小麦籽粒品质与品种的近红外光谱分析
|
摘要
小麦是世界上最重要的谷物资源之一,产量仅次于稻谷居第2位。小麦产量对世界经济有重要影响,在农业和工业中都需要对小麦的品质和品种进行分析和鉴别。本论文试验以关中地区小麦为材料,用德国Bruker公司的MPA近红外扫描仪进行籽粒的光谱扫描,同时对小麦籽粒的总淀粉含量、直链淀粉含量和蛋白含量进行化学检测,用OPUS 5.5、SAS 8.1、DPS 6.55分析软件对数据进行分析,建立小麦品质测定和品种鉴别的近红外模型,主要的研究结果如下:
小麦籽粒品质分析的近红外模型均用偏最小二乘法(PLSR)建立。小麦籽粒淀粉近红外模型的最佳建模方法为:预处理方法选择一阶导数+减去一条直线,光谱范围选择7501.9 cm-1~5450 cm-1,主成分数确定为7;模型的交叉验证决定系数R2cv达到0.6902,交叉验证标准差RMSECV值为1.08,外部验证决定系数R2val为0.8151,外部验证标准差RMSEP达到0.878。小麦籽粒直链淀粉近红外模型的最佳建模方法为:预处理方法选择矢量归一化,光谱范围选择7501.9 cm-1~4597.6 cm-1,主成分数确定为8;模型的交叉验证决定系数R2cv达到0.8552,交叉验证标准差RMSECV值为0.638,外部验证决定系数R2val为0.8403,外部验证标准差RMSEP达到0.675。小麦籽粒蛋白质近红外模型的最佳建模方法为:预处理方法选择一阶导数+减去一条直线,光谱范围选择10502 cm-1~6098 cm-1,主成分数确定为9;模型的交叉验证决定系数R2cv达到0.9528,交叉验证标准差RMSECV值为0.402,外部验证决定系数R2val为0.9449,外部验证标准差RMSEP达到0.422。
小麦品种鉴别模型的建立:依据光谱分析,选择光谱范围4000 cm-1~10500 cm-1,采用Savitzky-Golay平滑法,平滑点选择9,进行平滑处理,滤除噪声,再用矢量归一化处理。将处理后的光谱数据导入SAS中进行主成分分析。将得到的主成分作为新变量,分别用三种不同的模式识别方法(BP-人工神经网络、Fisher多类线性判别、Bayes多类逐步判别)分析,建立小麦品种鉴别模型,以模型的正确分类率来衡量其好坏。BP-人工神经网络分析方法选择6个主成分建立了一个6(输入层节点)—5(隐含层节点)—1(输出层节点)的三层ANN-BP模型,建立模型的校正集和验证集的正确分类率均达到100%;Fisher多类线性判别分析方法选择6个主成分分析,所建模型校正集的正确分类为96.8%,验证集的正确分类率达到100%;Bayes多类逐步判别分析方法选择3个主成分,所建模型的校正集和验证集的正确分类率均达到100%。比较分析,主成分分析结合Bayes多类逐步判别方法建立小麦品种鉴别模型效果较好。
建立小麦籽粒品质和品种的近红外模型是可行的,所建模型均有较好的预测效果。
Wheat is one of the word’s most important grain resource, the only yield in the 2nd. Wheat yield has important effect on world economy. The quality analysis and species identification are needed in agriculture and industry. This paper takes wheat in central Shaanxi area as samples, scan the wheat by MPA from German Bruker Corporation and then get the starch content,amylose content and protein content of whole wheat by chemistry test. With OPUS 5.5, SAS 8.1, DPS 6.55 analysis softwares to analyze data, establish near-infrared spectroscopy(NIRS) models of quality analysis and species identification. The main research results are as follows:
The NIRS models of wheat quality were established by partial least square regression(PLSR). The optimal NIRS modeling method of wheat starch are that pretreatment method is FD+SLS, NIRS region is 7501.9 cm-1~5450 cm-1 and the number of principal component is 7. The model parameters are that R2cv value achieves 0.6902, RMSECV is 1.08, R2val is 0.8151 and RMSEP achieves 0.878. The optimal NIRS modeling method of wheat amylose are that pretreatment method is SNV, NIRS region is 7501.9 cm-1~4597.6 cm-1 and the number of principal component is 8. The model parameters are that R2cv value achieves 0.8552, RMSECV is 0.678, R2val is 0.8403 and RMSEP achieves 0.675. The optimal NIRS modeling method of wheat protein are that pretreatment method is FD+SLS, NIRS region is 10502 cm-1~6098 cm-1 and the number fo principal component is 9. The model parameters are that R2cv value achieves 0.9528, RMSECV is 0.402, R2val is 0.9449 and RMSEP achieves 0.422.
Wheat cultivars identification model was established.based on spectral analysis, select NIRS region 4000 cm-1~10500 cm-1 and apply smoothing process(Savitzky-Golay smoothing method and smoothing point 9) to filter noise and SVN process. Then import the spectral data processed into SAS analysis software for PCA. The principal components computed by PCA was used as the new variables and analysized by three different methods of pattern recognition (BP-ANN, FLDA,BSDA) respectively, Wheat cultivars identification model was set up. The correct classification rate of identification model was used to measure its quality. With 6 principal components as input vector, and the nodes of input layer, pattern layer and output layer was 6, 5, 1, respectively, the ANN-BP model was build up well and the correct classification rates of calibration set and validation set are both 100%; Select 6 principal components to apply FLDA and the correct classification rates of calibration set and validation set are 96.8%, 100%, seprately; Select 3 principal components to apply BSDA and the correct classification rates of calibration set and validation set are both 100%. Through the comparion, the effect of the wheat cultivars identification model by principal component analysis compared with BSDA method is good.
It is feasible to establish NIRS model of wheat grain quality and cultivars. The effect of all models is good.
