欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2023, Vol. 49 ›› Issue (9): 2498-2504.doi: 10.3724/SP.J.1006.2023.24241

• 耕作栽培·生理生化 • 上一篇    下一篇

单粒花生蔗糖含量近红外预测模型的建立

胡美玲1(), 郅晨阳1, 薛晓梦1, 吴洁1, 王瑾2, 晏立英1, 王欣1, 陈玉宁1, 康彦平1, 王志慧1, 淮东欣1,*(), 姜慧芳1, 雷永1,*(), 廖伯寿1   

  1. 1中国农业科学院油料作物研究所 / 农业农村部遗传育种重点实验室, 湖北武汉 430062
    2河北省农林科学院粮油作物研究所, 河北石家庄 050035
  • 收稿日期:2022-10-28 接受日期:2023-02-21 出版日期:2023-09-12 网络出版日期:2023-03-09
  • 通讯作者: *淮东欣, E-mail: dxhuai@caas.cn; 雷永, E-mail: leiyong@caas.cn
  • 作者简介:胡美玲, E-mail: hml13419876358@163.com
  • 基金资助:
    国家重点研发计划项目(2018YFD1000901);河北省重点研发计划项目(21326316D);湖北省重点研发计划项目(2021BBA077);中国农业科学院科技创新工程项目(CAAS-ASTIP-2013-OCRI)

Establishment of near-infrared reflectance spectroscopy model for predicting sucrose content of single seed in peanut

HU Mei-Ling1(), ZHI Chen-Yang1, XUE Xiao-Meng1, WU Jie1, WANG Jin2, YAN Li-Ying1, WANG Xin1, CHEN Yu-Ning1, KANG Yan-Ping1, WANG Zhi-Hui1, HUAI Dong-Xin1,*(), JIANG Hui-Fang1, LEI Yong1,*(), LIAO Bo-Shou1   

  1. 1Oil Crops Research Institute, Chinese Academy of Agricultural Sciences / Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, Hubei, China
    2Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, Hebei, China
  • Received:2022-10-28 Accepted:2023-02-21 Published:2023-09-12 Published online:2023-03-09
  • Supported by:
    National Key Research and Development Program of China(2018YFD1000901);Key Area Research and Development Program of Hebei Province(21326316D);Key Area Research and Development Program of Hubei Province(2021BBA077);Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2013-OCRI)

RichHTML

11

PDF

382

摘要：

蔗糖含量是影响花生口感和风味的重要因素, 培育高蔗糖甜味品种已成为食用型花生遗传改良的重要目标。因此, 建立单粒花生蔗糖含量的近红外预测模型有助于加快甜花生品种选育进程。本研究选择128份遗传多样性丰富的代表性材料, 采集了近红外光谱, 利用高效液相色谱-折光指数检测器(HPLC-RID)测得蔗糖含量化学值, 并利用偏最小二乘法(PLS)建立了单粒花生蔗糖含量的数学预测模型, 其决定系数(R2)为0.913, 交叉验证根均方差(RMSECV)为0.750。另选用50粒花生种子对预测模型进行外部验证, 预测值和化学值的相关系数达0.92, 表明本研究建立的模型预测值准确可靠。本研究建立的单粒花生蔗糖含量预测模型可以应用于杂交早期世代育种材料蔗糖含量的选择, 也可以应用于高蔗糖材料纯度的筛选和鉴定, 为食用型花生品种选育和产业化应用提供技术支撑。

关键词: 花生, 单粒, 蔗糖含量, 近红外光谱, 校准模型

Abstract:

