南疆骏枣叶片含水量的近红外光谱检测研究
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摘要
[目的]建立南疆骏枣叶片含水量的快速、无损的检测模型。[方法]选取73片完好、无损的骏枣叶片,运用NIR检测骏枣叶片含水量的重要指标。通过3种不同的光谱预处理方法进行预处理,建立骏枣叶片含水量的PLS检测模型。[结果]在预测骏枣叶片含水量的PLS模型中,最好的组合是原始光谱+MSC+PLS,相关系数(R)由原始的0.673 1提高到0.874 6,预测精度(Precision)由0.950 7提高到0.957 8,预测残差平方和(PRESS)由0.028 4降低到0.017 7,预测标准偏差(RMSEP)由0.037 7降低到0.029 7。[结论]应用NIR技术不仅对南疆骏枣叶片含水量的快速、无损检测具有可行性,同时还对其他农作物叶片水分、叶绿素、氮含量光谱预处理检测具有一定的借鉴意义。
[Objective]A rapid and non-destructive detection model of leaf moisture content in Jun jujube of Southern Xinjiang was established.[Method]73 pieces of intact and undamaged Jun jujube leaves were selected,and NIR was used to detect the important index of water content of Jun jujube leaves. 3 different spectral pretreatment methods were applied to establish PLS detection model for water content of Jun jujube leaf.[Result]The best combination in the PLS model predicting the water content of Jun jujube leaves was the original spectrum +MSC+PLS,the correlation coefficient( R) was rised from 0. 673 1 to 0. 874 6,the prediction accuracy( Precision) was rised from 0. 950 7 to 0.957 8,and the predicted residual square sum( PRESS) was reduced from 0.028 4 to 0.017 7,and the prediction standard deviation( RMSEP) was reduced from 0.037 7 to 0.029 7.[Conclusion]The application of NIR technology was not only feasible for the rapid and non-destructive testing of water content in the leaves of Southern Xinjiang jujube,but also provided certain reference significance for the spectral pretreatment and detection of water,chlorophyll and nitrogen content in other crops.
[Objective]A rapid and non-destructive detection model of leaf moisture content in Jun jujube of Southern Xinjiang was established.[Method]73 pieces of intact and undamaged Jun jujube leaves were selected,and NIR was used to detect the important index of water content of Jun jujube leaves. 3 different spectral pretreatment methods were applied to establish PLS detection model for water content of Jun jujube leaf.[Result]The best combination in the PLS model predicting the water content of Jun jujube leaves was the original spectrum +MSC+PLS,the correlation coefficient( R) was rised from 0. 673 1 to 0. 874 6,the prediction accuracy( Precision) was rised from 0. 950 7 to 0.957 8,and the predicted residual square sum( PRESS) was reduced from 0.028 4 to 0.017 7,and the prediction standard deviation( RMSEP) was reduced from 0.037 7 to 0.029 7.[Conclusion]The application of NIR technology was not only feasible for the rapid and non-destructive testing of water content in the leaves of Southern Xinjiang jujube,but also provided certain reference significance for the spectral pretreatment and detection of water,chlorophyll and nitrogen content in other crops.
引文
[1]李伟伟.基于近红外高光谱图像技术的南疆红枣内部品质的研究[D].阿拉尔:塔里木大学,2017.
[2]彭云发.近红外光谱技术在南疆红枣品质快速无损检测中的应用研究[D].阿拉尔:塔里木大学,2015.
[3]张蕾蕾.苹果幼树叶片叶绿素与水分含量的高光谱估测研究[D].泰安:山东农业大学,2013.
[4]吉海彦,马淏,梁雪.近红外光谱无损检测农作物叶片水分含量的研究进展及发展趋势[J].仪器仪表学报,2009,30(6):497-499.
[5]梁爽.苹果树叶片氮素、叶绿素及水分含量的高光谱估测[D].泰安:山东农业大学,2013.
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[9]刘建学.实用近红外光谱分析技术[M].北京:科学出版社,2008.
[10]李民赞.光谱分析技术及其应用[M].北京:科学出版社,2008.
[11]陆婉珍.现代近红外光谱分析技术[M].2版.北京:中国石化出版社,2007.
[12]陆婉珍.近红外光谱仪器[M].北京:化学工业出版社,2010.
[13]徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2):134-142.
[14]张瑶,郑立华,李民赞,等.基于光谱特征分析的苹果树叶片营养素预测模型构建[J].农业工程学报,2013,29(8):171-178.