小麦籽粒品质分析的近红外模型均用偏最小二乘法(PLSR)建立。小麦籽粒淀粉近红外模型的最佳建模方法为:预处理方法选择一阶导数+减去一条直线,光谱范围选择7501.9 cm-1~5450 cm-1,主成分数确定为7;模型的交叉验证决定系数R2cv达到0.6902,交叉验证标准差RMSECV值为1.08,外部验证决定系数R2val为0.8151,外部验证标准差RMSEP达到0.878。小麦籽粒直链淀粉近红外模型的最佳建模方法为:预处理方法选择矢量归一化,光谱范围选择7501.9 cm-1~4597.6 cm-1,主成分数确定为8;模型的交叉验证决定系数R2cv达到0.8552,交叉验证标准差RMSECV值为0.638,外部验证决定系数R2val为0.8403,外部验证标准差RMSEP达到0.675。小麦籽粒蛋白质近红外模型的最佳建模方法为:预处理方法选择一阶导数+减去一条直线,光谱范围选择10502 cm-1~6098 cm-1,主成分数确定为9;模型的交叉验证决定系数R2cv达到0.9528,交叉验证标准差RMSECV值为0.402,外部验证决定系数R2val为0.9449,外部验证标准差RMSEP达到0.422。
小麦品种鉴别模型的建立:依据光谱分析,选择光谱范围4000 cm-1~10500 cm-1,采用Savitzky-Golay平滑法,平滑点选择9,进行平滑处理,滤除噪声,再用矢量归一化处理。将处理后的光谱数据导入SAS中进行主成分分析。将得到的主成分作为新变量,分别用三种不同的模式识别方法(BP-人工神经网络、Fisher多类线性判别、Bayes多类逐步判别)分析,建立小麦品种鉴别模型,以模型的正确分类率来衡量其好坏。BP-人工神经网络分析方法选择6个主成分建立了一个6(输入层节点)—5(隐含层节点)—1(输出层节点)的三层ANN-BP模型,建立模型的校正集和验证集的正确分类率均达到100%;Fisher多类线性判别分析方法选择6个主成分分析,所建模型校正集的正确分类为96.8%,验证集的正确分类率达到100%;Bayes多类逐步判别分析方法选择3个主成分,所建模型的校正集和验证集的正确分类率均达到100%。比较分析,主成分分析结合Bayes多类逐步判别方法建立小麦品种鉴别模型效果较好。
建立小麦籽粒品质和品种的近红外模型是可行的,所建模型均有较好的预测效果。
Wheat is one of the word’s most important grain resource, the only yield in the 2nd. Wheat yield has important effect on world economy. The quality analysis and species identification are needed in agriculture and industry. This paper takes wheat in central Shaanxi area as samples, scan the wheat by MPA from German Bruker Corporation and then get the starch content,amylose content and protein content of whole wheat by chemistry test. With OPUS 5.5, SAS 8.1, DPS 6.55 analysis softwares to analyze data, establish near-infrared spectroscopy(NIRS) models of quality analysis and species identification. The main research results are as follows:
The NIRS models of wheat quality were established by partial least square regression(PLSR). The optimal NIRS modeling method of wheat starch are that pretreatment method is FD+SLS, NIRS region is 7501.9 cm-1~5450 cm-1 and the number of principal component is 7. The model parameters are that R2cv value achieves 0.6902, RMSECV is 1.08, R2val is 0.8151 and RMSEP achieves 0.878. The optimal NIRS modeling method of wheat amylose are that pretreatment method is SNV, NIRS region is 7501.9 cm-1~4597.6 cm-1 and the number of principal component is 8. The model parameters are that R2cv value achieves 0.8552, RMSECV is 0.678, R2val is 0.8403 and RMSEP achieves 0.675. The optimal NIRS modeling method of wheat protein are that pretreatment method is FD+SLS, NIRS region is 10502 cm-1~6098 cm-1 and the number fo principal component is 9. The model parameters are that R2cv value achieves 0.9528, RMSECV is 0.402, R2val is 0.9449 and RMSEP achieves 0.422.
Wheat cultivars identification model was established.based on spectral analysis, select NIRS region 4000 cm-1~10500 cm-1 and apply smoothing process(Savitzky-Golay smoothing method and smoothing point 9) to filter noise and SVN process. Then import the spectral data processed into SAS analysis software for PCA. The principal components computed by PCA was used as the new variables and analysized by three different methods of pattern recognition (BP-ANN, FLDA,BSDA) respectively, Wheat cultivars identification model was set up. The correct classification rate of identification model was used to measure its quality. With 6 principal components as input vector, and the nodes of input layer, pattern layer and output layer was 6, 5, 1, respectively, the ANN-BP model was build up well and the correct classification rates of calibration set and validation set are both 100%; Select 6 principal components to apply FLDA and the correct classification rates of calibration set and validation set are 96.8%, 100%, seprately; Select 3 principal components to apply BSDA and the correct classification rates of calibration set and validation set are both 100%. Through the comparion, the effect of the wheat cultivars identification model by principal component analysis compared with BSDA method is good.
It is feasible to establish NIRS model of wheat grain quality and cultivars. The effect of all models is good.
