLSR has the best performance.The correlation coefficient of correction set R_c is 0.979 1,the standard deviation of correction set RMSEC is 0.004 7,the correlation coefficient of verification set R_p is0.983 7,and the standard deviation of verification set RMSEP is 0.005 4.The model was called in the upper computer software for testing,and the online near infrared detection system was tested in accordance with the optimal working parameters.The results showed that the difference between the predicted value and the measured value of rice moisture content was only 0.7%,indicating that the model had good prediction performance in the production site,and the online near infrared detection system could meet the needs of practical applications.