便携式猪肉营养组分无损实时检测装置研究

摘要

In order to realize fast,nondestructive and real-time detection of nutrition components (fat and protein) for pork,a portable nondestructive detection device based on near infrared reflectance spectra was designed and developed.The hardware part included spectrum acquisition unit,light source unit and control unit.The corresponding detection software was developed to realize the effective acquisition and real-time analysis of the sample spectrum information.In order to establish a stable and reliable forecasting model,the research focused on the effects of band selection,different sample grouping methods and variables selection methods on the models.Based on visible/short wavelength near infrared (Vis/SWNIR),long wavelength near-infrared (LWNIR) and Vis/SWNIR-LWNIR,all the samples were divided by random selection (RS) method,Kennard-Stone (KS) algorithm and sample set partitioning based on joint X-Y distances (SPXY) algorithm,and then partial least square prediction models for fat and protein content were built,respectively.The results showed that the best prediction models for fat and protein were built based on Vis/SWNIR-LWNIR by using SPXY algorithm.On the basis of the best model for each parameter,comparative analysis of competitive adaptive weighted algorithm,Random Frog algorithm and uninformative variable elimination-successive projection algorithm were employed to screen variables.The results showed that the simplified model based on competitive adaptive weighting algorithm was the best with correlation coefficients in the prediction set of 0.950 5 and 0.951 0 for fat and protein,respectively.The results indicated that the designed portable detection device based on near infrared reflectance spectroscopy was able to realize fast,nondestructive and real-time detection of fat and protein content for fresh meat and had certain application potential and market prospects.%为了实现猪肉营养组分(脂肪和蛋白质)的快速、无损、实时检测,基于近红外反射光谱设计了便携式猪肉营养组分无损检测装置.硬件部分包括光谱采集单元、光源单元和控制单元,并开发了相应的检测软件,实现样品光谱信息的有效获取和实时分析.为了建立稳定可靠的预测模型,考察了波段选择、样本分组方式和筛选变量方法对模型的影响.分别基于可见/短波近红外(Vis/SWNIR)、长波近红外(LWNIR)及Vis/SWNIR-LWNIR,利用随机选择法(RS)、Kennard-Stone法(KS)和基于联合X-Y距离的样本划分法(SPXY)对样本进行划分,建立了脂肪和蛋白质质量分数的偏最小二乘预测模型.结果发现,基于Vis/SWNIR-LWNIR波段,利用SPXY算法进行样本分组,取得了最佳的预测模型.在此基础上,比较分析竞争性自适应加权算法、随机蛙跳算法和蒙特卡罗无信息变量消除-连续投影算法3种算法筛选变量建立的模型效果.基于竞争性自适应加权算法筛选变量的模型结果最佳,对脂肪和蛋白质建立的模型验证集相关系数分别为0.950 5和0.951 0.结果表明:基于近红外反射光谱设计的便携式猪肉组分检测装置可以对脂肪和蛋白质含量进行快速、无损、实时检测.