引文
安志丛,周惠明,朱科学. 2009.预处理方法对小麦面筋蛋白功能性质及其酶解效率的影响.食品与发酵专业,35(9): 25~30
陈达,王芳,邵学广,等. 2004.近红外光谱与烟草样品总糖含量的非线性模型研究.光谱学与光谱分析,24(6): 672~674
陈锋,何中虎,崔党群. 2004.利用近红外透射光谱技术测定小麦籽粒硬度的研究.作物学报,30(5): 455~459
陈锋,何中虎,崔党群,赵武善,张艳,王德森. 2003.利用近红外透射光谱技术测定小麦品质性状的研究.麦类作物学报,23(3): 1~4
陈建,陈晓,李伟,王加华,韩东海. 2008.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究.光谱与光谱学分析,28(8): 1806~1809
陈鹰,乐俊明,丁映. 2009.酸水解~DNS法测定马铃薯中淀粉含量.种子,28(9): 109~110
褚小立,田高友,袁洪福,陆婉珍. 2006.小波变换结合多维偏最小二乘方法用于近红外光谱定量分析.分析化学(FENXIHUAXUE)研究简报,34: 175~178
褚小立,王艳斌,陆婉珍. 2007.近红外光谱仪国内外现状与展望.分析仪器,(4): 1~4
戴双,程敦公,李豪圣,刘爱峰,刘建军,赵振东,宋健民. 2008.小麦直、支链淀粉和总淀粉含量的比色快速测定研究.麦类作物学报,28(3): 442~447
杜朝,杨学举,刘桂茹,张彩英,常文锁,关正君. 2002.小麦面粉淀粉特性与烧烤品质关系的研究. 河北农业大学学报,25(4): 29~33
杜雨静,范英芳. 2010.人工神经网络用于三苯基丙烯腈衍生物的定量结构~活性关系模型.化工进展,29(1): 25~28
高居荣,彭莉,王秀芹,孙智英,封德顺,李兴锋,王洪刚. 2008.山东省主推小麦品种品质性状的近红外光谱分析.中国农学通报,24(11): 186~188
谷淑波,于振文,王东,张永丽. 2009.小麦贮藏蛋白对加工品质的影响及对环境的反应.山东农业大学学报(自然科学版),40(2): 309~312
郭星,温其标. 2006.测定直链淀粉含量的几种新方法.粮油加工,(4): 87~89
郝大程,陈士林,肖培根. 2009.基于分子生物学和基因组学的药用植物鉴别.药物生物技术,16(5): 490~494
郝炯,渠云芳. 2009. DNA分子标记在作物育种中的应用.山西农业科学,37(3): 81~85
郝小燕,张巨松,顾卫红,麻浩. 2009.响应面法及三波长技术优化蒽酮比色法测定豆类淀粉含量的研究.中国粮油学报,24(4): 169~171
何勇,李晓丽,邵咏妮. 2006a.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究. 光谱学与光谱分析,26(5): 850~853
何勇,冯水娟,李晓丽,裘正军. 2006b.应用近红外光谱快速鉴别酸奶品种的研究.光谱与光谱学分析,26(11): 2021~2023
胡红利,白涛,马采文,张娟. 2010.基于小波变换的红外焦平面阵列图像边缘保护处理法.光子学报,39(1): 169~172
黄晓钰,刘邻渭. 2002.食品化学综合实验.北京:中国农业大学出版社:114~135
黄艳,王锡昌. 2007.近红外光谱分析在食品检测中的最新进展.食品研究与开发,28(7): 137~140
金玉红,张开利,张兴春,杜金华. 2009.双波长测定小麦及小麦芽中直链、支链淀粉含量.中国粮油学报,24(1): 137~140
乐俊明,陈鹰,丁映. 2005.近红外光谱分析法测定烟草化学成分.贵州农业科学,33(3): 62
李冬云,李彩云. 2007.近红外光谱信号的优化处理.计算机与应用化学,24(10): 1418~1420
李建平,傅霞萍,周莹等. 2006. NIRS定量分析技术在枇杷可溶性固形物无损检测中的应用.光谱学与光谱分析,26(9): 1605~1609
李俊超,刘钟栋,岑涛,孙红明. 2008.小麦淀粉中不同形状淀粉对流变学性质的影响.食品工业科技,29(7): 63~68
李维莉,张亚平. 2010.近红外光谱的主成分分析——马氏距离分类法应用于品牌卷烟烟丝的快速鉴别.云南农业大学学报,25(3): 268~271
李晓丽,胡兴越,何勇. 2006.基于主成分和多类判别分析的可见~红外光谱水蜜桃品种鉴别新方法.红外与毫米波学报,25(16): 417~420
李晓丽,唐月明,何勇,应霞芳. 2008.基于可见/近红外光谱的水稻品种快速鉴别研究.光谱学与光谱分析,28(3): 578~581
梁亮,刘志霄,杨敏华,张佑祥,汪承华. 2009.基于可见/近红外反射光谱的稻米品种与真伪鉴别. 红外与毫米波学报,28(5): 353~356
刘桂华,杨雪,李卫华. 2007.小麦淀粉、蛋白质特性与面条品质关系的研究进展.新疆农业科学,44(2): 176~179.
陆启玉,郭祀远,李炜. 2010.麦醇溶蛋白对湿面条品质的影响.河南工业大学学报(自然科学版),31(1): 1~3,94
卢文喜,陈社明,王晨子,刘磊,贾洪玮,吕德全. 2010.基于小波变换的大安地区年降水量变化特征. 吉林大学学报(地球科学版),40(1): 121~127
彭玉魁,李菊英,祁振秀. 1997.近红外光谱分析技术在小麦营养成份鉴定上的应用.麦类作物,17(2): 33~35
邵咏妮,何勇,鲍一丹. 2008.基于独立组分分析和BP神经网络的可见/近红外光谱蜂蜜品牌的鉴别.光谱与光谱学分析,28(3): 602~605
沈林峰,沈掌泉. 2008.应用近红外光谱和偏最小二乘回归法预测玉米中淀粉含量.化学分析计量,17(6): 26~28
谭超,吴同,覃鑫. 2008.偏最小二乘组合后向区间选择在近红外定量建模中的应用.计算机与应用化学,25(4): 509~513
汤守鹏,姚鑫锋,姚霞,田永超,曹卫星,朱艳. 2009.基于主成分分析和小波神经网络的近红外多组分建模研究.分析化学(FENXIHUAXUE)研究报告,37(10): 1445~1450
陶锦鸿,郑铁松. 2009.旋光法测定莲子淀粉含量的研究.食品工业科技,(1): 306~308
田高友,袁洪福,褚小立,刘慧颖,陆婉珍. 2005.结合小波变换与微分法改善近红外光谱分析精度. 光谱与光谱学分析,25(4): 516~520
田高友,袁洪福,刘慧颖,陆婉珍. 2004.小波变换用于近红外光谱性质分析.分析化学,32(9): 1125~1130
童晓星,鲍一丹,何勇. 2008.应用近红外光谱技术快速检别酱油品牌的研究.光谱与光谱学分析,28(3): 597~601
王多加,周向阳,金同铭. 2004. NIRS检测技术在农业和食品分析上的应用.光谱学与光谱分析,24(4): 447~450.
