Rapid Determination of Protein Components and Their Subunits in Peanut Based on Near Infrared Technology
ZHAO Si-meng1, YU Hong-wei1, GAO Guan-yong2, CHEN Ning2, WANG Bo-yan3, WANG Qiang1*, LIU Hong-zhi1*
1. Institute of Agro-Food Science and Technology, Chinese Academy of Agricultural Sciences，Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China
2. Jinsheng Grain and Oil Group Co., Ltd., Lünan 276600, China
3. Beijing University of Agriculture, Beijing 102206, China
摘要： 花生球蛋白、伴花生球蛋白及亚基含量显著影响蛋白质的凝胶性和溶解性等功能特性，进而影响其在肉制品、植物蛋白饮料中的应用效果。目前常采用提取蛋白质后再用电泳及光密度法测定球蛋白、伴球蛋白及亚基含量的方法，操作步骤繁琐，样品损失量大。为此收集了178个花生品种，分别提取蛋白，采用电泳法测定球蛋白、伴球蛋白、23.5和37.5 kDa亚基含量并获得大量数据的基础上，利用近红外光谱技术进行整粒花生样品的光谱扫描，将其与传统方法测定的化学值进行拟合，采用偏最小二乘回归（PLSR）化学计量法构建数学模型。通过比较单一和复合光谱预处理方式，对比模型相关系数和误差评估预测模型性能。确定球蛋白模型最佳预处理方法为2nd-der with Detrend，校正集相关系数为0.92，标准差为1.41；伴球蛋白模型最佳预处理方法为Detrend with 1st-der，校正集相关系数为0.85，标准差为1.46；23.5 kDa亚基含量模型最佳预处理方法为Normalization with 2nd-der，校正集相关系数为0.91，标准差为0.53；37.5 kDa模型最佳预处理方法为Detrend with Baseline，校正集相关系数为0.91，标准差为0.89。外部验证结果表明，球蛋白预测均方根误差（square errors of prediction, SEP）为1.25，伴球蛋白SEP为0.73，23.5 kDa模型SEP为0.47，37.5 kDa模型SEP为0.75。本研究基于近红外光谱技术实现了对整粒花生进行球蛋白、伴球蛋白、23.5 kDa和37.5 kDa亚基含量的同步、快速和无损检测，为育种专家加工专用品种选育和蛋白加工企业原料选用提供了根据。
Abstract：The contents of arachin, conarachin and subunits significantly affect the gel properties and solubility of peanut proteins, and then affect its application in meat products and beverage. In this study, we collected 178 peanut varieties, measured arachin, conarachin, 23.5 and 37.5 kDa subunits contents by chemical methods. On the basis of peanut sample spectrum scan by near-infrared spectrum technology, we used Partial Least Squares Regression (PLSR) stoichiometry to build a mathematical model with the chemical data. By comparing single and composite spectral pretreatments, model correlation coefficient and errors to value the performance of the models. The best pretreatment method for arachin model was determined as 2nd-der with Detrend, the correlation coefficient of correction (Rc) set was 0.92, and the standard error of calibration (SEC) was 1.41; the best pretreatment method of conarachin model was detrended with 1st-der, the Rc and SEC were 0.85 and 1.46; the best pretreatment method for the 23.5 kDa subunit model was Normalization with 2nd-der, the Rc and SEC were 0.91 and 0.53; Detrend with Baseline was the best pretreatment method for the 37.5 kDa model, the Rc and SEC was 0.91 and 0.53. External validation results showed the Square Errors of Prediction (SEP) of arachin and conarachin were 1.25 and 0.73, respectively. The SEP of 23.5 kDa model and 37.5 kDa model were 0.47 and 0.75 separately. In this study, the contents of arachin, conarachin, 23.5 and 37.5kDa subunits in the whole peanut were detected simultaneously, rapidly and non-destructively based on NIRS. It’s important for the breeding specialist to select special varieties and raw materials for the protein processing industry.
Key words：Near infrared spectral analysis; Arachin; Conarachin; Subunit content (23.5 kDa and 37.5 kDa); Partial least squares regression (PLSR)
赵思梦，于宏威，高冠勇，陈 宁，王博妍，王 强，刘红芝. 花生蛋白组分及其亚基含量近红外分析检测方法[J]. 光谱学与光谱分析, 2021, 41(03): 912-917.
ZHAO Si-meng, YU Hong-wei, GAO Guan-yong, CHEN Ning, WANG Bo-yan, WANG Qiang, LIU Hong-zhi. Rapid Determination of Protein Components and Their Subunits in Peanut Based on Near Infrared Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 912-917.
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