花生烘烤食用品质评价及指标筛选

doi:10.3864/j.issn.0578-1752.2022.04.002

摘要/Abstract

摘要：

【目的】探索花生烘烤食用品质评价方法,筛选评价指标,建立预测模型,为花生食用品质育种提供依据。【方法】以51个不同品质类型花生品种（系）的籽仁为试验材料,测定烘烤籽仁的食味指标、外观指标、质构指标和营养指标等食用品质相关指标27项,运用相关分析、主成分分析对花生烘烤食用品质进行综合评价,通过聚类分析对51个花生品种（系）的烘烤食用品质进行分类,通过回归分析建立花生烘烤食用品质预测模型和鉴定指标的筛选。【结果】51个花生品种（系）27项指标的变异幅度不同,变异系数为5.86%—39.65%。各单项指标均存在与之显著或极显著相关的指标,175组相关系数达显著水平,140组达极显著水平。主成分分析将27个单项指标转换为5个相互独立的综合指标,其贡献率分别为35.70%、20.63%、10.07%、8.19%和6.38%,代表了全部数据80.97%的信息量。综合评价分析表明,51个花生品种（系）烘烤食用品质综合值F平均为0.76,徐花甜29的F最高（F=1.51）,烘烤食用品质最好,徐花15号的F最低（F=0.03）,烘烤食用品质最差。相关性分析结果表明,21项指标与F显著相关。对花生烘烤食用品质综合值F进行聚类分析,将51个品种（系）划分为3类,第一类属于烘烤食用品质较好,包含徐花甜29、冀花甜1号、临花16号和徐花甜30共4个品种;第二类属于烘烤食用品质一般,包含33个品种（系）;第三类属于烘烤食用品质较差,包含14个品种（系）。采用逐步回归分析方法,建立花生烘烤食用品质预测模型为：Y=0.979+0.021X₇+0.081X₂₁+0.009X₂₀-0.034X₁₉-0.074X₂₇（R²=0.953）,筛选出5个烘烤食用品质鉴定指标,分别为百仁重、蔗糖含量、蛋白质含量、脂肪含量和山嵛酸含量。对不同类别花生特征分析可知,4个烘烤食用品质较好品种的百仁重中至高,蔗糖含量高,蛋白质含量中,脂肪含量低,山嵛酸含量低至中,根据预测模型,此类品种仍需进一步改良以提高蛋白质含量和降低山嵛酸含量。【结论】百仁重、蔗糖含量、蛋白质含量、脂肪含量和山嵛酸含量可作为鉴定花生烘烤品质的指标,可以确定优质烘烤花生品种应具备籽仁中大粒、高油酸、高蔗糖、高蛋白、低脂肪和低山嵛酸等性状。

关键词: 花生, 烘烤, 食用品质, 综合评价, 指标筛选

Abstract:

