稻谷品种和品质的光谱快速无损检测研究
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摘要
水稻是世界上最重要的粮食作物之一,也是我国最重要的粮食作物之一。水稻的品种共有五万多种。稻谷品种、年份鉴别和内部品质检测一直是农业生产、作物育种和种子检验上的重要问题。随着社会经济的发展,人们越来越青睐于质优价高的优质稻谷,随之而来的是制售假冒伪劣种子等现象的日益增多,每年造成的经济损失是巨大的。因此,稻谷品种、年份的鉴别和内部品质的检测问题日益受到种子质检部门、水稻育种研究以及粮食企业等单位的重视。长期以来水稻品种的鉴别和稻谷内部品质的检测主要由人工结合化学方法来完成,操作过程繁琐,工作量大,所需时间长,检测效率和检测结果的一致性都比较差。在稻谷品种的鉴别方面,目前常用的方法有形态法、化学法、幼苗法、田间小区种植法、电泳法等,近年来国内外学者采用计算机图像技术进行稻谷品种鉴别也取得了很大进展。在稻谷内部品质成分的检测方面,生产实际中主要还是采用碘比色法、凯氏定氮法等一些标准化学测量方法。建立一套简便、快捷、高效、经济、准确的稻谷检测技术体系成为当前的一个迫切需要,而红外光谱技术的发展为此提供了可能。
本文针对上述目标,以稻谷为研究对象,采用红外光谱技术结合化学计量学方法和数据挖掘技术,对稻谷进行了品种和不同年份的鉴别,以及辐照剂量、直链淀粉和蛋白质含量的检测,主要研究内容和结果如下:
(1)使用Field Spec Handheld光谱仪对五个水稻品种的150个样本进行可见/近红外光谱测定,采用小波变换结合主成分分析和人工神经网络的组合化学计量学方法对对获得的光谱特征进行分析,建立了稻谷品种的识别模型,对五个水稻品种进行了定性的聚类和定量的鉴别,正确率达96%。提出的组合算法为复杂体系的光谱非线形建模提供了一种有力的工具。结果表明,应用可见/近红外光谱技术结合化学计量学方法可简单、快速、无损地鉴别稻谷品种,为稻谷的品种的快速无损鉴别提供了一种新的方法。
(2)试验得到了2003-2005年三年的晚粳谷的可见/近红外光谱,经处理后采用独立组分分析提取稻谷样本的敏感波段作为神经网络的输入,建立稻谷年份的BP神经网络鉴别模型,对不同年份的稻谷的识别率达到100%。通过独立组分分析,找到了晚粳谷主要成分对应的敏感波段,其中770nm,970nm对应水分含量,880nm对应脂肪含量,922nm,972nm,996nm对应稻谷中蛋白质的含量。该结果对不同种类的稻谷具有普适性。研究结果表明利用可见/近红外光谱技术结合化学计量学的方法对同年份的稻谷进行快速鉴别是可行的,它为稻谷年份的快速检测提供了一种新方法。
(3)采用偏最小二乘法(PLS)和最小二乘支持向量机(LS-SVM)分别建立辐照谷物剂量及内部品质(直链淀粉和蛋白质)的线性数学模型。用偏最小二乘法(PLS)建立的较优模型所得的辐照谷物剂量测定,直链淀粉和蛋白质含量的预测相关系数分别为0.978,0.911和0.943,预测均方根误差为114.902,0.250和0.102,偏差为-6.032E-03,-6.012E-04和2.150E-05,其中对于辐照剂量的预测和蛋白质含量测定,中红外波段好于近红外波段;对于直链淀粉含量的测定,近红外波段优于中红外波段。用最小二乘支持向量机(LS-SVM)建立的最优模型所得的辐照谷物剂量测定,直链淀粉和蛋白质含量的预测相关系数分别为0.989,0.951和0.982,预测均方根误差为95.763,0.201和0.052,偏差为-3.621E-03,-1.302E-06和-2.105E-07,其中对于辐照剂量的预测和蛋白质含量测定,中红外波段好于近红外波段,对于直链淀粉含量的测定,近红外波段优于中红外。研究结果表明最小二乘支持向量机具有较好的预测准确性和抗干扰性,可以得到较好的预测结果。
Rice is one of the most important crops in the world,also in China,and the total species of rice are more than 50,000.Rice varieties,stored years identification and internal quality testing are the important issues in agricultural production,crop and seed breeding.With the development of social economy,more and more people favor the high-quality rice.But there also exist phenomena like manufacturing and selling fake and shoddy seeds,which bring the enormous economic losses every year. Therefore,seed qu.ality inspection departments,rice breeding and grain enterprises and other units give more attention on rice varieties,year identification and rice internal quality prediction.Presently,the identification of rice varieties and rice internal quality are mainly by the combination of chemical methods,which are with heavy workload,long time,poor efficiency and bad test results.In the identification of rice varieties,the current methods are morphology,chemical method,seedlings, planting field plot,electrophoresis and so on.In recent years,domestic and foreign scholars have also made great progress in computing imaging technology to identify rice varieties.In rice quality prediction,the main methods like iodine colorimetry, Kjeldahl method,and some other standard methods.So,establish a simple,fast, efficient,economic and accurate detection of rice become a pressing need,and the infrared spectra technology provides the possibility for development.