王芳,陈达,邵学广,苏庆德. 2004.近红外光谱与烟草样品总糖含量的非线性模型研究.烟草科技,(3): 31~34
王广鹏,刘庆香,孔德军,张志宏. 2007.两种板栗淀粉含量测定方法的比较研究.安徽农业通报,13(18): 27,107
王海萍,师凤华,唐朝晖,李保云,刘广田. 2007.一些小麦品种的淀粉特性分析.麦类作物学报,27(3): 479~482
王惠文. 1999.偏最小二乘回归方法及其应用.北京:国防工业初版社: 2
王建国. 2010.一种新的基于小波变换与SFM遥感影像的融合算法.江苏农业科学,(1): 368~370
王莉,刘飞,何勇. 2008.应用可见~近红外光谱技术进行白醋品牌和pH值的快速检测.光谱与光谱学分析,28(4): 813~816
王美美,范璐,钱向明,谢洪学. 2009. 3种植物油傅里叶变换红外光谱信息的判别分析研究.中国油脂,34(4): 72~74
王玮,张泽俊,薛文通,张惠. 2009.近红外检测技术在小麦品质及面制品研究中的应用.食品科技,(9): 211~214
王卫东,谷运红,秦广雍,霍裕平. 2007.近红外漫反射光谱法测定整粒小麦单株蛋白质含量.光谱与光谱学分析,27(4): 697~701
王宪泽,阚世红. 2004部分山东小麦品种面粉粘度性状及其与面条品质相关的研究.中国粮油学报,19(6): 8~10
王小品,贺振华,熊晓军. 2010.基于小波变换与奇异值分解的地震资料去噪新方法.石油天然气学报(江汉石油学院学报),32(1): 232~236, 280~281
王晓曦,王修法,温纪平,郭祯祥. 2008.世界小麦产量及加工业发展概况.粮食加工,33(4): 11~13.
吴红京,郝冰,唐根源,林玉娟. 1997.高效凝胶过滤色谱法分离测定豆薯种子蛋白.色谱,15(2): 153~155
吴静珠,刘翠玲,陈岩,陈媛媛,隋淑霞. 2008.基于近红外光谱和系统聚类法的样品分类方法研究. 农机化研究,(11): 143~145
谢军,潘涛,陈洁梅,陈华舟,任小焕. 2010.血糖近红外光谱分析的Savitzky~Golay平滑模式与偏最小二乘法因子数的联合优选.分析化学(FENXIHUAXUE)研究报告,38(3): 342~346
谢丽娟,应义斌,应铁进,田海清,牛晓颖,傅霞萍. 2008.可见/近红外光谱分析技术鉴别转基因番茄叶.光谱学与光谱分析,28(5): 1062~1066
熊雪梅,王一鸣. 2007.基于近红外光谱和系统聚类法的蝗虫识别模型研究.激光与红外,37(3): 237~239
严衍禄. 2005.近红外光谱分析基础与应用.北京:中国轻工业出版社: 1
阳志刚,邢俊杰,詹庆才,朱克永,王伟平,刘芳芳,何赵云,袁隆平,曹孟良. 2009.基于杂交水稻种子鉴别其亲本的分子检测技术.杂交水稻,24(2): 78~80
姚大年,李保云,朱金宝,梁荣奇,刘广田. 1999.小麦品种主要淀粉性状及面条品质预测指标的研究.中国农业科学,32(6): 84~88
禹萍,范世宾,朗秋美,焦润生,张永哲. 2000.酶电极法测定谷物淀粉含量的研究.中国卫生检验杂志,10(5): 515~517
易时来,邓烈,何绍兰,谢让金,张璇. 2007.近红外光谱技术在农作物品质检测上的研究进展.湖南农业科学,(6): 67~69
尹力,刘强,王惠文. 2003.偏最小二乘相关算法在系统建模中的两类典型应用.系统仿真学报,15(1): 135~137
张建新,李慧. 2008.傅立叶变换近红外光谱法测定豆腐干中总酸、蛋白质和水分含量.食品与发酵工业,34(1): 124~128
张银,周孟然. 2007.近红外光谱分析技术的数据处理方法.红外技术,29(6): 345~348
张勇,何中虎. 2002.我中春麦区部分小麦品种品质状况分析.中国农业科学,35(5): 471~475
赵立军. 2008.世界粮食形势最新动态.世界农业,(8): 1~2
赵琛,瞿海斌,程翼宇. 2004.虫草氨基酸的人工神经网络~近红外光谱快速测定方法.光谱与光谱学分析,24(1): 50~53
赵永亮. 2005.一种同时测定小麦种子中直链淀粉、总淀粉含量的新方法.食品与发酵工业,31(8): 23~26
郑咏梅,张军,陈星旦,申铉国,张铁强. 2004.短波近红外光谱的整粒小麦蛋白质PLS方法的定量分析.光谱与光谱学分析,24(9): 1047~1049
纵伟,盛欣昕. 2008.近红外分析技术在粮油产品加工中的应用.食品加工,(2): 37~40
周竹,刘洁,李小昱,李培武,王为,展慧. 2009.霉变板栗的近红外光谱和神经网络方法判别.农业机械学报,40: 109~112
祝诗平,王一鸣,张小超. 2003.小波消噪及其在小麦蛋白质含量近红外光谱分析中的应用.西南农业大学学报(自然科学版),25(6): 522~525
AACC. 1995. Approved Method of the American Association of Cereal Chemists method. American Association of Cereal Chemists Inc., St. Paul, MN, USA.