【Objective】The objective of this study was to explore the evaluation methods of edible quality of roasted peanuts, screen identification indexes, establish prediction model, and provide basis for peanut edible quality breeding. 【Method】The kernels of 51 peanut varieties (lines) with different quality types were used as experimental materials. A total of 27 edible quality indexes related to tastes, appearances, textures and nutrition of roasted kernels were measured. Correlation analysis and principal component analysis were used to comprehensively evaluate the edible quality of roasted peanut kernels. Cluster analysis was used to classify edible quality of 51 peanut varieties (lines). Regression analysis were used to establish predictive model and index screening. 【Result】27 indexes had different ranges of variation in 51 peanut varieties (lines), with coefficients of variation ranging from 5.86% to 39.65%. There were significant or extremely significant related indexes for each individual index. The correlation coefficients of 175 groups reached significant level, and the 140 groups reached extremely significant level. The 27 individual indexes were converted into 5 independent comprehensive indexes through principal component analysis, and their contribution rates respectively were 35.70%, 20.63%, 10.07%, 8.19% and 6.38%, representing the information of 80.97% of all data. The comprehensive evaluation analysis showed that the average F value of the roasted edible quality of 51 peanut varieties (lines) was 0.76. Xuhuatian 29 had the highest F value (F=1.51) and the best roasting edible quality. Xuhua 15 had the lowest F value (F=0.03) and the worst roasting edible quality. The correlation analysis showed that 21 indexes were significantly correlated with F value. Cluster analysis was performed on the comprehensive value F of peanut roasting edible quality, and 51 varieties (lines) were divided into 3 categories. The first category was of good edible quality, including 4 varieties of Xuhuatian 29, Jihuatian 1, Linhua 16 and Xuhuatian 30. The second category was of general edible quality and contained 33 varieties (lines). The third category was of poor edible quality and contained 14 varieties (lines). Using stepwise regression analysis method, the prediction model of roasting edible quality was established as: Y=0.979+ 0.021X₇+0.081X₂₁+0.009X₂₀-0.034X₁₉-0.074X₂₇ (R²=0.953). Then, 5 identification indexes were screened, which were the hundred kernel weight, sucrose content, protein content, fat content and behenic acid content. The analysis of characteristics showed that the four varieties with good roasting edible quality had medium to high hundred kernel weight, high sucrose content, medium protein content, low fat content, and low to medium behenic acid content. According to the prediction model, this category varieties still need to be improved to increase protein content and reduce behenic acid content. 【Conclusion】Hundred kernel weight, sucrose content, protein content, fat content and behenic acid content could be used to identify the edible quality of roasted peanuts. It is determined that high-quality roasted peanut varieties should have the characteristics of medium to large kernels, high oleic acid content, high sucrose content, high protein content, low fat content and low behenic acid content.

Key words: peanut, roast, edible quality, comprehensive evaluation, index screening

卞能飞, 孙东雷, 巩佳莉, 王幸, 邢兴华, 金夏红, 王晓军. 花生烘烤食用品质评价及指标筛选[J]. 中国农业科学, 2022, 55(4): 641-652.

BIAN NengFei, SUN DongLei, GONG JiaLi, WANG Xing, XING XingHua, JIN XiaHong, WANG XiaoJun. Evaluation of Edible Quality of Roasted Peanuts and Indexes Screening[J]. Scientia Agricultura Sinica, 2022, 55(4): 641-652.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2022.04.002

https://www.chinaagrisci.com/CN/Y2022/V55/I4/641

图/表 8

表1

表2

27项指标的相关系数"