Sucrose content is an important factor to determine the taste and flavor of peanuts. Breeding sweet peanut varieties with high sucrose content has been a main objective for the edible peanut genetic improvement. Developing a near-infrared reflectance spectroscopy model for predicting the sucrose content in single peanut kernel will accelerate the process of sweet peanut breeding. In this study, 128 representative materials with abundant genetic diversity were selected, the near-infrared spectral data were collected, and the sucrose content of each seed was determined by high performance liquid chromatography (HPLC) with RID detector. Based on spectral data and chemical value of sucrose content, a calibration model for predicting the sucrose content in single seed with a coefficient of determination (R2) of 0.913 and a root mean square error of cross validation (RMSECV) of 0.750, was built up by partial least squares (PLS) method. 50 peanut seeds were analyzed by both NIR and HPLC for external validation and the correlation coefficient between the prediction value and the chemical value reached 0.92, indicating that this model could predict sucrose content with adequate accuracy and reliability. This calibration model for sucrose content in single seed could be applied to the selection of high sucrose lines in the early generations of hybrid progenies and in the purity monitoring of seed and raw material, which will be a supportive technique for the edible peanut variety breeding and industrial application.

Key words: peanut, single seed, sucrose content, Near Infrared Spectroscopy (NIRS), calibration model

图1

本试验中使用的近红外检测仪Unity-Spectrastar XL和单粒扫描适配器"

图2

样品集的色谱分析"

图3

样品集蔗糖含量化学值的分布"

图4

本方法中蔗糖含量模型决定系数"

图5

外部验证使用的单粒花生籽仁的蔗糖含量近红外预测值与化学值的相关性 NIR: 单粒近红外检测值; HPLC: 高效液相色谱法单粒检测值。"

附表1

检验数学模型准确性使用的单粒花生种子蔗糖含量的NIR预测值与化学值"

