基于高光谱成像技术的青贮玉米饲料pH值无损检测  

Nondestructive detection of the pH value of silage maize feeds based on hyperspectral images

作　　者：张梦宇 郝敏[1] 田海清[1] 李鹏宇 赵凯 任仙国 ZHANG Mengyu;HAO Min;TIAN Haiqing;LI Pengyu;ZHAO Kai;REN Xianguo(College of Mechincal and Electrical Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China)

机构地区：[1]内蒙古农业大学机电工程学院,呼和浩特010018

出　　处：《农业工程学报》2023年第4期239-247,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(32071893);内蒙古自治区科技计划项目(2022YFDZ0024)联合资助。

摘　　要：为实现青贮玉米饲料pH值的快速、无损检测,该研究采用高光谱成像技术建立不同品质青贮玉米饲料pH值的定量检测模型。采集青贮玉米饲料样本936~2539nm的平均光谱,采用6种预处理方法对青贮玉米饲料平均光谱进行处理,通过建立偏最小二乘回归(partialleastsquaresregression,PLSR)模型得出多元散射校正(multiplicativescatter correction,MSC)和卷积平滑(savitzky-golay,SG)两种预处理方法效果较好,使用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)、变量组合集群分析算法(variable combination population analysis,VCPA)以及迭代保留信息变量(iteratively retains informative variables,IRIV)算法对经MSC和SG卷积平滑预处理光谱进行特征波长提取,利用PLSR和极限学习机(extreme learning machines,ELM)分别建立饲料全波段、特征波长的pH值预测模型。MSC-CARS-PLSR为最优算法组合,其校正集决定系数为0.9262,均方根误差为0.4213,预测集决定系数为0.9170,均方根误差为0.4266。研究结果表明,结合PLSR模型可以实现对青贮玉米饲料pH值的准确预测,可为青贮玉米饲料pH值提供一种可靠且有效的无损检测新方法。The fermentation of whole maize silage relies mainly on the reaction of lactic acid bacteria.Among them,the soluble carbohydrates in the raw material can be converted rapidly into organic acids(mainly lactic acid),resulting in a rapid decrease in the pH of the maize silage.At the same time,the degradation of nutrients can be inhibited in the silage free by other aerobic microorganisms,thus preserving the nutrients of the feed.As such,the pH value can be expected to serve as the main influencing factor on the quality of the maize silage.Particularly,the rate of pH reduction has a great impact influence on the preservation of protein.Therefore,it is very necessary to accurately and rapidly detect the pH value in the maize silage.This study aims to achieve the rapid and nondestructive detection of the pH value in the maize silages using hyperspectral techniques.A pH content prediction model was developed for the different qualities of maize silages.The mean spectra of silages were sampled from the range of 936-2539 nm by a hyperspectral imaging system.The spectral information contained the useful physicochemical information of silage maize feed and the interference information(such as the dark current and light scattering),because the hyperspectral data was susceptible to the instrument noise and surrounding environment.There were also more burrs in the original spectral curve of silage maize feed after detection.Therefore,spectral preprocessing was used to reduce the interference signals for the subsequent modeling,in order to improve the accuracy and stability of the detection model of silage maize feed quality.Firstly,six pre-processing methods were used to treat the feed spectral data,including the multiplicative scatter correction(MSC),standard normalized variables(SNV),Savitzky-Golay smoothing(S-G),orthogonal signal correction(OSC),the first-order derivative,and the second-order derivative.A partial least squares(PLS)regression model was then constructed to derive two well-effective preprocessing,namely the MS

关 键 词：无损检测 高光谱 PH值 青贮玉米 饲料 

分 类 号：S147.2[农业科学—肥料学]