光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
5期
1253-1258
,共6页
黄华军%严衍禄%申兵辉%刘哲%顾建成%李绍明%朱德海%张晓东%马钦%李林%安冬
黃華軍%嚴衍祿%申兵輝%劉哲%顧建成%李紹明%硃德海%張曉東%馬欽%李林%安鼕
황화군%엄연록%신병휘%류철%고건성%리소명%주덕해%장효동%마흠%리림%안동
近红外光谱分析%玉米杂交种%纯度鉴别%仿生模式识别
近紅外光譜分析%玉米雜交種%純度鑒彆%倣生模式識彆
근홍외광보분석%옥미잡교충%순도감별%방생모식식별
Near infrared spectroscopy%Maize hybrid%Purity discrimination%Biomimetic pattern recognition
以不同产地和年份的农华101（NH101）玉米杂交种和母本种子为对象，研究了鉴别玉米杂交种子纯度的近红外光谱分析方法。光谱采集时间跨度达10个月，运用傅里叶变换（FT ）近红外光谱仪器，在不同季节用23天（分五个时间段）采集了这些样品共920条玉米单子粒近红外漫反射光谱。全部原始光谱用移动窗口平均、一阶差分导数和矢量归一化进行预处理，使用主成分分析（PCA ）方法和线性判别分析（LDA ）方法降维，采用仿生模式识别（BPR）方法建立模型。通过对光谱预处理校正光谱失真，使样品光谱集在特征空间分布的范围收缩，相对距离增大了近70倍，实现了母本和杂交种子的鉴别。通过代表性样品的选择，提高了模型对光谱采集时间、地点、环境等条件变动的应变能力，也提高了模型对样品种子制种时间与地点变动的应变能力，增强了模型的稳健性，使测试集玉米单子粒杂交种和母本种子的平均正确识别率达到95％以上，而平均正确拒识率也达到85％以上。
以不同產地和年份的農華101（NH101）玉米雜交種和母本種子為對象，研究瞭鑒彆玉米雜交種子純度的近紅外光譜分析方法。光譜採集時間跨度達10箇月，運用傅裏葉變換（FT ）近紅外光譜儀器，在不同季節用23天（分五箇時間段）採集瞭這些樣品共920條玉米單子粒近紅外漫反射光譜。全部原始光譜用移動窗口平均、一階差分導數和矢量歸一化進行預處理，使用主成分分析（PCA ）方法和線性判彆分析（LDA ）方法降維，採用倣生模式識彆（BPR）方法建立模型。通過對光譜預處理校正光譜失真，使樣品光譜集在特徵空間分佈的範圍收縮，相對距離增大瞭近70倍，實現瞭母本和雜交種子的鑒彆。通過代錶性樣品的選擇，提高瞭模型對光譜採集時間、地點、環境等條件變動的應變能力，也提高瞭模型對樣品種子製種時間與地點變動的應變能力，增彊瞭模型的穩健性，使測試集玉米單子粒雜交種和母本種子的平均正確識彆率達到95％以上，而平均正確拒識率也達到85％以上。
이불동산지화년빈적농화101（NH101）옥미잡교충화모본충자위대상，연구료감별옥미잡교충자순도적근홍외광보분석방법。광보채집시간과도체10개월，운용부리협변환（FT ）근홍외광보의기，재불동계절용23천（분오개시간단）채집료저사양품공920조옥미단자립근홍외만반사광보。전부원시광보용이동창구평균、일계차분도수화시량귀일화진행예처리，사용주성분분석（PCA ）방법화선성판별분석（LDA ）방법강유，채용방생모식식별（BPR）방법건립모형。통과대광보예처리교정광보실진，사양품광보집재특정공간분포적범위수축，상대거리증대료근70배，실현료모본화잡교충자적감별。통과대표성양품적선택，제고료모형대광보채집시간、지점、배경등조건변동적응변능력，야제고료모형대양품충자제충시간여지점변동적응변능력，증강료모형적은건성，사측시집옥미단자립잡교충화모본충자적평균정학식별솔체도95％이상，이평균정학거식솔야체도85％이상。
Near infrared spectroscopy analysis method of discrimination of maize hybrid seed purity was studied with the sample of Nong Hua 101 (NH101) from different origins and years .Spectral acquisition time lasted for 10 months .Using Fourier transform (FT) near infrared spectroscopy instruments ,including 23 days in different seasons (divided into five time periods) ,a total of 920 near infrared diffuse reflectance spectra of single corn grain of those samples were collected .Moving window aver-age ,first derivative and vector normalization were used to pretreat all original spectra ,principal component analysis (PCA) and linear discriminant analysis (LDA ) were applied to reduce data dimensionality ,and the discrimination model was established based on biomimetic pattern recognition (BPR) method .Spectral distortion was calibrated by spectra pretreatment ,which makes characteristics spatial distribution range of sample spectra set contract .The relative distance between hybrid and female parent increased by nearly 70-fold ,and the discrimination model achieved the identification of hybrid and female parent seeds .Through the choice of representative samples ,the model's response capacity to the changes in spectral acquisition time ,place and environ-ment ,etc .was improved .Besides ,the model's response capacity to the changes in time and site of seed production was also im-proved ,and the robustness of the model was enhanced .The average correct acceptance rate (CAR) of the test set reached more than 95% while the average correct rejection rate (CRR) of the test set also reached 85% .
