不同种皮颜色花生蛋白质及其组分含量近红外检测模型的构建  

Construction of Near-Infrared Detection Models for Peanut Protein and Their Components With Different Seed Coat Colors

作　　者：尚艳侠 侯名语[1] 崔顺立[1] 刘盈茹 刘立峰[1] 李秀坤 SHANG Yan-xia;HOU Ming-yu;CUI Shun-li;LIU Ying-ru;LIU Li-feng;LI Xiu-kun(State Key Laboratory of North China Crop Improvement and Regulation,Key Laboratory of Crop Germplasm Resources Research and Utilization in North China,Ministry of Education,Key Laboratory for Crop Germplasm Resources in Hebei Province,Hebei Agricultural University,Baoding 071001,China)

机构地区：[1]华北作物改良与调控国家重点实验室,华北作物种质资源研究与利用教育部重点实验室,河北省作物种质资源实验室,河北农业大学农学院,河北保定071001

出　　处：《光谱学与光谱分析》2025年第4期1129-1136,共8页Spectroscopy and Spectral Analysis

基　　金：河北省现代农业产业技术体系建设专项资金(HBCT2024040205);国家现代农业产业技术体系建设专项(CARS-13);河北省现代种业科技创新专项(21326316D);河北省省属高校基本科研业务费研究项目(KY2021036);河北农业大学引进人才科研专项(YJ2020044);华北作物调控与改良国家重点实验室引进人才科研专项(NCCIR2020RC-2)资助。

摘　　要：花生籽仁中蛋白质及其组分含量是重要的品质性状,探索无损、高效的含量检测方法是花生育种及生产的重要研究方向。根据样品外观颜色分别构建近红外模型有利于提高检测的准确性。利用市场常见的黑色、红色、粉色共282个花生种质,采用考马斯亮蓝法(Bradford)检测蛋白含量,与近红外光谱值拟合,采用偏最小二乘回归(PLSR)法构建近红外预测模型。共构建黑色种皮粗蛋白、黑色种皮清蛋白、黑色种皮球蛋白、黑色种皮伴球蛋白、红色种皮粗蛋白、红色种皮清蛋白、红色种皮球蛋白、红色种皮伴球蛋白、粉色种皮清蛋白、粉色种皮球蛋白、粉色种皮伴球蛋白等共11个近红外预测模型,光谱值预处理方法为多种复合处理法,黑色种皮粗蛋白、黑色种皮清蛋白、黑色种皮球蛋白、黑色种皮伴球蛋白模型的最佳预处理方法分别为Baseline+Detrend、Detrend+MSC、2^(nd)-der+Detrend+1^(st)-der、Baseline+SNV,红色种皮粗蛋白、红色种皮清蛋白、红色种皮球蛋白、红色种皮伴球蛋白模型的最佳预处理方法分别为Baseline+SNV、Baseline+SNV+MSC、SNV+MSC+Baseline、SNV+MSC+Baseline,粉色种皮清蛋白、粉色种皮球蛋白、粉色种皮伴球蛋白模型的最佳预处理方法分别为2^(nd)-der+1^(st)-der、2^(nd)-der+Detrend+1^(st)-der、2^(nd)-der+Baseline+1^(st)-der,所建模型的相关系数Rc为0.825~0.925,均方根误差RMSEC为0.110~1.383。对所建模型进行外部验证,11个模型的外部验证集相关系数Rp在0.822~0.971之间,均方根误差RMSEP在0.102~0.954之间。将不同种皮颜色花生利用其他颜色模型检测并与其化学值进行拟合,相关系数在0.002~0.877范围内,标准误差在0.257~9.464范围内,相关系数均低于外部验证集相关系数,最佳检测模型为对应种皮颜色的模型。构建的不同种皮颜色的花生蛋白及其组分含量的模型,可快速无损的检测其含量,为花生蛋白加工原料The protein and its component content in peanut seed is an important quality trait of peanuts.Exploring non-destructive and efficient content detection methods is an important research direction of peanut breeding and production.Constructing near-infrared models according to the color of the sample's appearance is beneficial for improving detection accuracy.This study used 282 peanut germplasms with black,red,and pink coats to detect protein content by the Bradford method and the near-infrared spectral value.The Partial Least Squares Regression(PLSR)method was used to construct the near-infrared prediction model.A total of 11 near-infrared prediction models were constructed,including black seed coat crude protein,black seed coat albumin,black seed coat reaching,black seed coat contracting,red seed coat crude protein,red seed coat albumin,red seed coat reaching,red seed coat contracting,pink seed coat albumin,pink seed coat reaching,pink seed coat contracting,etc.The spectral value preprocessing method was a variety of composite processing methods.The best pretreatment methods for black seed coat crude protein,black seed coat albumin,black seed coat reaching,and black seed coat contracting models were Baseline+Detrend,Detrend+MSC,2^(nd)-der+Detrend+1^(st)-der,Baseline+SNV,respectively.The best pretreatment methods of red seed coat crude protein,red seed coat albumin,red seed coat reaching,and red seed coat contracting model were Baseline+SNV,Baseline+SNV+MSC,SNV+MSC+Baseline,SNV+MSC+Baseline,respectively.The best pretreatment methods of pink seed coat albumin,pink seed coat reaching,and pink seed coat contracting model were 2^(nd)-der+1^(st)-der,2^(nd)-der+Detrend+1^(st)-der and 2^(nd)-der+Baseline+1^(st)-der,respectively.The model's correlation coefficient(R c)was 0.825~0.925,and the root means standard error of calibration(RMSEC)was 0.110~1.383.The correlation coefficient(R p)of the external validation set of the 11 models ranged from 0.822 to 0.971,and the root mean standard error of prediction(RMSEP)ranged fro

关 键 词：花生 近红外光谱分析 种皮颜色 蛋白质 蛋白质组分 

分 类 号：S123[农业科学—农业基础科学]