[15]DELALIEUX S,SOMERS B,VERSTRAETEN W W,et al.Hyperspectral indices to diagnose leaf biotic stress of apple plants,considering leaf phenology[J].International journal of remote sensing,2009,30(8):1887-1912.
[16]李民赞,郑立华,安晓飞,等.土壤成分与特性参数光谱快速检测方法及传感技术[J].农业机械学报,2013,44(3):73-87.
[17]田永超,曹卫星,姜东,等.不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系[J].植物生态学报,2005,29(2):318-323.
[18]董晶晶,牛铮,沈艳,等.利用反射光谱信息提取叶片水分含量的方法比较[J].江西农业大学学报,2006,28(4):587-592.
[19]吉海彦,王鹏新,严泰来.冬小麦活体叶片叶绿素和水分含量与反射光谱的模型建立[J].光谱学与光谱分析,2007,27(3):514-516.
[20]贾灿潮,卢慧娟,林丹,等.近红外光谱技术快速测定何首乌中水分的含量[J].医药导报,2015,34(12):1633-1636.
[21]洪添胜,乔军,NGADI M O,等.基于高光谱图像技术的雪花梨品质无损检测[J].农业工程学报,2007,23(2):151-155.
[22]张晓东,毛罕平,周莹,等.基于高光谱成像技术的生菜叶片水分检测研究[J].安徽农业科学,2011,39(33):20329-20331.
[2]彭云发.近红外光谱技术在南疆红枣品质快速无损检测中的应用研究[D].阿拉尔:塔里木大学,2015.
[3]张蕾蕾.苹果幼树叶片叶绿素与水分含量的高光谱估测研究[D].泰安:山东农业大学,2013.
[4]吉海彦,马淏,梁雪.近红外光谱无损检测农作物叶片水分含量的研究进展及发展趋势[J].仪器仪表学报,2009,30(6):497-499.
[5]梁爽.苹果树叶片氮素、叶绿素及水分含量的高光谱估测[D].泰安:山东农业大学,2013.
[6]褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011.
[7]陆婉珍,袁洪福,褚小立.近红外光谱仪器比较[M].北京:化学工业出版社,2010.
[8]PEDRESCHI F,SEGTNAN V H,KNUTSEN S H.On-line monitoring of fat,dry matter and acrylamide contents in potato chips using near infrared interactance and visual reflectance imaging[J].Food chemistry,2010,121(2):616-620.
[9]刘建学.实用近红外光谱分析技术[M].北京:科学出版社,2008.
[10]李民赞.光谱分析技术及其应用[M].北京:科学出版社,2008.
[11]陆婉珍.现代近红外光谱分析技术[M].2版.北京:中国石化出版社,2007.
[12]陆婉珍.近红外光谱仪器[M].北京:化学工业出版社,2010.
[13]徐广通,袁洪福,陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析,2000,20(2):134-142.
[14]张瑶,郑立华,李民赞,等.基于光谱特征分析的苹果树叶片营养素预测模型构建[J].农业工程学报,2013,29(8):171-178.
[15]DELALIEUX S,SOMERS B,VERSTRAETEN W W,et al.Hyperspectral indices to diagnose leaf biotic stress of apple plants,considering leaf phenology[J].International journal of remote sensing,2009,30(8):1887-1912.
[16]李民赞,郑立华,安晓飞,等.土壤成分与特性参数光谱快速检测方法及传感技术[J].农业机械学报,2013,44(3):73-87.
[17]田永超,曹卫星,姜东,等.不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系[J].植物生态学报,2005,29(2):318-323.
[18]董晶晶,牛铮,沈艳,等.利用反射光谱信息提取叶片水分含量的方法比较[J].江西农业大学学报,2006,28(4):587-592.
[19]吉海彦,王鹏新,严泰来.冬小麦活体叶片叶绿素和水分含量与反射光谱的模型建立[J].光谱学与光谱分析,2007,27(3):514-516.
[20]贾灿潮,卢慧娟,林丹,等.近红外光谱技术快速测定何首乌中水分的含量[J].医药导报,2015,34(12):1633-1636.
[21]洪添胜,乔军,NGADI M O,等.基于高光谱图像技术的雪花梨品质无损检测[J].农业工程学报,2007,23(2):151-155.
[22]张晓东,毛罕平,周莹,等.基于高光谱成像技术的生菜叶片水分检测研究[J].安徽农业科学,2011,39(33):20329-20331.