样品编号
Sample ID		 近红外值
NIR (%)		 液相色谱值
HPLC (%)		 偏差
Deviation		 样品编号
Sample ID		 近红外值
NIR (%)		 液相色谱值
HPLC (%)		 偏差
Deviation
1 3.95 4.69 -0.74 26 5.55 7.24 -1.69
2 2.79 2.39 0.40 27 6.43 7.15 -0.72
3 4.64 3.30 1.34 28 5.61 6.17 -0.56
4 3.02 4.13 -1.11 29 6.60 8.49 -1.89
5 4.69 3.02 1.67 30 5.92 7.95 -2.03
6 4.74 4.79 -0.05 31 7.36 8.70 -1.34
7 3.67 2.61 1.06 32 6.13 6.86 -0.73
8 2.80 1.98 0.82 33 5.86 8.58 -2.72
9 3.97 2.41 1.56 34 6.54 8.58 -2.04
10 3.76 2.24 1.52 35 6.71 8.01 -1.30
11 6.80 9.17 -2.37 36 2.68 2.38 0.30
12 5.58 6.22 -0.64 37 2.45 2.87 -0.42
13 6.00 7.95 -1.95 38 5.83 7.41 -1.58
14 6.56 7.92 -1.36 39 6.63 8.54 -1.91
15 6.21 7.88 -1.67 40 6.26 7.32 -1.06
16 6.48 8.71 -2.23 41 6.67 8.48 -1.81
17 6.77 8.99 -2.22 42 6.34 8.01 -1.67
18 6.71 9.14 -2.43 43 8.16 10.34 -2.18
19 5.65 8.45 -2.80 44 7.38 8.05 -0.67
20 6.53 9.05 -2.52 45 3.49 5.15 -1.66
21 6.53 9.40 -2.87 46 2.69 3.28 -0.59
22 5.61 8.64 -3.03 47 2.04 3.21 -1.17
23 5.41 7.93 -2.52 48 2.59 3.26 -0.67
24 4.56 5.60 -1.04 49 1.87 1.80 0.07
25 5.74 6.62 -0.88 50 2.12 2.39 -0.27
[1] Toomer O T. Nutritional chemistry of the peanut (Arachis hypogaea). Crit Rev Food Sci, 2017, 58: 3042-3053.
doi: 10.1080/10408398.2017.1339015
[2] 廖伯寿. 我国花生生产发展现状与潜力分析. 中国油料作物学报, 2020, 42: 161-166.
Liao B S. A review on progress and prospects of peanut industry in China. Chin J Oil Crop Sci, 2020, 42: 161-166. (in Chinese with English abstract)
[3] Birthal P S, Rao P P, Nigam S N, Bantilan M C S, Bhagavatula S. Groundnut and Soybean Economies in Asia: Facts, Trends and Outlook. India: International Crops Research Institute for the Semi-Arid Tropics, 2010. pp 23-26.
[4] 王传堂, 王秀贞, 吴琪, 王志伟, 唐月异, 杜祖波, 刘峰, 李秋. 鲜食花生感官品质主导分析、优异品系筛选与遗传力估算. 花生学报, 2018, 47(4): 71-74.
Wang C T, Wang X Z, Wu Q, Wang Z W, Tang Y Y, Du Z B, Liu F, Li Q. Sensory quality of fresh green peanuts: dominance analysis, selection of elite lines and heritability estimates. J Peanut Sci, 2018, 47(4): 71-74. (in Chinese with English abstract)
[5] 秦利, 刘华, 杜培, 董文召, 黄冰艳, 韩锁义, 张忠信, 齐飞艳, 张新友. 基于近红外光谱法的花生籽仁中蔗糖含量的测定. 中国油料作物学报, 2016, 38: 666-671.
Qin L, Liu H, Du P, Dong W Z, Huang B Y, Han S Y, Zhang Z X, Qi F Y, Zhang X Y. Determination of sucrose content in peanut seed kernel based on near infrared spectroscopy. Chin J Oil Crop Sci, 2016, 38: 666-671. (in Chinese with English abstract)
[6] 李威涛, 郭建斌, 喻博伦, 徐思亮, 陈海文, 吴贝, 龚廷峰, 黄莉, 罗怀勇, 陈玉宁, 周小静, 刘念, 陈伟刚, 姜慧芳. 基于HPLC-RID的花生籽仁可溶性糖含量检测方法的建立. 作物学报, 2021, 47: 368-375.