Barclay V J, Bonner R F, Hamilton I P. 1997. Application of wavelet transforms to experimental spectra:smoothing, denoising, and data set compression. Anal Chem, 69(1): 78~90
Cocchi M, Durante C, Foca G, Marchetti A, Tassi L, Ulrici A. 2006. Durum wheat adulteration detection by NIR spectroscopy multivariate calibration. Talanta, 68(5): 1505~1511
Cocchi M, Corbellini M, Foca G, Lucisano M M, Pagani A, Tassi L, Ulrici A. 2007. Classification of bread wheat flours in different quality categories by a wavelet~based feature selection/classification algorithm on NIR spectra. Analytica Chimica Acta, 544(1~2): 100~107
Crosbie G B. 1991. The relationship between starch swelling properties , paste viscosity and boiled noodle quality in wheat flours. Journal of Cereal Science, 13 (2): 145~150.
Davis E A. 1994. Wheat starch. Cereal Foods World, (39): 34~36
Delwiche S R, Williams R H. 2000. Protein content of bulk wheat from near~infrared reflectance. Cereal Chem, 77(1): 86~89
Delwiche S R, Graybosch R A. 2002. Identification of waxy wheat by near~infrared reflectance spectroscopy. Cereal Science, (35): 29~38
Donald D, Everinghamy, Coomans D. 2005. Integrated wavelet principal component mapping for unsupervised clustering on near infra~red spectra. Chemometrics and Intelligent Laboratory Syatems, 77(1~2): 32
Foca G, Cocchi M, Vigni M L, Caramanico R, Corbellini M, Ulrici A. 2009. Different feature selection strategies in the wavelet domain applied to NIR~based quality classification models of bread wheat flours. Chemometrics and Intelligent Laboratory Systems, 99(2): 91~100
Guo G, Jackson D S, Graybosch R A, Parkhurst A M. 2003. Asian salted noodle quality: impact ofamylose content adjustments using waxy wheat flour.Cereal Chemistry, 80(4): 437~445
Iura H , Tanii S. 1994. Endosperm starch properties in several wheat cultivars preferred for Jpanese noodles. Euphytica, 72 (3): 171~175
Jirsa O, Hru?kováM, ?vec I. 2008. Near~infrared prediction of milling and baking parameters of wheat varieties. Ournal of Food Engineering, 87(1): 21~25
Jouan R D, Walczak B, PoppiR J, deNoord O E, MassartD L. 1997. Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration. Anal Chem, 69(21): 4317~4323
Kaddour A A, Mondet M, Cuq B. 2008. Description of Chemical Changes Implied During Bread Dough Mixing by FT~ATR Mid~Infrared Spectroscopy. Cereal Chemistry,85(5): 673~678
Kelly J J, Callis J B. 1990. Nondestructive analytical procedure for simultaneous estimation of the major classes hydrocarbon constituents finished gasoline. Anal Chem, 62(14): 1444~1451.
Konik C M, Miskelly D M, Gras P W. 1992. Contribution of starch and non~starch parameters to the eating quality of Japanese white salted noodle. Journal of the Science of Food and Agriulturec, 58(3): 403~406
Lu R, Guyer D E, Beaudry R.M. 2000. Determination of firmness and sugar content of apples using near~infrared diffuse reflectance .Journal of Texture Studies, (31): 615~630
McGlone V A, Jordan R B, Martinsen P J. 2002. Vis/NIR estimation at harvest of pre~ and post~storage quality indices for‘Royal Gala’apple. Postharvest billogy and Technology, (25): 135~144
McClelland J L,Rumelhart D E. 1981. An interactive activation model of context effects in letter perception. Psychological Review, (88): 375
McCormick K M , Panozzo J F , Hong S H. 1991. A swelling power test for selecting potential noodle quality wheats. Australian Journal of Agricultural Research, (42): 317~323
Miralbes C. 2008. Discrimination of European Wheat varieties using near infrared reflectance spectroscopy. Food Chemistry, 106(1): 386~389
Perez~Mendoza J, Throne J E, Dowell F E, Baker J E. 2002. Detection of insect fragments in wheat flour by near~infrared spectroscopy. Journal of Stored Products Research, 39(3):305~312