指标 Index	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂	X₁₃	X₁₄	X₁₅	X₁₆	X₁₇	X₁₈	X₁₉	X₂₀	X₂₁	X₂₂	X₂₃	X₂₄	X₂₅	X₂₆
X₂	-0.14
X₃	0.62**	-0.45**
X₄	0.59**	-0.26	0.66**
X₅	-0.33*	0.17	-0.46**	-0.51**
X₆	-0.23	-0.07	-0.31*	-0.47**	0.67**
X₇	0.32*	0.03	0.15	0.09	-0.03	0.24
X₈	0.27	0.03	0.16	0.07	-0.01	0.22	0.96**
X₉	0.28*	-0.04	0.21	0.07	-0.04	0.19	0.90**	0.98**
X₁₀	0.25	-0.08	0.22	0.06	-0.03	0.18	0.80**	0.91**	0.98**
X₁₁	0.22	0.15	0.01	0.06	0.01	0.19	0.85**	0.76**	0.61**	0.44**
X₁₂	0.15	-0.19	0.27	0.05	-0.07	0.05	0.28*	0.46**	0.62**	0.77**	-0.22
X₁₃	0.34*	-0.14	0.43**	0.37**	-0.31*	-0.17	0.25	0.12	0.06	-0.03	0.36**	-0.25
X₁₄	0.41**	-0.42**	0.60**	0.38**	-0.20	0.06	0.59**	0.53**	0.55**	0.53**	0.33*	0.37**	0.55**
X₁₅	0.40**	-0.55**	0.65**	0.38**	-0.17	0.10	0.51**	0.48**	0.52**	0.52**	0.23	0.42**	0.43**	0.95**
X₁₆	0.30*	-0.10	0.35*	0.22	-0.03	-0.06	0.08	0.00	-0.04	-0.09	0.18	-0.24	0.56**	0.36**	0.31*
X₁₇	0.42**	-0.55**	0.70**	0.35*	-0.28*	0.01	0.44**	0.45**	0.52**	0.54**	0.17	0.46**	0.37**	0.82**	0.85**	0.40**
X₁₈	0.35*	-0.66**	0.72**	0.34*	-0.30*	-0.01	0.34*	0.36**	0.44**	0.47**	0.07	0.47**	0.31*	0.79**	0.85**	0.24	0.93**
X₁₉	-0.46**	0.63**	-0.73**	-0.43**	0.35*	0.08	-0.01	0.03	-0.03	-0.06	0.13	-0.18	-0.41**	-.64**	-0.73**	-0.25	-0.710**	-0.75**
X₂₀	0.06	-0.14	-0.10	0.02	-0.09	0.11	0.20	0.18	0.19	0.18	0.16	0.10	0.15	0.25	0.20	-0.09	0.14	0.16	-0.26
X₂₁	0.50**	-0.53**	0.87**	0.54**	-0.37**	-0.18	0.02	0.05	0.13	0.19	-0.18	0.36**	0.35*	0.59**	0.68**	0.25	0.68**	0.73**	-0.81**	-0.11
X₂₂	-0.41**	0.39**	-0.32*	-0.34*	0.50**	0.21	0.07	0.13	0.07	0.05	0.18	-0.11	0.02	-0.18	-0.23	0.09	-0.26	-0.35*	0.49**	-0.26	-0.34*
X₂₃	0.42**	-0.41**	0.35*	0.37**	-0.49**	-0.21	-0.07	-0.11	-0.05	-0.01	-0.20	0.17	-0.01	0.20	0.26	-0.09	0.29*	0.38**	-0.50**	0.30*	0.37**	-0.99**
X₂₄	0.37**	-0.30*	0.24	0.30*	-0.46**	-0.20	-0.07	-0.15	-0.10	-0.08	-0.17	0.07	-0.06	0.12	0.16	-0.12	0.15	0.24	-0.39**	0.19	0.25	-0.97**	0.95**
X₂₅	0.51**	-0.36**	0.59**	0.36**	-0.45**	-0.13	0.18	0.11	0.12	0.10	0.11	0.06	0.29*	0.47**	0.49**	0.05	0.52**	0.55**	-0.75**	0.27	0.59**	-0.60**	0.57**	0.52**
X₂₆	0.51**	-0.32*	0.53**	0.41**	-0.45**	-0.13	0.29*	0.27	0.29*	0.29*	0.18	0.21	0.24	0.46**	0.46**	-0.03	0.52**	0.53**	-0.63**	0.46**	0.51**	-0.55**	0.57**	0.44**	0.91**
X₂₇	-0.17	0.044	-0.06	-0.13	0.06	-0.15	-0.77**	-0.83**	-0.82**	-0.79**	-0.60**	-0.44**	-0.16	-0.38**	-0.32*	0.00	-0.32*	-0.25	-0.14	-0.23	-0.02	-0.10	0.07	0.12	0.07	-0.18