doi: 10.3724/SP.J.1006.2021.04110
Li W T, Guo J B, Yu B L, Xu S L, Chen H W, Wu B, Gong T F, Huang L, Luo H Y, Chen Y N, Zhou X J, Liu N, Chen W G, Jiang H F. Establishment of HPLC-RID method for the determination of soluble sugars in peanut seed. Acta Agron Sin, 2021, 47: 368-375. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.04110
[7] 张琨, 周芹. 甜菜中糖类的测定方法及应用. 中国糖料, 2018, 40(6): 73-76.
Zhang K, Zhou Q. Research progress on determination and utilization of carbohydrate in beet. Sugar Crop China, 2018, 40(6): 73-76. (in Chinese with English abstract)
[8] 蒋祎丽, 吴晓宗, 郝莉花, 张丽华, 胡颖喆, 纵伟. 近红外光谱技术快速检测猪肉糜中的掺杂鸭肉. 食品研究与开发, 2015, 36(21): 133-135.
Jiang Y L, Wu X Z, Hao L H, Zhang L H, Hu Y Z, Zong W. Rapidly qualitative discrimination of minced duck in ground pork by near infrared reflectance spectroscopy. Food Res Develop, 2015, 36(21): 133-135. (in Chinese with English abstract)
[9] 刘佳, 李月, 高婷, 王佳雅, 胡晓雨, 孙志文, 叶能胜, 李建. 近红外光谱技术在兽药及兽药残留检测方面的应用进展. 中国兽药杂志, 2021, 55(7): 48-58.
Liu J, Li Y, Gao T, Wang J Y, Hu X Y, Sun Z W, Ye N S, Li J. Advance on application of near infrared spectroscopy in the detection of veterinary drug and veterinary drug residue. Chin J Veter Drug, 2021, 55(7): 48-58. (in Chinese with English abstract)
[10] 刘鹏, 蒋雪松, 沈飞, 吴启芳, 许林云, 周宏平. 近红外光谱技术的花生产毒霉菌侵染快速检测. 光谱学与光谱分析, 2017, 37: 1397-1402.
Liu P, Jiang X S, Shen F, Wu Q F, Xu L Y, Zhou H P. Rapid detection of toxigenic fungal contamination in peanuts with near infrared spectroscopy technology. Spectrosc Spectral Anal, 2017, 37: 1397-1402. (in Chinese with English abstract)
[11] 穆同娜, 庄胜利, 赵玉琪, 吴燕涛, 于晓瑾, 孙婷. 近红外光谱法快速检测婴儿配方奶粉中的脂肪酸含量. 现代食品科技, 2015, 31(4): 277-281.
Mu T N, Zhuang S L, Zhao Y Q, Wu Y T, Yu X J, Sun T. Rapid Determination of total fatty acids, saturated fatty acids, and unsaturated fatty acids in infant formula using near-infrared spectroscopy. Modern Food Sci Technol, 2015, 31(4): 277-281. (in Chinese with English abstract)
[12] 王红珂, 张言, 钱诚, 薛莉, 刘玉冰, 李韪韬. 基于近红外光的腹部出血检测系统的开发. 生物医学工程研究, 2016, 35(3): 178-182.
Wang H K, Zhang Y, Qian C, Xue L, Liu Y B, Li W T. Development of an abdominal bleeding detecting system based on near infrared light. J Biomed Eng Res, 2016, 35(3): 178-182. (in Chinese with English abstract)
[13] 吴阮琦, 董艳虹, 范琦, 陈杨, 王以武, 李娟. NIRS法分析复方磺胺甲噁唑片的均匀性. 药物分析杂志, 2014, 34: 291-296.
