光谱特征变量和BP神经网络构建油用牡丹种子含水率估算模型  

Estimation of moisture content in peony seed oil using spectral characteristic variables and BP neural network

作　　者：刘秀英[1,2,3] 余俊茹 王世华 Liu Xiuying;Yu Junru;Wang Shihua(College of Agronomy,Henan University of Science and Technology,Luoyang 471023,China;Luoyang Key Laboratory of Symbiotic Microorganism and Green Development,Luoyang 471023,China;Luoyang Key Laboratory of Plant Nutrition and Environmental Ecology,Luoyang 471023,China)

机构地区：[1]河南科技大学农学院,洛阳471023 [2]洛阳市共微生物与绿色发展重点实验室,洛阳471023 [3]洛阳市植物营养与环境生态重点实验室,洛阳471023

出　　处：《农业工程学报》2020年第22期308-315,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：河南省科技公关项目(182102110206);河南科技大学博士科研启动基金(13480074);河南科技大学大学生研究训练计划项目(20200347)。

摘　　要：为了进一步提高种子含水率的高光谱估算精度,该研究测定了156份油用牡丹种子的近红外吸收光谱及其对应的含水率值,分析了近红外吸收光谱、一阶微分光谱、水分吸收特征参数与含水率的相关关系,构建了基于特征波长吸收光谱、特征波长一阶微分光谱、水分特征吸收参数和BP神经网络的油用牡丹种子含水率估算模型,并对模型进行了验证;再结合一元线性回归(SLR,Single Linear Regression)、逐步多元线性回归(SMLR,Stepwise MultipleLinear Regression)、偏最小二乘回归(PLSR,Partial Least Squares Regression)模型与BP神经网络(BPNN,BP Neural Network)模型进行比较。结果表明:1)油用牡丹种子含水率的吸收光谱特征波长位于1410、1900、1990 nm,一阶微分光谱特征波长位于1150、1950、2080 nm;2)以DF2080和AD2140为自变量建立的一元线性回归模型预测效果较优,在能够满足水分估算精度的情况下,是最优的选择方法。3)将优选的特征参数作为输入,实测含水率值作为输出,构建BP神经网络模型,其建模与验模R2分别为0.978和0.973,RMSE分别为0.220%和0.242%,而RPD值分别为6.478和5.889,与其他模型相比,BP神经网络模型的建模及预测精度均最高,是估算油用牡丹种子含水率的最优模型,其次为逐步多元线性回归模型。研究结果表明BP神经网络模型对种子含水率具有更好的预测能力,是估算油用牡丹种子含水率的有效方法。Tree peony seed has recently been introduced to produce a high-quality edible oil,rich in the green and organic nutritional ingredients.This study aims to explore the rapid detection for the content of moisture with the near-infrared spectroscopy(NIRS)in oil tree peony seed,and thereby to improve the accuracy of hyper-spectral estimation for the moisture content of peony seed oil.A specific modeling was developed to evaluate the moisture content in oil tree peony seeds,using the advanced hyper spectra technology.The near-infrared spectral reflectance measurements were used to collect the data in the wavelength of 350 to 2500 nm using the spectrometer(SVC HR-1024i).An oven drying method was selected to obtain the moisture content of seeds.156 samples were collected in total,two thirds of which were marked as the training set,and one third as the validation set.The constructed model was verified,according to the training set and the validation set.A systematic analysis was performed on the correlation between near-infrared absorption spectra,first derivative spectra,characteristic parameters of moisture absorption,and moisture content.The Single Linear Regression(SLR)models were established to evaluate the moisture content,according to the characteristic wavelength of absorption spectra,characteristic wavelength of first derivative spectra,and characteristic parameters of moisture absorption.Taking 2 characteristic wavelength first derivative spectra and 3 characteristic moisture absorption depth parameters as the input parameters,a BP Neural Network(BPNN)model was built,where the measured moisture content values were set as the output parameters.A Stepwise Multiple Linear Regression(SMLR)and Partial Least Squares Regression(PLSR)were used to simulate the moisture content,using the same input parameters.The predictive powers of SLR,SMLR and PLSR models were compared with that of the BPNN model.The results showed that:1)The characteristic wavelength of moisture content absorption spectrum was located at 1410,1900 an

关 键 词：水分 模型 近红外光谱 特征变量 BP神经网络 油用牡丹种子 

分 类 号：S379[农业科学—农产品加工]