表2

表3

表4

表5

图1

图2

表6

参考文献 33

[1]	张照华, 王志慧, 淮东欣, 谭家壮, 陈剑洪, 晏立英, 王晓军, 万丽云, 陈傲, 康彦平, 姜慧芳, 雷永, 廖伯寿. 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. 中国农业科学, 2018, 51(9):1641-1652.
	ZHANG Z H, WANG Z H, HUAI D X, TAN J Z, CHEN J H, YAN L Y, WANG X J, WAN L Y, CHEN A, KANG Y P, JIANG H F, LEI Y, LIAO B S. Fast development of high oleate peanut cultivars by using marker-assisted backcrossing and their evaluation. Scientia Agricultura Sinica, 2018, 51(9):1641-1652. (in Chinese)
[2]	WANG Q. Peanut Processing Characteristics and Quality Evaluation. Singapore: Springer Nature, 2018: 42-63.
[3]	林茂, 赵景芳, 郑秀艳, 孟繁博, 黄道梅, 李国林, 陈曦, 宋光艳, 蒋力. 不同种皮颜色花生生品的营养、感官和品质的分析. 分子植物育种, 2019, 17(5):1647-1657.
	LIN M, ZHAO J F, ZHENG X Y, MENG F B, HUANG D M, LI G L, CHEN X, SONG G Y, JIANG L. Analysis of nutrition, sense and quality of peanut seed with different testa color. Molecular Plant Breeding, 2019, 17(5):1647-1657. (in Chinese)
[4]	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(2):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 Agronomica Sinica, 2021, 47(2):332-341. (in Chinese) doi: 10.3724/SP.J.1006.2021.04106
[5]	WANG C T. Evaluation of groundnut genotypes from China for quality traits. Journal of SAT Agricultural Research, 2011, 12(9):1-5.
[6]	孙东雷, 卞能飞, 陈志德, 邢兴华, 徐泽俊, 齐玉军, 王幸, 王晓军, 王伟. 花生种质资源表型性状的综合评价及指标筛选. 植物遗传资源学报, 2018, 19(5):865-874.
	SUN D L, BIAN N F, CHEN Z D, XING X H, XU Z J, QI Y J, WANG X, WANG X J, WANG W. Comprehensive evaluation and index screening of phenotypic traits in peanut germplasm resources. Journal of Plant Genetic Resources, 2018, 19(5):865-874. (in Chinese)
[7]	鲁清, 梁炫强, 陈小平, 李少雄, 刘浩, 周桂元, 刘海燕, 李海芬, 洪彦彬. 花生落荚、裂荚和裂仁特性评鉴及优异种质筛选. 植物遗传资源学报, 2020, 21(5):1102-1111.
	LU Q, LIANG X Q, CHEN X P, LI S X, LIU H, ZHOU G Y, LIU H Y, LI H F, HONG Y B. Evaluation on traits of pod abscission, dehiscence and kernel cracking of peanut and identification of elite germplasm. Journal of Plant Genetic Resources, 2020, 21(5):1102-1111. (in Chinese)
[8]	胡廷会, 成良强, 王军, 吕建伟, 饶庆琳. 不同基因型花生耐荫性评价及其鉴定指标的筛选. 中国农业科学, 2020, 53(6):1140-1153.
	HU T H, CHENG L Q, WANG J, LÜ J W, RAO Q L. Evaluation of shade tolerance of peanut with different genotypes and screening of identification indexes. Scientia Agricultura Sinica, 2020, 53(6):1140-1153. (in Chinese)
[9]	张鹤, 蒋春姬, 殷冬梅, 董佳乐, 任婧瑶, 赵新华, 钟超, 王晓光, 于海秋. 花生耐冷综合评价体系构建及耐冷种质筛选. 作物学报, 2021, 47(9):1753-1767. doi: 10.3724/SP.J.1006.2021.04182
	ZHANG H, JIANG C J, YIN D M, DONG J L, REN J Y, ZHAO X H, ZHONG C, WANG X G, YU H Q. Establishment of comprehensive evaluation system for cold tolerance and screening of cold-tolerance germplasm in peanut. Acta Agronomica Sinica, 2021, 47(9):1753-1767. (in Chinese) doi: 10.3724/SP.J.1006.2021.04182
[10]	孙东雷, 卞能飞, 王幸, 邢兴华, 沈一, 徐泽俊, 齐玉军, 王晓军. 高油酸花生萌发期耐冷性综合评价及种质筛选. 核农学报, 2021, 35(6):1263-1272.
	SUN D L, BIAN N F, WANG X, XING X H, SHEN Y, XU Z J, QI Y J, WANG X J. Comprehensive evaluation of cold tolerance and germplasm screening of high oleic acid peanut at germination stage. Journal of Nuclear Agricultural Sciences, 2021, 35(6):1263-1272. (in Chinese)
[11]	巩阿娜, 刘红芝, 刘丽, 石爱民, 王强. 原料特性与花生酱品质间关系模型的建立. 食品科学技术学报, 2016, 34(2):24-30.
	GONG A N, LIU H Z, LIU L, SHI A M, WANG Q. Establishment of relationship model between peanut material characteristics and peanut butter quality. Journal of Food Science and Technology, 2016, 34(2):24-30. (in Chinese)
