鲜食玉米果穗不同部位含水率近红外光谱建模研究OA

Water content is one of the most important indicators for evaluating the quality of fresh corn cobs,which affects the quality grading of fresh corn.Because of the special physical characteristics of fresh corn cobs,such as the rod-like shape with different thickness and the unevenness of kernel rows on the surface,the near-infrared spectroscopic(NIRS)acquisition of fresh corn cobs and the prediction of water content modeling analysis have a great impact on the quality of fresh corn cobs,and therefore it is necessary to carry out the NIRS modeling study of the water content of fresh corn cobs in different collection areas and points.Firstly,the 360° spectra of the first,middle and last regions of the cob were collected by using a homemade NIR NDT device,with 60° intervals between each region and six point locations.Secondly,the outliers were rejected by Z-score,and combined with no preprocessing(NONE),standard normal variate(SNV),multiplicative scatter correction(MSC),first order derivative(1D),second order derivative(2D)and Savitzky-Golay smoothing(SG).Then,the sample set portion based on joint x-y distance(SPXY)algorithm was used to divide it into correction set and prediction set.Finally,the partial least squares regression(PLSR)was used to establish a global prediction model containing data from all regions and a local prediction model for data from different regions,respectively.The effects of different numbers of collection sites in the middle part of the cob on the prediction model were further explored,and the prediction models were constructed under different numbers of collection sites(1,2,3,4,5 and 6),respectively.The results showed that the modeling results of the global prediction model,R2p,RMSEP and RPD,were 0.905,0.011%and 3.270,respectively;the local prediction model had the best modeling effect and stronger generalization ability in the mid-section of the cob,with the modeling results,R2p,RMSEP and RPD,being 0.955,0.007%and 4.884,respectively;when the number of collection sites was 5,the predictive accuracy of the model was optimal,with R2p and R2c values of 0.967 and 0.974,respectively.The research result showed that the scheme of choosing the mid-section of fresh maize cob and five collection sites could establish the best predictive model.