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CAI Hairong, SONG Wenjing, LIN Jianben, et al. Construction and application of near-infrared spectroscopy analysis model for peanut quality traits[J]. Journal of South China Agricultural University, 2025, 46(4): 450-458. DOI: 10.7671/j.issn.1001-411X.202411011
Citation: CAI Hairong, SONG Wenjing, LIN Jianben, et al. Construction and application of near-infrared spectroscopy analysis model for peanut quality traits[J]. Journal of South China Agricultural University, 2025, 46(4): 450-458. DOI: 10.7671/j.issn.1001-411X.202411011

Construction and application of near-infrared spectroscopy analysis model for peanut quality traits

More Information
  • Received Date: November 06, 2024
  • Available Online: May 21, 2025
  • Published Date: June 15, 2025
  • Objective 

    To develop a near-infrared spectroscopy analysis model integrating the main quality traits of Arachis hypogaea L. (peanut), provide an efficient and convenient identification method for screening of peanut quality trait mutants, shorten the breeding process, and improve the breeding efficiency.

    Method 

    A total of 115 peanut germplasm materials were collected from major peanut-producing areas in China, 100 of which were utilised as a calibration set and the remaining 15 as a validation set. The Swedish Broadcom DA7200 near-infrared analyzer was used to collect spectral information. The fat content was determined by using the Soxhlet extraction method, the protein content by Kjeldahl determination, the total sugar and sucrose contents by acid hydrolysis-Rein-Einon’s method, and the content of each fatty acid by gas chromatography. The full wavelength spectral range was selected, and models were constructed by using partial least squares regression. The single and composite preprocessing methods were compared to select the best model that performed optimally under this spectral preprocessing by comparing the determination coefficients (R2) and root mean square errors of calibration (RMSEC) of different models. The 15 peanut germplasm materials in validation set were used for external validation of the optimal model for each trait. The mutants in the offspring of aerospace mutagenesis materials were screened by the best model to investigate the application value of the model.

    Result 

    A near-infrared spectral analysis model for 12 peanut quality traits was constructed. The R2 of the traits was higher than 0.85, with the exception of fat and arachidic acid contents. External validation also demonstrated that the R2 of the constructed model was greater than 0.85, with the exception of fat and arachidic acid contents. Furthermore, the oleic acid near-infrared analysis model screened 12 peanut oleic acid mutants from 805 aerospace mutagenic materials, with the oleic acid contents significantly higher than that of the wild type (P<0.001).

    Conclusion 

    The constructed model is an effective predictor of quality traits in peanuts and is suitable for the efficient detection of peanut kernel quality in mutants, germplasm resources and hybrid offspring populations.