Wu R Q, Dong Y H, Fan Q, Chen Y, Wang Y W, Li J. Analysis of uniformities of compound sulfamethoxazole tablets by NIRS. Chin J Pharm Anal, 2014, 34: 291-296. (in Chinese with English abstract)
[14] 吴双, 王杰, 俞雅茹, 涂斌, 郑晓, 何东平. 基于拉曼和近红外光谱特征层融合的食用油MUFA和PUFA含量检测. 中国粮油学报, 2017, 32(11): 158-164.
Wu S, Wang J, Yu Y R, Tu B, Zheng X, He D P. Content detection of MUFA and PUFA integrated based on characteristic fusion of raman and near infrared spectrum. J Chin Cereals Oils Assoc, 2017, 32(11): 158-164. (in Chinese with English abstract)
[15] 肖莹, 彭凤, 杨洪芹, 熊歆诺, 李晖. 近红外光谱法在琥乙红霉素片定量分析中的应用. 化学研究与应用, 2016, 28: 954-959.
Xiao Y, Peng F, Yang H Q, Xiong X N, Li H. Application of near infrared spectroscopy in quantitative analysis of erythromycin ethylsuccinate tablets with different manufactures. Chem Reser Appl, 2016, 28: 954-959. (in Chinese with English abstract)
[16] 许倩, 朱秋劲, 叶春, 王文秀. 近红外光谱分析技术快速检测冰温贮藏牛肉品质. 肉类研究, 2015, 29(3): 23-26.
Xu Q, Zhu Q J, Ye C, Wang W X. Application of near infrared spectroscopy in rapid evaluation of beef quality in controlled freezing point storage. Meat Res, 2015, 29(3): 23-26. (in Chinese with English abstract)
[17] 张森, 张双虹, 赵金力, 李绪国, 吴子毅, 王宝, 赵抒娜. 近红外光谱技术在糖业的应用及进展. 食品安全质量检测学报, 2020, 11: 7196-7202.
Zhang S, Zhang S H, Zhao J L, Li X G, Wu Z Y, Wang B, Zhao S N. Application and progress of near infrared reflectance spectroscopy in sugar industry. J Food Safe Qual, 2020, 11: 7196-7202. (in Chinese with English abstract)
[18] 章海亮, 何勇. 便携式短波近红外光谱仪器检测土壤总氮含量研究. 光谱学与光谱分析, 2016, 36(1): 91-95.
Zhang H L, He Y. Measurement of soil total n based on portable short wave NIR spectroscopy technology. Spectrosc Spectral Anal, 2016, 36(1): 91-95. (in Chinese with English abstract)
[19] 丛媛媛, 刘浩, 衣志刚, 王博, 刘念析, 陈亮. 基于混合样品集的大豆种子蔗糖含量近红外光谱定标模型建立. 大豆科学, 2020, 39: 940-946.
Cong Y Y, Liu H, Yi Z G, Wang B, Liu N X, Chen L. Establishment of NIRS calibration models for sucrose content in soybean based on the combined sample Set. Soybean Sci, 2020, 39: 940-946. (in Chinese with English abstract)
[20] 韩洁楠, 王美娟, 赵训超, 鲁鑫, 周志强, 李明顺, 张德贵, 郝转芳, 翁建峰, 雍洪军, 李新海. 玉米粉淀粉含量近红外模型建立与优化. 玉米科学, 2020, 28(6): 81-87.
Han J N, Wang M J, Zhao X C, Lu X, Zhou Z Q, Li M S, Zhang G D, Hao Z F, Wong J F, Yong H J, Li X H. Establishment and optimization of a near-infrared model of maize starch content. J Maize Sci, 2020, 28(6): 81-87. (in Chinese with English abstract)
[21] 雷永, 王志慧, 淮东欣, 高华援, 晏立英, 李建国, 李威涛, 陈玉宁, 康彦平, 刘海龙, 王欣, 薛晓梦, 姜慧芳, 廖伯寿. 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. 作物学报, 2021, 47: 332-341.