Vannucci M, Sha N, Brown P J. 2005. Nir and mass spectra classification: bayesian methods for wavelet~based featrue selection. Chemometrics and Intelligent Laboratory Systems, 77(1~2): 139~148
Schrader B, Schulz H, Quilitzsch R, Steuer B. 2002. Quantitative analysis of various citrus oils by ATR/RT~IR and NIR~FT raman spectroscopy. Applied Spectroscopy, (56):117~124
Shao L M, Lin X Q, Shao X G. 2002. A wavelet transform and its application to spectroscopic analysis. Applied Spectroscopy, 37(4): 429~450
Trygg J, Wold S. 1998. PLS regression on wavelet compressed NIR spectra. Chemometrics and intelligent Laboratory Systems, 42(1~2): 209~220
Yamamori M, Quynh N T. 2000. Differential effects of Wx~A1,~B1 and D1 protein deficiencies on apparent amylase content and starch pasting propertied in common wheat. Theoretical and Applied Genetics, 100(1): 32~38
Zhao X C , Batey I L , Sharp P J ,Crosbie G, Barclay I, Wilson R, Morell M K, Appels R. 1998. A single genetic locus associated wit h starch granule properties and noodle quality in wheat. Jornal of Cereal Science, 27(1): 7~13
陈达,王芳,邵学广,等. 2004.近红外光谱与烟草样品总糖含量的非线性模型研究.光谱学与光谱分析,24(6): 672~674
陈锋,何中虎,崔党群. 2004.利用近红外透射光谱技术测定小麦籽粒硬度的研究.作物学报,30(5): 455~459
陈锋,何中虎,崔党群,赵武善,张艳,王德森. 2003.利用近红外透射光谱技术测定小麦品质性状的研究.麦类作物学报,23(3): 1~4
陈建,陈晓,李伟,王加华,韩东海. 2008.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究.光谱与光谱学分析,28(8): 1806~1809
陈鹰,乐俊明,丁映. 2009.酸水解~DNS法测定马铃薯中淀粉含量.种子,28(9): 109~110
褚小立,田高友,袁洪福,陆婉珍. 2006.小波变换结合多维偏最小二乘方法用于近红外光谱定量分析.分析化学(FENXIHUAXUE)研究简报,34: 175~178
褚小立,王艳斌,陆婉珍. 2007.近红外光谱仪国内外现状与展望.分析仪器,(4): 1~4
戴双,程敦公,李豪圣,刘爱峰,刘建军,赵振东,宋健民. 2008.小麦直、支链淀粉和总淀粉含量的比色快速测定研究.麦类作物学报,28(3): 442~447
杜朝,杨学举,刘桂茹,张彩英,常文锁,关正君. 2002.小麦面粉淀粉特性与烧烤品质关系的研究. 河北农业大学学报,25(4): 29~33
杜雨静,范英芳. 2010.人工神经网络用于三苯基丙烯腈衍生物的定量结构~活性关系模型.化工进展,29(1): 25~28
高居荣,彭莉,王秀芹,孙智英,封德顺,李兴锋,王洪刚. 2008.山东省主推小麦品种品质性状的近红外光谱分析.中国农学通报,24(11): 186~188
谷淑波,于振文,王东,张永丽. 2009.小麦贮藏蛋白对加工品质的影响及对环境的反应.山东农业大学学报(自然科学版),40(2): 309~312
郭星,温其标. 2006.测定直链淀粉含量的几种新方法.粮油加工,(4): 87~89
郝大程,陈士林,肖培根. 2009.基于分子生物学和基因组学的药用植物鉴别.药物生物技术,16(5): 490~494
郝炯,渠云芳. 2009. DNA分子标记在作物育种中的应用.山西农业科学,37(3): 81~85
郝小燕,张巨松,顾卫红,麻浩. 2009.响应面法及三波长技术优化蒽酮比色法测定豆类淀粉含量的研究.中国粮油学报,24(4): 169~171
何勇,李晓丽,邵咏妮. 2006a.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究. 光谱学与光谱分析,26(5): 850~853
何勇,冯水娟,李晓丽,裘正军. 2006b.应用近红外光谱快速鉴别酸奶品种的研究.光谱与光谱学分析,26(11): 2021~2023
胡红利,白涛,马采文,张娟. 2010.基于小波变换的红外焦平面阵列图像边缘保护处理法.光子学报,39(1): 169~172
黄晓钰,刘邻渭. 2002.食品化学综合实验.北京:中国农业大学出版社:114~135
黄艳,王锡昌. 2007.近红外光谱分析在食品检测中的最新进展.食品研究与开发,28(7): 137~140
金玉红,张开利,张兴春,杜金华. 2009.双波长测定小麦及小麦芽中直链、支链淀粉含量.中国粮油学报,24(1): 137~140
乐俊明,陈鹰,丁映. 2005.近红外光谱分析法测定烟草化学成分.贵州农业科学,33(3): 62
李冬云,李彩云. 2007.近红外光谱信号的优化处理.计算机与应用化学,24(10): 1418~1420
李建平,傅霞萍,周莹等. 2006. NIRS定量分析技术在枇杷可溶性固形物无损检测中的应用.光谱学与光谱分析,26(9): 1605~1609
李俊超,刘钟栋,岑涛,孙红明. 2008.小麦淀粉中不同形状淀粉对流变学性质的影响.食品工业科技,29(7): 63~68
李维莉,张亚平. 2010.近红外光谱的主成分分析——马氏距离分类法应用于品牌卷烟烟丝的快速鉴别.云南农业大学学报,25(3): 268~271
李晓丽,胡兴越,何勇. 2006.基于主成分和多类判别分析的可见~红外光谱水蜜桃品种鉴别新方法.红外与毫米波学报,25(16): 417~420
李晓丽,唐月明,何勇,应霞芳. 2008.基于可见/近红外光谱的水稻品种快速鉴别研究.光谱学与光谱分析,28(3): 578~581
梁亮,刘志霄,杨敏华,张佑祥,汪承华. 2009.基于可见/近红外反射光谱的稻米品种与真伪鉴别. 红外与毫米波学报,28(5): 353~356
刘桂华,杨雪,李卫华. 2007.小麦淀粉、蛋白质特性与面条品质关系的研究进展.新疆农业科学,44(2): 176~179.
陆启玉,郭祀远,李炜. 2010.麦醇溶蛋白对湿面条品质的影响.河南工业大学学报(自然科学版),31(1): 1~3,94
卢文喜,陈社明,王晨子,刘磊,贾洪玮,吕德全. 2010.基于小波变换的大安地区年降水量变化特征. 吉林大学学报(地球科学版),40(1): 121~127
彭玉魁,李菊英,祁振秀. 1997.近红外光谱分析技术在小麦营养成份鉴定上的应用.麦类作物,17(2): 33~35
邵咏妮,何勇,鲍一丹. 2008.基于独立组分分析和BP神经网络的可见/近红外光谱蜂蜜品牌的鉴别.光谱与光谱学分析,28(3): 602~605
沈林峰,沈掌泉. 2008.应用近红外光谱和偏最小二乘回归法预测玉米中淀粉含量.化学分析计量,17(6): 26~28
谭超,吴同,覃鑫. 2008.偏最小二乘组合后向区间选择在近红外定量建模中的应用.计算机与应用化学,25(4): 509~513
汤守鹏,姚鑫锋,姚霞,田永超,曹卫星,朱艳. 2009.基于主成分分析和小波神经网络的近红外多组分建模研究.分析化学(FENXIHUAXUE)研究报告,37(10): 1445~1450
陶锦鸿,郑铁松. 2009.旋光法测定莲子淀粉含量的研究.食品工业科技,(1): 306~308
田高友,袁洪福,褚小立,刘慧颖,陆婉珍. 2005.结合小波变换与微分法改善近红外光谱分析精度. 光谱与光谱学分析,25(4): 516~520
田高友,袁洪福,刘慧颖,陆婉珍. 2004.小波变换用于近红外光谱性质分析.分析化学,32(9): 1125~1130
童晓星,鲍一丹,何勇. 2008.应用近红外光谱技术快速检别酱油品牌的研究.光谱与光谱学分析,28(3): 597~601
王多加,周向阳,金同铭. 2004. NIRS检测技术在农业和食品分析上的应用.光谱学与光谱分析,24(4): 447~450.