[12]	巩阿娜, 刘红芝, 刘丽, 石爱民, 林伟静, 王强. 原料特性对花生酱品质的影响. 中国食品学报, 2016, 16(11):253-262.
	GONG A N, LIU H Z, LIU L, SHI A M, LIN W J, WANG Q. Influence of peanut material characteristics on peanut butter quality. Journal of Chinese Institute of Food Science and Technology, 2016, 16(11):253-262. (in Chinese)
[13]	YU H, LIU H, ERASMUS S W, ZHAO S, WANG Q, RUTH S M. An explorative study on the relationships between the quality traits of peanut varieties and their peanut butters. LWT-Food Science and Technology, 2021, 151:112068.
[14]	王丽, 王强, 刘红芝, 刘丽, 杜寅, 张建书. 不同品种花生分离蛋白凝胶性评价方法的研究. 中国油脂, 2012, 37(7):20-23.
	WANG L, WANG Q, LIU H Z, LIU L, DU Y, ZHANG J S. Evaluation methods of gel property of peanut protein isolate with different varieties. China Oils and Fats, 2012, 37(7):20-23. (in Chinese)
[15]	王丽, 刘红芝, 刘丽, 石爱民, 胡晖, 杨颖, 王强. 油用花生品质评价模型的建立及其加工适宜性研究. 食品科学技术学报, 2016, 34(1):21-27.
	WANG L, LIU H Z, LIU L, SHI A M, HU H, YANG Y, WANG Q, Research on evaluation model and processing suitability of oil-used peanut. Journal of Food Science and Technology, 2016, 34(1):21-27. (in Chinese)
[16]	PATTEE H E, ISLEIB T G, GIESBRECHT F G. Variation in intensity of sweet and bitter sensory attributes across peanut genotypes. Peanut Science, 1998, 25:63-69. doi: 10.3146/i0095-3679-25-2-2
[17]	PATTEE H E, ISLEIB T G, GIESBRECHT F G, MCFEETERS R F. Investigations into genotypic variations of peanut carbohydrates. Journal of Agricultural and Food Chemistry, 2000, 48:750-756. doi: 10.1021/jf9910739
[18]	PATTEE H E, ISLEIB T G, GIESBRECHT F G, MCFEETERS R F. Relationships of sweet, bitter, and roasted peanut sensory attributes with carbohydrate components in peanuts. Journal of Agricultural and Food Chemistry, 2000, 48:757-763. doi: 10.1021/jf9910741
[19]	王传堂, 张建成, 唐月异, 吴琪, 王志伟, 王秀贞, 穆树旗, 李平涛, 李滨. 花生生仁生化成分、生仁和烤仁感官品质的典型相关分析. 山东农业科学, 2020, 52(8):12-16.
	WANG C T, ZHANG J C, TANG Y Y, WU Q, WANG Z W, WANG X Z, MU S Q, LI P T, LI B. Inter-relationships between peanut biochemical components of raw kernels, sensory quality of raw and roasted kernels revealed by canonical correlation analysis. Shandong Agricultural Sciences, 2020, 52(8):12-16. (in Chinese)
[20]	王志伟, 王秀贞, 唐月异, 吴琪, 孙全喜, 杜龙, 刘婷, 张欣, 王传堂. 31个花生品种(系)的生、熟花生感官品质评价研究. 山东农业科学, 2018(6):52-56.
	WANG Z W, WANG X Z, TANG Y Y, WU Q, SUN Q X, DU L, LIU T, ZHANG X, WANG C T. Sensory evaluation for raw and roasted peanuts of 31 genotypes. Shandong Agricultural Sciences, 2018(6):52-56. (in Chinese)
[21]	王秀贞, 吴琪, 成波, 王志伟, 唐月异, 韩守萍, 王传堂. 基因型和成熟度对鲜食花生感官品质的影响. 花生学报, 2019, 48(3):51-54.
	WANG X Z, WU Q, CHENG B, WANG Z W, TANG Y Y, HAN S P, WANG C T. Effect of genotype and maturity on sensory quality of fresh green peanuts. Journal of Peanut Science, 2019, 48(3):51-54. (in Chinese)
[22]	房元瑾, 孙子淇, 苗利娟, 齐飞艳, 黄冰艳, 郑峥, 董文召, 汤丰收, 张新友. 花生籽仁外观和营养品质特征及食用花生育种利用分析. 植物遗传资源学报, 2018, 19(5):875-886.
	FANG Y J, SUN Z Q, MIAO L J, QI F Y, HUANG B Y, ZHENG Z, DONG W Z, TANG F S, ZHANG X Y. Characterization of kernel appearance and nutritional quality in peanut accessions and its application for food-use peanut breeding. Journal of Plant Genetic Resources, 2018, 19(5):875-886. (in Chinese)