doi: 10.3724/SP.J.1006.2021.04106
Lei Y, Wang Z H, Huai D X, Gao H Y, Yan L Y, Li J G, Li W T, Chen Y N, Kang Y P, Liu H L, Wang X, Xue X M, Jiang H F, Liao B S. Development and application of a near infrared spectroscopy model for predicting high sucrose content of peanut seed. Acta Agron Sin, 2021, 47: 332-341 (in Chinese with English abstract).
doi: 10.3724/SP.J.1006.2021.04106
[22] 卞能飞, 童飞, 巩佳莉, 孙东雷, 沈一, 王幸, 邢兴华, 王晓军. 花生籽仁中蔗糖含量近红外预测群体模型的建立及在突变体筛选中的应用. 核农学报, 2022, 36: 251-258.
doi: 10.11869/j.issn.100-8551.2022.02.0251
Bian N F, Tong F, Gong J L, Sun D L, Shen Y, Wang X, Xing X H, Wang X J. Establishment of near infrared spectroscopy model for predicting sucrose content of peanut seed and application in mutants selection. J Nucl Agric Sci, 2022, 36: 251-258. (in Chinese with English abstract)
doi: 10.11869/j.issn.100-8551.2022.02.0251
[23] 唐月异, 王秀贞, 刘婷, 吴琪, 孙全喜, 王志伟, 张欣, 王传堂, 邵俊飞. 花生自然风干种子蔗糖含量近红外定量分析模型构建. 山东农业科学, 2018, 50(6): 159-162.
Tang Y Y, Wang X Z, Liu T, Wu Q, Sun Q X, Wang Z W, Zhang X, Wang C T, Shao J F. A near infrared spectroscopy model for predicting sucrose content of sun dried peanut seeds. Shandong Agric Sci, 2018, 50(6): 159-162 (in Chinese with English abstract).
[24] 张建成, 王传堂, 王秀贞, 唐月异, 张树伟, 李贵杰. 花生自然风干种子油酸、亚油酸和棕榈酸含量的近红外分析模型构建. 中国农学通报, 2011, 27(3): 90-93.
Zhang J C, Wang C T, Wang X Z, Tang Y Y, Zhang S W, Li G J. NIRS calibration models predictive of oleic, linoleic and palmitic acid content in sun-dried bulk peanut seed samples. Chin Agric Sci Bull, 2011, 27(3): 90-93. (in Chinese with English abstract)
doi: 10.11924/j.issn.1000-6850.2010-2237
[25] 李建国, 薛晓梦, 张照华, 王志慧, 晏立英, 陈玉宁, 万丽云, 康彦平, 淮东欣, 姜慧芳, 雷永, 廖伯寿. 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. 作物学报, 2019, 45: 1891-1898.
doi: 10.3724/SP.J.1006.2019.94016
Li J G, Xue X M, Zhang Z H, Wang Z H, Yan L Y, Chen Y N, Wan L Y, Kang Y P, Huai D X, Jiang H F, Liao B S. Establishment and applicant of near-infrared reflectance spectroscopy models for predicting main fatty acid contents of single seed in peanut. Acta Agron Sin, 2019, 45: 1891-1898. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2019.94016
[1] 王菲菲, 张胜忠, 胡晓辉, 崔凤高, 钟文, 赵立波, 张天雨, 郭进涛, 于豪谅, 苗华荣, 陈静. 比较转录组分析花生种子休眠调控网络[J]. 作物学报, 2023, 49(9): 2446-2461.
[2] 徐扬, 张岱, 康涛, 温赛群, 张冠初, 丁红, 郭庆, 秦斐斐, 戴良香, 张智猛. 盐胁迫对花生幼苗离子动态及耐盐基因表达的影响[J]. 作物学报, 2023, 49(9): 2373-2384.
