近红外光谱技术对花生蛋白组分与亚基的高通量模型建立

doi:10.3964/j.issn.1000-0593(2024)07-1982-06

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摘要：花生是一种优质植物蛋白资源，花生蛋白组分与亚基含量显著影响其功能特性，决定了其在食品领域的应用范围，花生蛋白主要包括花生球蛋白和伴花生球蛋白，其中花生球蛋白包含4个亚基（40.5、37.5、35.5和23.5 kDa），伴花生球蛋白Ⅰ包含3个亚基（15.5、17和18 kDa），伴花生球蛋白Ⅱ只含有1个61 kDa亚基。为了实现花生蛋白主要组分及亚基的快速无损、高通量、高灵敏度检测，以全国145份优质花生样品为研究对象，首先采用便携式近红外花生品质速测仪，在900～1 700 nm波长范围内对不同花生样品进行光谱采集，再采用聚丙烯酰胺凝胶电泳法测定了花生蛋白组分与亚基含量，花生球蛋白的亚基含量在44.3%～67.3%之间；伴花生球蛋白亚基含量在35.2%～55.7%之间；61 kDa亚基含量在13.5%～25.3%之间；40.5 kDa亚基含量在6.8%～16.0%之间；37.5 kDa亚基含量在6.9%～17.4%之间；35.5 kDa亚基含量在5.7%～19.2%之间；23.5 kDa亚基含量在18.7%～27.4%之间；18 kDa亚基含量在5.9%～11.7%之间；17 kDa亚基含量在6.9%～13.6%之间；15.5 kDa亚基含量在4.5%～11.9%之间，分别对比归一化（Normalize）、一阶导数（FD）和二阶导数处理（SD）、基线校准（Baseline）、去趋势（Detrend）、多元散射校正（MSC）和数据元素解析处理（Deresolve）这7种光谱预处理方法，结合主成分分析（PCA）和偏最小二乘法（PLSR），优化了2个蛋白组分和2个亚基（花生球蛋白、伴花生球蛋白、37.5和23.5 kDa）的近红外光谱模型，构建了6个亚基（61、40.5、35.5、18、17和15.5 kDa）的近红外光谱模型，实现了对上述10个指标的同步检测。结果表明，确定最优预处理方式后建立的模型校正集相关系数（R_cal）为0.90～0.96，校正均方根误差（SEC）为0.25%～1.27%；预测集相关系数（R_cp）为0.76～0.96，预测均方根误差（SEP）为0.50%～1.81%，具有很好的预测能力，可用于花生品种蛋白组分与亚基含量的快速检测，为花生蛋白品质的快速评价提供了新方法。

关键词：近红外光谱；花生；蛋白组分；亚基含量；偏最小二乘法

Abstract：Peanut is a high-quality plant protein resource. The content of peanut protein components and subunits significantly affects its functional characteristics and determines its application range in the food field. Peanut proteins mainly include arachin and arachin. Among them, arachin contains four subunits (40.5, 37.5, 35.5, 23.5 kDa), arachin I contains three subunits (15.5, 17, 18 kDa), and arachin II contains only one 61 kDa subunit. To realize the rapid, non-destructive, high-throughput and high-sensitivity detection of peanut protein's main components and subunits, 145 high-quality peanut samples in China were used as the research object. Firstly, the portable near-infrared peanut quality rapid tester was used to collect the spectra of different peanut samples in the wavelength range of 900~1 700 nm. Then, the peanut protein components and subunits were determined by polyacrylamide gel electrophoresis. The subunit content of arachin was between 44.3% and 67.3%. The content of arachin subunits was between 35.2% and 55.7%. The 61 kDa subunit content ranged from 13.5% to 25.3%. The content of 40.5 kDa subunit was between 6.8% and 16.0%. The content of 37.5 kDa subunit was between 6.9% and 17.4%. The content of 35.5 kDa subunit was between 5.7% and 19.2%. The 23.5 kDa subunit content was between 18.7% and 27.4%. The content of the 18 kDa subunit was between 5.9% and 11.7%. The content of the 17 kDa subunit was between 6.9% and 13.6%. The content of the 15.5 kDa subunit was 4.5%~11.9%. The near-infrared spectral models of two protein components and two subunits (arachin, arachin, 37.5, 23.5 kDa) were optimized by comparing seven spectral pretreatment methods, including normalization, first derivative (FD) and second derivative (SD), baseline calibration, detrend, multiple scattering corrections (MSC) and data element resolution (Deresolve), combined with principal component analysis (PCA) and partial least squares (PLSR). The near-infrared spectroscopy models of six subunits (61, 40.5, 35.5, 18, 17, 15.5 kDa) were constructed, and the simultaneous detection of the above 10 indicators was realized. The results showed that the calibration set's correlation coefficient (R_cal) 0.90～0.96, and the corrected root mean square error (SEC) was 0.25%～1.27%. The prediction set's correlation coefficient (R_cp) was 0.76～0.96, and the root mean square error of prediction (SEP) was 0.50%～1.81%. It has good predictive ability and can be used for rapid detection of protein components and subunit content in peanut varieties, which provides a new method for fast evaluation of peanut protein quality.

Key words：Near-infrared spectroscopy; Peanuts; Protein components; Subunit content; Partial least squares method

收稿日期: 2023-03-16 修订日期: 2023-10-20

中图分类号:

S123

基金资助: 国家花生产业技术体系(CARS-13)，国家自然科学基金联合基金项目（U21A20270），新疆自治区重点研发计划课题项目（2021B02003-4），新疆维吾尔自治区重大科技专项（2022A02008），河北省油料产业体系项目(HBCT2019090203)

通讯作者: 郭芹，王强 E-mail: guoqin2010yl@163.com；wangqiang06@caas.cn

作者简介: 崔颢凡，1999年生，中国农业科学院农产品加工所硕士研究生 e-mail: cuihaofan1999@163.com

引用本文:

崔颢凡，刘红芝，郭芹，顾丰颖，张雨，王强. 近红外光谱技术对花生蛋白组分与亚基的高通量模型建立[J]. 光谱学与光谱分析, 2024, 44(07): 1982-1987.
CUI Hao-fan, LIU Hong-zhi, GUO Qin, GU Feng-ying, ZHANG Yu, WANG Qiang. Establishment of High-Throughput Model of Peanut Protein Components and Subunits by Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1982-1987.

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