鲜食玉米果穗不同部位含水率近红外光谱建模研究

Near Infrared Spectroscopy Modeling of Moisture Content in Different Parts of Fresh Corn Ear

摘要:

含水率是评价鲜食玉米果穗品质的重要指标之一,影响鲜食玉米的品质分级。由于鲜食玉米果穗具有棒状且粗细不一的形态,表面籽粒呈现排布凹凸不平的特殊物理特性,对鲜食玉米果穗的近红外光谱获取和含水率预测建模分析产生了极大影响,因此有必要开展鲜食玉米果穗不同部位及点位含水率近红外光谱建模研究。首先,使用自制的近红外无损检测综合实验装置采集鲜食玉米果穗首段、中段和尾段3个区域的360°光谱,每个区域内间隔60°采集6个点位。其次,通过Z-score进行异常值剔除,结合无预处理(NONE)、标准正态变换(Standard normal variate,SNV)、多元散射校正(Multiplicative scatter correction,MSC)、一阶导数(First derivative,1D)、二阶导数(Second derivative,2D)和Savitzky-Golay卷积平滑(Savitzky-Golay smoothing,SG)等进行预处理。然后,采用光谱理化值共生距离(Sample set portion based on joint x-y distance,SPXY)算法将其划分为校正集和预测集。最后,采用偏最小二乘法(Partial least squares regression,PLSR)分别建立包含所有区域数据的全局预测模型和不同区域数据的局部预测模型。进一步探究了果穗中段不同采集点位数量对预测模型的影响,并分别构建了不同采集点位数量(1、2、3、4、5、6)下的预测模型。结果显示,全局预测模型的最佳建模结果R2p、RMSEP和RPD分别为0.905、0.011%和3.270;局部预测模型中果穗中段建模效果最佳且泛化能力更强,其最佳建模结果R2p、RMSEP和RPD分别为0.955、0.007%和4.884;当采集点位数量为5时,模型预测精度达到最佳,其R2p和R2c分别为0.967和0.974。研究表明,选择鲜食玉米果穗中段及5个采集点位的方案能够建立最佳鲜食玉米果穗含水率预测模型。

Abstract:

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, R2 p, 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, R2 p, 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 R2 p and R2 c 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.