[3] 黄莉, 陈伟刚, 李威涛, 喻博伦, 郭建斌, 周小静, 罗怀勇, 刘念, 雷永, 廖伯寿, 姜慧芳. 花生根部结瘤性状QTL定位[J]. 作物学报, 2023, 49(8): 2097-2104.
[4] 李星, 杨会, 骆璐, 李华东, 张昆, 张秀荣, 李玉颖, 于海洋, 王天宇, 刘佳琪, 王瑶, 刘风珍, 万勇善. 栽培种花生单仁重QTL定位分析[J]. 作物学报, 2023, 49(8): 2160-2170.
[5] 陶顺玉, 吴贝, 刘念, 罗怀勇, 黄莉, 周小静, 陈伟刚, 郭建斌, 喻博伦, 雷永, 廖伯寿, 姜慧芳. 花生InDel标记开发及其在含油量QTL定位中的应用[J]. 作物学报, 2023, 49(5): 1222-1230.
[6] 孙全喜, 苑翠玲, 牟艺菲, 闫彩霞, 赵小波, 王娟, 王奇, 孙慧, 李春娟, 单世华. 花生SWEET基因全基因组鉴定及表达分析[J]. 作物学报, 2023, 49(4): 938-954.
[7] 纪红昌, 胡畅丽, 邱晓臣, 吴兰荣, 李晶晶, 李鑫, 李晓婷, 刘雨函, 唐艳艳, 张晓军, 王晶珊, 乔利仙. 花生籽仁品质性状高通量表型分析模型的构建[J]. 作物学报, 2023, 49(3): 869-876.
[8] 刘俊华, 吴正锋, 党彦学, 于天一, 郑永美, 万书波, 王才斌, 李林. 密度对不同株型花生单粒精播群体质量及产量的影响[J]. 作物学报, 2023, 49(2): 459-471.
[9] 郭建斌, 成良强, 李威涛, 刘念, 罗怀勇, 丁膺宾, 喻博伦, 陈伟刚, 黄莉, 周小静, 雷永, 廖伯寿, 姜慧芳. 花生蔗糖含量与蛋白质和含油量的相关性分析及蔗糖含量QTL定位[J]. 作物学报, 2023, 49(10): 2698-2704.
[10] 丁红, 张智猛, 徐扬, 张冠初, 郭庆, 秦斐斐, 戴良香. 氮素缓解花生干旱胁迫的生理和转录调控机制[J]. 作物学报, 2023, 49(1): 225-238.
[11] 张胜忠, 胡晓辉, 慈敦伟, 杨伟强, 王菲菲, 邱俊兰, 张天雨, 钟文, 于豪諒, 孙冬平, 邵战功, 苗华荣, 陈静. 基于三维模型重构的花生网纹厚度性状QTL分析[J]. 作物学报, 2022, 48(8): 1894-1904.
[12] 白冬梅, 薛云云, 黄莉, 淮东欣, 田跃霞, 王鹏冬, 张鑫, 张蕙琪, 李娜, 姜慧芳, 廖伯寿. 不同花生品种芽期耐寒性鉴定及评价指标筛选[J]. 作物学报, 2022, 48(8): 2066-2079.
[13] 徐扬, 张智猛, 丁红, 秦斐斐, 张冠初, 戴良香. 钙肥对酸性红壤花生种子萌发及种子际微生物菌群结构的调控[J]. 作物学报, 2022, 48(8): 2088-2099.
[14] 杨欢, 周颖, 陈平, 杜青, 郑本川, 蒲甜, 温晶, 杨文钰, 雍太文. 玉米-豆科作物带状间套作对养分吸收利用及产量优势的影响[J]. 作物学报, 2022, 48(6): 1476-1487.
[15] 李海芬, 魏浩, 温世杰, 鲁清, 刘浩, 李少雄, 洪彦彬, 陈小平, 梁炫强. 花生电压依赖性阴离子通道基因(AhVDAC)的克隆及在果针向地性反应中表达分析[J]. 作物学报, 2022, 48(6): 1558-1565.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 王丽燕;赵可夫. 玉米幼苗对盐胁迫的生理响应[J]. 作物学报, 2005, 31(02): 264 -268 .
[2] 倪大虎;易成新;李莉;汪秀峰;张毅;赵开军;王春连;章琦;王文相;杨剑波. 分子标记辅助培育水稻抗白叶枯病和稻瘟病三基因聚合系[J]. 作物学报, 2008, 34(01): 100 -105 .
[3] 戴小军;梁满中;陈良碧. 栽培稻种内核糖体基因的ITS序列比较研究[J]. 作物学报, 2007, 33(11): 1874 -1878 .
[4] 汪保华;武耀廷;黄乃泰;郭旺珍;朱协飞;张天真. 陆地棉重组自交系产量及产量构成因子性状的上位性QTL分析[J]. 作物学报, 2007, 33(11): 1755 -1762 .
[5] 王春梅;冯祎高;庄丽芳;曹亚萍;亓增军;别同德;曹爱忠;陈佩度. 普通小麦近缘物种黑麦1R、簇毛麦1V及鹅观草1Rk#1染色体特异分子标记的筛选[J]. 作物学报, 2007, 33(11): 1741 -1747 .
[6] 赵庆华;黄剑华;颜昌敬. 油菜花粉发芽的研究[J]. 作物学报, 1986, (01): 15 -20 .
[7] 周录英;李向东;王丽丽;汤笑;林英杰. 钙肥不同用量对花生生理特性及产量和品质的影响[J]. 作物学报, 2008, 34(05): 879 -885 .
[8] 王立新;李云伏;常利芳;黄 岚;李宏博;葛玲玲;刘丽华;姚 骥;赵昌平;姚 骥;赵昌平. 建立小麦品种DNA指纹的方法研究[J]. 作物学报, 2007, 33(10): 1738 -1740 .
[9] 郑天清;徐建龙;傅彬英;高用明;Satish VERUKA;Renee LAFITTE;翟虎渠;万建民;朱苓华;黎志康. 回交高代选择导入系的纹枯病抗性与抗旱性的遗传重叠研究[J]. 作物学报, 2007, 33(08): 1380 -1384 .
[10] 杨燕;赵献林;张勇;陈新民;何中虎;于卓;夏兰琴. 四个小麦抗穗发芽分子抗性标记有效性的验证与评价[J]. 作物学报, 2008, 34(01): 17 -24 .
京ICP备15008789号-2