王芳,陈达,邵学广,苏庆德. 2004.近红外光谱与烟草样品总糖含量的非线性模型研究.烟草科技,(3): 31~34
王广鹏,刘庆香,孔德军,张志宏. 2007.两种板栗淀粉含量测定方法的比较研究.安徽农业通报,13(18): 27,107
王海萍,师凤华,唐朝晖,李保云,刘广田. 2007.一些小麦品种的淀粉特性分析.麦类作物学报,27(3): 479~482
王惠文. 1999.偏最小二乘回归方法及其应用.北京:国防工业初版社: 2
王建国. 2010.一种新的基于小波变换与SFM遥感影像的融合算法.江苏农业科学,(1): 368~370
王莉,刘飞,何勇. 2008.应用可见~近红外光谱技术进行白醋品牌和pH值的快速检测.光谱与光谱学分析,28(4): 813~816
王美美,范璐,钱向明,谢洪学. 2009. 3种植物油傅里叶变换红外光谱信息的判别分析研究.中国油脂,34(4): 72~74
王玮,张泽俊,薛文通,张惠. 2009.近红外检测技术在小麦品质及面制品研究中的应用.食品科技,(9): 211~214
王卫东,谷运红,秦广雍,霍裕平. 2007.近红外漫反射光谱法测定整粒小麦单株蛋白质含量.光谱与光谱学分析,27(4): 697~701
王宪泽,阚世红. 2004部分山东小麦品种面粉粘度性状及其与面条品质相关的研究.中国粮油学报,19(6): 8~10
王小品,贺振华,熊晓军. 2010.基于小波变换与奇异值分解的地震资料去噪新方法.石油天然气学报(江汉石油学院学报),32(1): 232~236, 280~281
王晓曦,王修法,温纪平,郭祯祥. 2008.世界小麦产量及加工业发展概况.粮食加工,33(4): 11~13.
吴红京,郝冰,唐根源,林玉娟. 1997.高效凝胶过滤色谱法分离测定豆薯种子蛋白.色谱,15(2): 153~155
吴静珠,刘翠玲,陈岩,陈媛媛,隋淑霞. 2008.基于近红外光谱和系统聚类法的样品分类方法研究. 农机化研究,(11): 143~145
谢军,潘涛,陈洁梅,陈华舟,任小焕. 2010.血糖近红外光谱分析的Savitzky~Golay平滑模式与偏最小二乘法因子数的联合优选.分析化学(FENXIHUAXUE)研究报告,38(3): 342~346
谢丽娟,应义斌,应铁进,田海清,牛晓颖,傅霞萍. 2008.可见/近红外光谱分析技术鉴别转基因番茄叶.光谱学与光谱分析,28(5): 1062~1066
熊雪梅,王一鸣. 2007.基于近红外光谱和系统聚类法的蝗虫识别模型研究.激光与红外,37(3): 237~239
严衍禄. 2005.近红外光谱分析基础与应用.北京:中国轻工业出版社: 1
阳志刚,邢俊杰,詹庆才,朱克永,王伟平,刘芳芳,何赵云,袁隆平,曹孟良. 2009.基于杂交水稻种子鉴别其亲本的分子检测技术.杂交水稻,24(2): 78~80
姚大年,李保云,朱金宝,梁荣奇,刘广田. 1999.小麦品种主要淀粉性状及面条品质预测指标的研究.中国农业科学,32(6): 84~88
禹萍,范世宾,朗秋美,焦润生,张永哲. 2000.酶电极法测定谷物淀粉含量的研究.中国卫生检验杂志,10(5): 515~517
易时来,邓烈,何绍兰,谢让金,张璇. 2007.近红外光谱技术在农作物品质检测上的研究进展.湖南农业科学,(6): 67~69
尹力,刘强,王惠文. 2003.偏最小二乘相关算法在系统建模中的两类典型应用.系统仿真学报,15(1): 135~137
张建新,李慧. 2008.傅立叶变换近红外光谱法测定豆腐干中总酸、蛋白质和水分含量.食品与发酵工业,34(1): 124~128
张银,周孟然. 2007.近红外光谱分析技术的数据处理方法.红外技术,29(6): 345~348
张勇,何中虎. 2002.我中春麦区部分小麦品种品质状况分析.中国农业科学,35(5): 471~475
赵立军. 2008.世界粮食形势最新动态.世界农业,(8): 1~2
赵琛,瞿海斌,程翼宇. 2004.虫草氨基酸的人工神经网络~近红外光谱快速测定方法.光谱与光谱学分析,24(1): 50~53
赵永亮. 2005.一种同时测定小麦种子中直链淀粉、总淀粉含量的新方法.食品与发酵工业,31(8): 23~26
郑咏梅,张军,陈星旦,申铉国,张铁强. 2004.短波近红外光谱的整粒小麦蛋白质PLS方法的定量分析.光谱与光谱学分析,24(9): 1047~1049
纵伟,盛欣昕. 2008.近红外分析技术在粮油产品加工中的应用.食品加工,(2): 37~40
周竹,刘洁,李小昱,李培武,王为,展慧. 2009.霉变板栗的近红外光谱和神经网络方法判别.农业机械学报,40: 109~112
祝诗平,王一鸣,张小超. 2003.小波消噪及其在小麦蛋白质含量近红外光谱分析中的应用.西南农业大学学报(自然科学版),25(6): 522~525
AACC. 1995. Approved Method of the American Association of Cereal Chemists method. American Association of Cereal Chemists Inc., St. Paul, MN, USA.