[23]	BAGHERI H, KASHANINEJAD M, ZIAIIFAR A M, AALAMI M. Textural, color and sensory attributes of peanut kernels as affected by infrared roasting method. Information Processing in Agriculture, 2019, 6(2):255-264. doi: 10.1016/j.inpa.2018.11.001
[24]	汤鹏宇, 孟繁博, 黄道梅, 郑秀艳, 林茂. 质构参数与花生物性测定的相关性. 现代食品科技, 2021, 37(7):294-301.
	TANG P Y, MENG F B, HUANG D M, ZHENG X Y, LIN M. Correlation between texture-analyzer parameters and physical properties measurement of peanut. Modern Food Science and Technology, 2021, 37(7):294-301. (in Chinese)
[25]	李威涛, 郭建斌, 喻博伦, 徐思亮, 陈海文, 吴贝, 龚廷锋, 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(2):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 Agronomica Sinica, 2021, 47(2):368-375. (in Chinese) doi: 10.3724/SP.J.1006.2021.04110
[26]	COLEMAN W M, WHITE J L, PERFETTI T A. Characteristics of heat-treated aqueous extracts of peanuts and cashews. Journal of Agricultural and Food Chemistry, 1994, 42(1):190-194. doi: 10.1021/jf00037a034
[27]	MISRA J B. A mathematical approach to comprehensive evaluation of quality in groundnut. Journal of Food Composition and Analysis, 2004, 17(1):69-79. doi: 10.1016/S0889-1575(03)00102-9
[28]	王强, 石爱民, 盛晓静, 刘红芝, 刘丽, 胡晖. 一种适宜休闲花生加工的花生品质评价方法及装置: 中国, 107228809. 2017-10-03.
	WANG Q, SHI A M, SHENG X J, LIU H Z, LIU L, HU H. Peanut quality evaluation method and device suitable for leisure peanut processing: China, 107228809. 2017-10-03. (in Chinese)
[29]	宋丽君, 聂晓玉, 何磊磊, 蒯婕, 杨华, 郭安国, 黄俊生, 傅廷栋, 汪波, 周广生. 饲用大豆品种耐荫性鉴定指标筛选及综合评价. 作物学报, 2021, 47(9):1741-1752. doi: 10.3724/SP.J.1006.2021.04149
	SONG L J, NIE X Y, HE L L, KUAI J, YANG H, GUO A G, HUANG J S, FU T D, WANG B, ZHOU G S. Screening and comprehensive evaluation of shade tolerance of forage soybean varieties. Acta Agronomica Sinica, 2021, 47(9):1741-1752. (in Chinese) doi: 10.3724/SP.J.1006.2021.04149
[30]	刘秋员, 周磊, 田晋钰, 程爽, 陶钰, 邢志鹏, 刘国栋, 魏海燕, 张洪程. 长江中下游地区常规中熟粳稻氮效率综合评价及高产氮高效品种筛选. 中国农业科学, 2021, 54(7):1397-1409.
	LIU Q Y, ZHOU L, TIAN J Y, CHENG S, TAO Y, XING Z P, LIU G D, WEI H Y, ZHANG H C. Comprehensive evaluation of nitrogen efficiency and screening of varieties with high grain yield and high nitrogen efficiency of inbred middle-ripe japonica rice in the middle and lower reaches of Yangtze river. Scientia Agricultura Sinica, 2021, 54(7):1397-1409. (in Chinese)
[31]	杨涛, 黄雅婕, 李生梅, 任丹, 崔进鑫, 庞博, 于爽, 高文伟. 海岛棉种质资源表型性状的遗传多样性分析及综合评价. 中国农业科学, 2021, 54(12):2499-2509.
	YANG T, HUANG Y J, LI S M, REN D, CUI J X, PANG B, YU S, GAO W W. Genetic diversity and comprehensive evaluation of phenotypic traits in sea-island cotton germplasm resources. Scientia Agricultura Sinica, 2021, 54(12):2499-2509. (in Chinese)
[32]	孙珍珠, 李秋月, 王小柯, 赵婉彤, 薛杨, 冯锦英, 刘小丰, 刘梦雨, 江东. 宽皮柑橘种质资源表型多样性分析及综合评价. 中国农业科学, 2017, 50(22):4362-4372.
	SUN Z Z, LI Q Y, WANG X K, ZHAO W T, XUE Y, FENG J Y, LIU X F, LIU M Y, JIANG D. Comprehensive evaluation and phenotypic diversity analysis of germplasm resources in mandarin. Scientia Agricultura Sinica, 2017, 50(22):4362-4372. (in Chinese)
[33]	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(12):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, LEI Y, LIAO B S. Establishment and applicant of near-infrared reflectance spectroscopy models for predicting main fatty acid contents of single seed in peanut. Acta Agronomica Sinica, 2019, 45(12):1891-1898. (in Chinese) doi: 10.3724/SP.J.1006.2019.94016

