便携式猪肉营养组分无损实时检测装置研究
发布时间：2018-08-21 12:40
【摘要】：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。
[Abstract]:In order to realize the fast, nondestructive and real-time detection of pork nutrient components (fat and protein), a portable nondestructive detection device for pork nutrient components was designed based on near-infrared reflectance spectroscopy (NIR). The hardware is composed of spectral acquisition unit, light source unit and control unit, and the corresponding detection software is developed to realize the effective acquisition and real-time analysis of the sample spectral information. In order to establish a stable and reliable prediction model, the effects of band selection, sample grouping and screening variables on the model were investigated. Based on visible / short wave near infrared (Vis/SWNIR), long wave near infrared (LIR) and Vis/ SWNIR-LWNIRs, samples were divided by random selection method (RS) Kennard-Stone method (KS) and sample partition method (SPXY) based on joint X-Y distance, respectively. A partial least square prediction model for the mass fraction of fat and protein was established. The results show that the best prediction model is obtained by using SPXY algorithm to group samples based on Vis/SWNIR-LWNIR band. On this basis, the model effects of three kinds of algorithms, namely competitive adaptive weighting algorithm, stochastic leapfrog algorithm and Monte Carlo non-information variable cancellation-continuous projection algorithm, are compared and analyzed. The model result based on competitive adaptive weighting algorithm is the best. The correlation coefficient of model verification set for fat and protein is 0.950 5 and 0.951 0 respectively. The results show that the portable pork component detection device based on near infrared reflectance spectroscopy can be used to detect fat and protein content quickly, nondestructive and in real time.
【作者单位】： 中国农业大学工学院;国家农产品加工技术装备研发分中心;
【基金】：国家重点研发计划项目(2016YFD0401205) 公益性行业(农业)科研专项(201003008)
【分类号】：O657.33;TS251.51
本文编号：2195767
[Abstract]:In order to realize the fast, nondestructive and real-time detection of pork nutrient components (fat and protein), a portable nondestructive detection device for pork nutrient components was designed based on near-infrared reflectance spectroscopy (NIR). The hardware is composed of spectral acquisition unit, light source unit and control unit, and the corresponding detection software is developed to realize the effective acquisition and real-time analysis of the sample spectral information. In order to establish a stable and reliable prediction model, the effects of band selection, sample grouping and screening variables on the model were investigated. Based on visible / short wave near infrared (Vis/SWNIR), long wave near infrared (LIR) and Vis/ SWNIR-LWNIRs, samples were divided by random selection method (RS) Kennard-Stone method (KS) and sample partition method (SPXY) based on joint X-Y distance, respectively. A partial least square prediction model for the mass fraction of fat and protein was established. The results show that the best prediction model is obtained by using SPXY algorithm to group samples based on Vis/SWNIR-LWNIR band. On this basis, the model effects of three kinds of algorithms, namely competitive adaptive weighting algorithm, stochastic leapfrog algorithm and Monte Carlo non-information variable cancellation-continuous projection algorithm, are compared and analyzed. The model result based on competitive adaptive weighting algorithm is the best. The correlation coefficient of model verification set for fat and protein is 0.950 5 and 0.951 0 respectively. The results show that the portable pork component detection device based on near infrared reflectance spectroscopy can be used to detect fat and protein content quickly, nondestructive and in real time.
【作者单位】： 中国农业大学工学院;国家农产品加工技术装备研发分中心;
【基金】：国家重点研发计划项目(2016YFD0401205) 公益性行业(农业)科研专项(201003008)
【分类号】：O657.33;TS251.51
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