NIRS model prediction of air-dry density of Pinus sylvestris wood based on long short-term memory network(LSTM)
[Objective]Wood density is not only related to various wood properties,but also an important indicator to evaluate the quality and value of wood.The NIRS analysis technique can predict wood density quickly and efficiently,avoiding the tedious detection steps in conventional experiments.As a variant of recurrent neural network(RNN),long short-term memory network(LSTM)can not only learn the high-order characteristic information between sequential data,but also handle the problems of long-distance dependencies,gradient explosion and gradient extinction in RNN.By combining LSTM and NIRS,a non-destructive detecting technique that can accurately predict the air-dry density of Pinus sylvestris wood was proposed to provide a theoretical basis for improving the accuracy of the NIRS model in predicting the air-dry density of wood.[Method]In this study,the spectral data of 106 samples of P.sylvestris were obtained by NIRS spectrometer,and the air-dry density of these samples was obtained with NIRS spectrometer under constant temperature(20±2℃)and relative humidity(65%±3%)environment.By comparing multiple groups of pretreatment methods and feature selection methods,Savitzky-Golay smoothing(SGS)and other methods were used for pretreatment,competitive adaptive weighting algorithm(CARS)was used for band selection,eliminating high-frequency noise and redundant information in NIRS data,and improving spectral quality,modeling speed and accuracy.In order to verify the predictive ability of LSTM model,it was compared and analysed with the modelling algorithms such as partial least square regression(PSLR),convolutional neural network(CNN).Each of these above-mentioned three modelling methods were applied to establish a near-infrared prediction model for the air-dry density of P.sylvestris wood.[Result]The NIRS models established based on the above-mentioned modelling methods achieved effective prediction of the air-dry density of P.sylvestris wood.And the prediction accuracy and regression fit of LSTM model were better than PLSR and CNN model.The LSTM model treated by SGS+CARS had the highest prediction accuracy,the strongest generalization performance and the best fitting effect(R2=0.959,RMSEP=0.005,RPD=5.033).[Conclusion]Through the collection of spectral data and air-dry density of pine sylvestris wood,a novel method for detecting air-dry density of wood based on NIRS and LSTM was established.Comparing to the traditional regression model,LSTM model has higher prediction accuracy,better regression effect and stronger robustness.The detection method can not only ensure the integrity of the wood,but also improve the prediction accuracy of the air-dry density,which achieves the rapid non-destructive detection of the air-dry density of the Pinus sylvestris wood,and provides a referable model and theoretical basis for the NIRS analysis of wood.
long short-term memory networknear infrared spectroscopyPinus sylvestrisair-dry density
万方数据
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