  • [1]
    刘娟, 汤丰收, 张俊, 等. 国内花生生产技术现状及发展趋势研究[J]. 中国农学通报, 2017, 33(22): 13-18. doi: 10.11924/j.issn.1000-6850.casb17050016
    [2]
    廖伯寿, 殷艳, 马霓. 中国油料作物产业发展回顾与展望[J]. 农学学报, 2018, 8(1): 107-112.
    [3]
    VASSILIOU E K, GONZALEZ A, GARCIA C, et al. Oleic acid and peanut oil high in oleic acid reverse the inhibitory effect of insulin production of the inflammatory cytokine TNF-α both in vitro and in vivo systems[J]. Lipids in Health and Disease, 2009, 8: 25. doi: 10.1186/1476-511X-8-25.
    [4]
    金华丽, 李琳琳. 近红外光谱技术测定花生蛋白质含量研究[J]. 河南工业大学学报(自然科学版), 2014, 35(1): 26-29.
    [5]
    孙东雷, 卞能飞, 陈志德, 等. 花生种质资源表型性状的综合评价及指标筛选[J]. 植物遗传资源学报, 2018, 19(5): 865-874.
    [6]
    房元瑾, 孙子淇, 苗利娟, 等. 花生籽仁外观和营养品质特征及食用型花生育种利用分析[J]. 植物遗传资源学报, 2018, 19(5): 875-886.
    [7]
    雷永, 王志慧, 淮东欣, 等. 花生籽仁蔗糖含量近红外模型构建及在高糖品种培育中的应用[J]. 作物学报 , 2021, 47(2): 332-341.
    [8]
    PATTEE H E, ISLEIB T G, GIESBRECHT F G, et al. Investigations into genotypic variations of peanut carbohydrates[J]. Journal of Agricultural and Food Chemistry, 2000, 48(3): 750-756. doi: 10.1021/jf9910739
    [9]
    COLEMAN W M, WHITE J L, PERFETTI T. Characteristics of heat-treated aqueous extracts of peanuts and cashews[J]. Journal of Agricultural and Food Chemistry, 1994, 42(1): 190-194. doi: 10.1021/jf00037a034
    [10]
    王传堂, 王秀贞, 吴琪, 等. 鲜食花生感官品质主导分析、优异品系筛选与遗传力估算[J]. 花生学报, 2018, 47(4): 71-74.
    [11]
    常莉, 钱承敬, 史晓梅, 等. 基于近红外光谱技术的主产区玉米品质分析[J]. 中国粮油学报, 2024, 39(6): 36-42.
    [12]
    孙晓荣, 张晨光, 刘翠玲, 等. 近红外光谱技术对小麦粉品质定量快速检测[J]. 食品科技, 2023, 48(11): 246-252.
    [13]
    朱文博, 高锦红, 晁雨蕊, 等. 近红外光谱技术在茶品质及鉴别分析中的应用进展[J]. 山东化工, 2023, 52(17): 80-82. doi: 10.3969/j.issn.1008-021X.2023.17.022
    [14]
    何代弟, 张晓, 张楠楠, 等. 苹果内部品质近红外光谱无损检测研究进展[J]. 安徽农学通报, 2023, 29(16): 136-139. doi: 10.3969/j.issn.1007-7731.2023.16.031
    [15]
    丁坤, 项安. 近红外高光谱成像技术结合偏最小二乘-判别分析所建模型快速鉴定核桃仁的品质[J]. 理化检验: 化学分册, 2023, 59(7): 844-848.
    [16]
    黎海华, 陈文亮, 罗尔伦, 等. 基于近红外技术的快速酒精仪在乙醇浓度分析中的应用[J]. 现代食品, 2023, 29(12): 205-208.
    [17]
    乔继红, 苑希岩, 吴静珠, 等. 近红外光谱技术结合宽度学习系统识别国外奶粉产地[J]. 食品安全质量检测学报, 2023, 14(5): 9-15. doi: 10.3969/j.issn.2095-0381.2023.5.spaqzljcjs202305002
    [18]
    袁洁, 朱亚雯, 屯妮萨古丽·艾买提江, 等. 管花肉苁蓉饮片中主要苯乙醇苷近红外快检技术的建立及应用[J]. 分析试验室, 2023, 42(6): 767-774.
    [19]
    黄子淇, 吴玉章, 刘犇, 等. 近红外技术在含能材料领域的应用研究进展[J]. 兵器装备工程学报, 2022, 43(7): 58-66. doi: 10.11809/bqzbgcxb2022.07.010
    [20]
    赵星, 张嘉楠, 张一鸣, 等. 花生籽仁蔗糖含量近红外光谱快速测定方法研究[J]. 中国油料作物学报, 2025, 47(1): 226-233.
    [21]
    胡美玲, 郅晨阳, 薛晓梦, 等. 单粒花生蔗糖含量近红外预测模型的建立[J]. 作物学报, 2023, 49(9): 2498-2504.
    [22]
    吕建伟, 饶庆琳, 姜敏, 等. 花生籽仁油酸、亚油酸含量近红外模型构建及育种应用[J]. 中国油料作物学报, 2023, 45(2): 399-406.
    [23]
    韩宏伟, 王传堂, 符明联, 等. 11个单粒花生脂肪酸近红外定量分析模型构建[J]. 中国油料作物学报, 2023, 45(2): 407-412.
    [24]
    纪红昌, 邱晓臣, 柳文浩, 等. 花生籽仁含油量近红外模型的构建及其应用[J]. 中国油料作物学报, 2022, 44(5): 1089-1097.
    [25]
    汪志强, 李大鹏, 刘强, 等. 基于温度修正和可见/近红外光谱的油茶籽含水率检测[J]. 食品与机械, 2022, 38(12): 127-132.
    [26]
    中华人民共和国国家卫生和计划生育委员会, 国家食品药品监督管理总局. 食品安全国家标准 食品中脂肪的测定: GB 5009.6—2016[S]. 北京: 中国标准出版社, 2017.
