光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
Spectroscopy and Spectral Analysis
2015年
11期
3268-3274
,共7页
刘金%郭婷婷%李浩川%贾仕强%严衍禄%安冬%张垚%陈绍江
劉金%郭婷婷%李浩川%賈仕彊%嚴衍祿%安鼕%張垚%陳紹江
류금%곽정정%리호천%가사강%엄연록%안동%장요%진소강
可见光光谱%玉米%单倍体鉴别%模式识别
可見光光譜%玉米%單倍體鑒彆%模式識彆
가견광광보%옥미%단배체감별%모식식별
Vis spectroscopy%Maize%Haploid kernel discrimination%Support vector machine
单倍体技术已发展成为玉米遗传研究及现代玉米育种的重要技术之一,单倍体籽粒的鉴别筛选是其中的重要环节。目前单倍体籽粒主要是依赖于籽粒的 R1‐n j 遗传标记通过人工肉眼观察颜色的有或无进行鉴别,费时费工。而且部分材料由于标记颜色很难从籽粒外部观察到,导致人工筛选准确率较低。基于可见光光谱分析建立玉米单倍体籽粒鉴别方法,探索利用可见光光谱鉴别玉米单倍体籽粒的可行性。同时,由于每季用于诱导单倍体的育种材料不尽相同,模型须能够鉴别未参加建模的材料的单倍体。本研究以9个遗传背景的单倍体和杂交籽粒共284粒作为试验材料,利用便携式紫外‐可见光光纤光谱仪采集单个玉米籽粒的可见光漫透射光谱。光谱数据经平滑、矢量归一化预处理和主成分分析,基于支持向量机方法建立单倍体和杂交籽粒判别模型。每次选择1个背景的样本作为测试集,其余背景的样本作为建模集对模型进行交叉验证。模型交叉验证平均正确判别率达到92.06%。其中8次测试正确判别率在85%以上。结果表明利用可见光光谱分析建立玉米单倍体籽粒鉴别方法,并使模型可鉴别未参与建模材料的单倍体具有可行性。并且基于该方法有望建立玉米单倍体籽粒的自动化快速筛选系统,提高玉米单倍体育种效率。
單倍體技術已髮展成為玉米遺傳研究及現代玉米育種的重要技術之一,單倍體籽粒的鑒彆篩選是其中的重要環節。目前單倍體籽粒主要是依賴于籽粒的 R1‐n j 遺傳標記通過人工肉眼觀察顏色的有或無進行鑒彆,費時費工。而且部分材料由于標記顏色很難從籽粒外部觀察到,導緻人工篩選準確率較低。基于可見光光譜分析建立玉米單倍體籽粒鑒彆方法,探索利用可見光光譜鑒彆玉米單倍體籽粒的可行性。同時,由于每季用于誘導單倍體的育種材料不儘相同,模型鬚能夠鑒彆未參加建模的材料的單倍體。本研究以9箇遺傳揹景的單倍體和雜交籽粒共284粒作為試驗材料,利用便攜式紫外‐可見光光纖光譜儀採集單箇玉米籽粒的可見光漫透射光譜。光譜數據經平滑、矢量歸一化預處理和主成分分析,基于支持嚮量機方法建立單倍體和雜交籽粒判彆模型。每次選擇1箇揹景的樣本作為測試集,其餘揹景的樣本作為建模集對模型進行交扠驗證。模型交扠驗證平均正確判彆率達到92.06%。其中8次測試正確判彆率在85%以上。結果錶明利用可見光光譜分析建立玉米單倍體籽粒鑒彆方法,併使模型可鑒彆未參與建模材料的單倍體具有可行性。併且基于該方法有望建立玉米單倍體籽粒的自動化快速篩選繫統,提高玉米單倍體育種效率。
단배체기술이발전성위옥미유전연구급현대옥미육충적중요기술지일,단배체자립적감별사선시기중적중요배절。목전단배체자립주요시의뢰우자립적 R1‐n j 유전표기통과인공육안관찰안색적유혹무진행감별,비시비공。이차부분재료유우표기안색흔난종자립외부관찰도,도치인공사선준학솔교저。기우가견광광보분석건립옥미단배체자립감별방법,탐색이용가견광광보감별옥미단배체자립적가행성。동시,유우매계용우유도단배체적육충재료불진상동,모형수능구감별미삼가건모적재료적단배체。본연구이9개유전배경적단배체화잡교자립공284립작위시험재료,이용편휴식자외‐가견광광섬광보의채집단개옥미자립적가견광만투사광보。광보수거경평활、시량귀일화예처리화주성분분석,기우지지향량궤방법건립단배체화잡교자립판별모형。매차선택1개배경적양본작위측시집,기여배경적양본작위건모집대모형진행교차험증。모형교차험증평균정학판별솔체도92.06%。기중8차측시정학판별솔재85%이상。결과표명이용가견광광보분석건립옥미단배체자립감별방법,병사모형가감별미삼여건모재료적단배체구유가행성。병차기우해방법유망건립옥미단배체자립적자동화쾌속사선계통,제고옥미단배체육충효솔。
Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many in‐stitutes and companies for their advantages of complete homozygosity and short breeding cycle length .A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer .At present ,haploid kernel selection is carried out manually using the“red‐crown”kernel trait (the haploid kernel has a non‐pigmented embryo and pigmented endosperm) controlled by the R1‐nj gene .Manual selection is time‐consuming and unreliable .Furthermore ,the color of the kernel embryo is concealed by the pericarp .Here ,we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM ) pattern recognition technology .The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV‐Vis spectrometer and integrating sphere .The raw spectral data were prepro‐cessed using smoothing and vector normalization methods .The desired feature wavelengths were selected based on the results of the Kolmogorov‐Smirnov test .The wavelengths with p values above 0.05 were eliminated be‐cause the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels .Principal component analysis was then performed to reduce the number of variables .The SVM model was evaluated by 9‐fold cross‐validation .In each round ,samples of one genotype were used as the testing set ,while those of other genotypes were used as the training set .The mean rate of correct discrimina‐tion was 92.06% .This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels .The method would help develop a rapid and accurate automated screening‐system for haploid kernels .