Barclay V J, Bonner R F, Hamilton I P. 1997. Application of wavelet transforms to experimental spectra:smoothing, denoising, and data set compression. Anal Chem, 69(1): 78~90
Cocchi M, Durante C, Foca G, Marchetti A, Tassi L, Ulrici A. 2006. Durum wheat adulteration detection by NIR spectroscopy multivariate calibration. Talanta, 68(5): 1505~1511
Cocchi M, Corbellini M, Foca G, Lucisano M M, Pagani A, Tassi L, Ulrici A. 2007. Classification of bread wheat flours in different quality categories by a wavelet~based feature selection/classification algorithm on NIR spectra. Analytica Chimica Acta, 544(1~2): 100~107
Crosbie G B. 1991. The relationship between starch swelling properties , paste viscosity and boiled noodle quality in wheat flours. Journal of Cereal Science, 13 (2): 145~150.
Davis E A. 1994. Wheat starch. Cereal Foods World, (39): 34~36
Delwiche S R, Williams R H. 2000. Protein content of bulk wheat from near~infrared reflectance. Cereal Chem, 77(1): 86~89
Delwiche S R, Graybosch R A. 2002. Identification of waxy wheat by near~infrared reflectance spectroscopy. Cereal Science, (35): 29~38
Donald D, Everinghamy, Coomans D. 2005. Integrated wavelet principal component mapping for unsupervised clustering on near infra~red spectra. Chemometrics and Intelligent Laboratory Syatems, 77(1~2): 32
Foca G, Cocchi M, Vigni M L, Caramanico R, Corbellini M, Ulrici A. 2009. Different feature selection strategies in the wavelet domain applied to NIR~based quality classification models of bread wheat flours. Chemometrics and Intelligent Laboratory Systems, 99(2): 91~100
Guo G, Jackson D S, Graybosch R A, Parkhurst A M. 2003. Asian salted noodle quality: impact ofamylose content adjustments using waxy wheat flour.Cereal Chemistry, 80(4): 437~445
Iura H , Tanii S. 1994. Endosperm starch properties in several wheat cultivars preferred for Jpanese noodles. Euphytica, 72 (3): 171~175
Jirsa O, Hru?kováM, ?vec I. 2008. Near~infrared prediction of milling and baking parameters of wheat varieties. Ournal of Food Engineering, 87(1): 21~25
Jouan R D, Walczak B, PoppiR J, deNoord O E, MassartD L. 1997. Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration. Anal Chem, 69(21): 4317~4323
Kaddour A A, Mondet M, Cuq B. 2008. Description of Chemical Changes Implied During Bread Dough Mixing by FT~ATR Mid~Infrared Spectroscopy. Cereal Chemistry,85(5): 673~678
Kelly J J, Callis J B. 1990. Nondestructive analytical procedure for simultaneous estimation of the major classes hydrocarbon constituents finished gasoline. Anal Chem, 62(14): 1444~1451.
Konik C M, Miskelly D M, Gras P W. 1992. Contribution of starch and non~starch parameters to the eating quality of Japanese white salted noodle. Journal of the Science of Food and Agriulturec, 58(3): 403~406
Lu R, Guyer D E, Beaudry R.M. 2000. Determination of firmness and sugar content of apples using near~infrared diffuse reflectance .Journal of Texture Studies, (31): 615~630
McGlone V A, Jordan R B, Martinsen P J. 2002. Vis/NIR estimation at harvest of pre~ and post~storage quality indices for‘Royal Gala’apple. Postharvest billogy and Technology, (25): 135~144
McClelland J L,Rumelhart D E. 1981. An interactive activation model of context effects in letter perception. Psychological Review, (88): 375
McCormick K M , Panozzo J F , Hong S H. 1991. A swelling power test for selecting potential noodle quality wheats. Australian Journal of Agricultural Research, (42): 317~323
Miralbes C. 2008. Discrimination of European Wheat varieties using near infrared reflectance spectroscopy. Food Chemistry, 106(1): 386~389
Perez~Mendoza J, Throne J E, Dowell F E, Baker J E. 2002. Detection of insect fragments in wheat flour by near~infrared spectroscopy. Journal of Stored Products Research, 39(3):305~312
Vannucci M, Sha N, Brown P J. 2005. Nir and mass spectra classification: bayesian methods for wavelet~based featrue selection. Chemometrics and Intelligent Laboratory Systems, 77(1~2): 139~148
Schrader B, Schulz H, Quilitzsch R, Steuer B. 2002. Quantitative analysis of various citrus oils by ATR/RT~IR and NIR~FT raman spectroscopy. Applied Spectroscopy, (56):117~124
Shao L M, Lin X Q, Shao X G. 2002. A wavelet transform and its application to spectroscopic analysis. Applied Spectroscopy, 37(4): 429~450
Trygg J, Wold S. 1998. PLS regression on wavelet compressed NIR spectra. Chemometrics and intelligent Laboratory Systems, 42(1~2): 209~220
Yamamori M, Quynh N T. 2000. Differential effects of Wx~A1,~B1 and D1 protein deficiencies on apparent amylase content and starch pasting propertied in common wheat. Theoretical and Applied Genetics, 100(1): 32~38
Zhao X C , Batey I L , Sharp P J ,Crosbie G, Barclay I, Wilson R, Morell M K, Appels R. 1998. A single genetic locus associated wit h starch granule properties and noodle quality in wheat. Jornal of Cereal Science, 27(1): 7~13