    [27]
    中华人民共和国国家卫生和计划生育委员会, 国家食品药品监督管理总局. 食品安全国家标准 食品中蛋白质的测定: GB 5009.5—2016[S]. 北京: 中国标准出版社, 2017.
    [28]
    中华人民共和国国家卫生和计划生育委员会, 国家食品药品监督管理总局. 食品安全国家标准 食品中果糖、葡萄糖、蔗糖、麦芽糖、乳糖的测定: GB 5009.8—2016[S]. 北京: 中国标准出版社, 2017.
    [29]
    中华人民共和国国家卫生和计划生育委员会, 国家食品药品监督管理总局. 食品安全国家标准 食品中脂肪酸的测定: GB 5009.168—2016[S]. 北京: 中国标准出版社, 2017.
    [30]
    郑咏梅, 张铁强, 张军, 等. 平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J]. 光谱学与光谱分析, 2004, 24(12): 1546-1548. doi: 10.3321/j.issn:1000-0593.2004.12.016
    [31]
    中华人民共和国农业农村部种植业管理司. 高油酸花生: NY/T 3250—2018[S]. 北京: 中国农业出版社, 2018.
    [32]
    李振, 侯名语, 崔顺立, 等. 花生籽仁黄酮含量近红外分析检测方法[J]. 光谱学与光谱分析, 2024, 44(4): 1112-1116. doi: 10.3964/j.issn.1000-0593(2024)04-1112-05
    [33]
    王志伟, 王秀贞, 马浪, 等. 花生籽仁食用感官品质近红外分析模型构建[J]. 花生学报, 2022, 51(3): 77-82.
    [34]
    胡云超, 刘智健, 汪莹, 等. 蜻蜓算法优选小麦粉蛋白质近红外建模校正集[J]. 食品科学, 2024, 45(9): 9-15. doi: 10.7506/spkx1002-6630-20230317-170
    [35]
    杜一平, 张璇, 陈贵平, 等. 近红外光谱分析低含量组分的能力评价[C]//全国第四届近红外光谱学术会议. 2012.
    [36]
    ZHANG X, REN Y L, DU Y P, et al. Assessment of ability to detect low concentration analyte with near-infrared spectroscopy based on pre-concentration technique[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 124: 1-8. doi: 10.1016/j.chemolab.2013.03.003
    [37]
    郑畅, 杨湄, 周琦, 等. 高油酸花生油与普通油酸花生油的脂肪酸、微量成分含量和氧化稳定性[J]. 中国油脂, 2014, 39(11): 40-43.
    [38]
    严寒, 郭平, 骆鹏杰, 等. 近红外光谱结合膜富集技术测定大米中毒死蜱农药残留[J]. 现代食品科技, 2017, 33(4): 289-294.
    [39]
    李先宽, 李赫宇, 李帅, 等. 白藜芦醇研究进展[J]. 中草药, 2016, 47(14): 2568-2578. doi: 10.7501/j.issn.0253-2670.2016.14.030
    [40]
    SCHOOT M, KAPPER C, VAN KESSEL G, et al. Cost-benefit analysis of calibration model maintenance strategies for process monitoring[J]. Analytica Chimica Acta, 2021, 1180: 338890. doi: 10.1016/j.aca.2021.338890.
    [41]
    NAWADE B, BOSAMIA T C, THANKAPPAN R, et al. Insights into the Indian peanut genotypes for ahFAD2 gene polymorphism regulating its oleic and linoleic acid fluxes[J]. Frontiers in Plant Science, 2016, 7: 1271. doi: 10.3389/fpls.2016.01271.
    [42]
    MOORE K M, KNAUFT D A. The inheritance of high oleic acid in peanut[J]. Journal of Heredity, 1989, 80(3): 252-253. doi: 10.1093/oxfordjournals.jhered.a110845
    [43]
    BARKLEY N A, ISLEIB T G, WANG M L, et al. Genotypic effect of ahFAD2 on fatty acid profiles in six segregating peanut (Arachis hypogaea L. ) populations[J]. BMC Genetics, 2013, 14: 62. doi: 10.1186/1471-2156-14-62.
    [44]
    BARKLEY N A, CHENAULT CHAMBERLIN K D, WANG M L, et al. Genotyping and fatty acid composition analysis in segregating peanut (Arachis hypogaea L. ) populations[J]. Peanut Science, 2011, 38(1): 11-19. doi: 10.3146/PS10-17.1
    [45]
    张照华, 王志慧, 淮东欣, 等. 利用回交和标记辅助选择快速培育高油酸花生品种及其评价[J]. 中国农业科学, 2018, 51(9): 1641-1652. doi: 10.3864/j.issn.0578-1752.2018.09.003
    [46]
    李建国, 薛晓梦, 张照华, 等. 单粒花生主要脂肪酸含量近红外预测模型的建立及其应用[J]. 作物学报, 2019, 45(12): 1891-1898.
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