指标 Index	极小值 Min	极大值 Max	均值 Mean	标准差 SD	变异系数 CV (%)
X₁	3.00	4.22	3.60	0.26	7.34
X₂	2.67	3.89	3.44	0.27	7.71
X₃	1.78	4.44	2.44	0.68	27.78
X₄	2.00	3.89	2.93	0.40	13.80
X₅	1.11	3.00	1.79	0.43	24.32
X₆	1.00	3.22	1.69	0.43	25.27
X₇	30.86	80.73	58.61	11.03	18.82
X₈	69.97	145.83	109.89	17.45	15.88
X₉	33.97	50.13	42.50	3.99	9.39
X₁₀	12.08	19.27	15.91	1.82	11.46
X₁₁	7.12	10.04	8.89	0.67	7.52
X₁₂	1.49	2.24	1.81	0.20	10.94
X₁₃	31.35	69.50	48.91	8.05	16.46
X₁₄	105.64	258.63	158.94	30.77	19.36
X₁₅	7.66	39.44	17.09	6.78	39.65
X₁₆	17.38	38.50	23.29	4.28	18.37
X₁₇	88.53	201.62	128.13	26.69	20.83
X₁₈	7.41	29.04	14.35	4.82	33.59
X₁₉	42.02	56.50	52.15	3.05	5.86
X₂₀	18.05	27.04	22.50	1.99	8.84
X₂₁	1.88	7.41	3.56	1.23	34.64
X₂₂	28.07	80.83	42.97	15.31	35.63
X₂₃	5.13	52.76	37.24	12.97	34.84
X₂₄	5.16	13.38	10.82	1.96	18.15
X₂₅	1.89	4.73	3.06	0.72	23.53
X₂₆	1.12	1.94	1.52	0.22	14.22
X₂₇	0.89	3.96	2.08	0.71	33.97

类别 Cluster	X₇		X₂₁		X₂₀		X₁₉		X₂₇
类别 Cluster	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range	均值 Mean	范围 Range
第一类First cluster	64.21	55.44-74.56	6.86	6.24-7.41	22.19	20.84-23.38	44.84	42.02-46.62	2.13	1.78-2.77
第二类Second cluster	63.90	52.92-80.73	3.32	1.88-5.45	22.95	19.56-27.04	52.69	47.49-56.5	1.73	0.89-2.53
第三类Third cluster	44.54	30.86-55.02	3.20	2.25-3.93	21.53	18.05-24.7	52.95	49.96-54.61	2.90	1.59-3.96