In this paper,according to the above objectives,we used infrared spectroscopy, chemometrics and data mining technology to identify rice variety and different years, and internal quality prediction(starch and protein) after radiation,the main research contents and findings are as follows:
(1) A simple,fast and non-destructive method was put forward for discriminating varieties of paddy,and the method was based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics.Firstly,thousands of spectral data of different varieties were denoised by wavelet transform(WT).Secondly,principal component analysis(PCA) compressed the above data into several variables.The analysis suggested that the first four PCs(principle components) could account for 99.89%of the original spectral information.In order to set up the model for discriminating varieties of paddy,the four diagnostic PCs were applied as inputs of back propagation artificial neural network(BP-ANN),and the values of different paddy varieties were applied as the outputs of BP-ANN.The whole 150 samples were randomly divided into two parts,one of them that consisted of 100 samples was used to model,and the other one contained 50 samples were used to predict.This model had been used to predict the varieties of 50 unknown samples,and the discrimination rate 96%had been obtained.It was proved that the model was very reliable and practicable.In short,this paper could offer a new approach to discriminate varieties of paddy.
(2) A new method for discrimination stored years of rough rice from 2003-2005 based on independent component analysis was developed through using visible/near infrared spectroscopy(Vis/NIRS).First,the vis/NIR loading weight of rough rice with different years were got through using independent component analysis(ICA),set the wavelengths corresponding to the maximal correlation as inputs of artificial neural network(ANN),then build the discrimination model.120 samples(40 with each year) from three years were selected randomly as calibration set;the left 60 samples(20 with each year) were as perdition set.The discrimination rate of 100%was achieved. Synchronously,the sensitive wavelengths corresponding to the main components in rough rice were obtained through ICA.The wavelengths 770nm and 970nm are corresponding to the moisture content,880nm corresponding to the starch,and 922nm, 972nm,996nm to the protein content in rice.It indicated that the result for discrimination years of rough rice was very good based on ICA method,and it offers a new approach to the fast discrimination years of rough rice.
(3) Partial least squares(PLS) analysis and least squares-support vector machine (LS-SVM) were implemented to predict the radiation doses and components of starch and protein in rice treated with different radiation doses(0Gy,250Gy,500Gy,750Gy, 1000Gy,1500Gy,2000Gy,2500Gy,3000Gy) based on sensitive wavelengths(SWs) and chemometrics.In the PLS models,the correlation coefficient(r),root mean square error of prediction(RMSEP) and bias in prediction set were 0.978,114.902, -6.032E-03 for radiation doses detection,0.911,0.250 and-6.012E-04 for starch, 0.943,0.102 and 2.150E-05 for protein,respectively.To radiation doses detection and protein prediction,mid infrared(MIR)(400-4000 cm~(-1)) region was better than near infrared(NIR)(1100-2500nm),to starch prediction,the NIR region was better.In the LS-SVM models,the correlation coefficient(r),root mean square error of prediction (RMSEP) and bias in prediction set were 0.989,95.763,-3.621E-03 for radiation doses detection,0.951,0.201 and-1.302E-06 for starch,0.982,0.052 and -2.105E-07 for protein,respectively.To radiation doses detection and protein prediction,mid infrared(MIR)(400-4000cm~(-1)) region was better than near infrared(NIR) (1100-2500nm),to starch prediction,the NIR region was better.The overall results indicated that IR spectroscopy combined with LS-SVM could be applied as a high precision and fast way for the radiation doses detection and starch and protein prediction in rice after radiation.
本文针对上述目标,以稻谷为研究对象,采用红外光谱技术结合化学计量学方法和数据挖掘技术,对稻谷进行了品种和不同年份的鉴别,以及辐照剂量、直链淀粉和蛋白质含量的检测,主要研究内容和结果如下:
(1)使用Field Spec Handheld光谱仪对五个水稻品种的150个样本进行可见/近红外光谱测定,采用小波变换结合主成分分析和人工神经网络的组合化学计量学方法对对获得的光谱特征进行分析,建立了稻谷品种的识别模型,对五个水稻品种进行了定性的聚类和定量的鉴别,正确率达96%。提出的组合算法为复杂体系的光谱非线形建模提供了一种有力的工具。结果表明,应用可见/近红外光谱技术结合化学计量学方法可简单、快速、无损地鉴别稻谷品种,为稻谷的品种的快速无损鉴别提供了一种新的方法。
(2)试验得到了2003-2005年三年的晚粳谷的可见/近红外光谱,经处理后采用独立组分分析提取稻谷样本的敏感波段作为神经网络的输入,建立稻谷年份的BP神经网络鉴别模型,对不同年份的稻谷的识别率达到100%。通过独立组分分析,找到了晚粳谷主要成分对应的敏感波段,其中770nm,970nm对应水分含量,880nm对应脂肪含量,922nm,972nm,996nm对应稻谷中蛋白质的含量。该结果对不同种类的稻谷具有普适性。研究结果表明利用可见/近红外光谱技术结合化学计量学的方法对同年份的稻谷进行快速鉴别是可行的,它为稻谷年份的快速检测提供了一种新方法。
(3)采用偏最小二乘法(PLS)和最小二乘支持向量机(LS-SVM)分别建立辐照谷物剂量及内部品质(直链淀粉和蛋白质)的线性数学模型。用偏最小二乘法(PLS)建立的较优模型所得的辐照谷物剂量测定,直链淀粉和蛋白质含量的预测相关系数分别为0.978,0.911和0.943,预测均方根误差为114.902,0.250和0.102,偏差为-6.032E-03,-6.012E-04和2.150E-05,其中对于辐照剂量的预测和蛋白质含量测定,中红外波段好于近红外波段;对于直链淀粉含量的测定,近红外波段优于中红外波段。用最小二乘支持向量机(LS-SVM)建立的最优模型所得的辐照谷物剂量测定,直链淀粉和蛋白质含量的预测相关系数分别为0.989,0.951和0.982,预测均方根误差为95.763,0.201和0.052,偏差为-3.621E-03,-1.302E-06和-2.105E-07,其中对于辐照剂量的预测和蛋白质含量测定,中红外波段好于近红外波段,对于直链淀粉含量的测定,近红外波段优于中红外。研究结果表明最小二乘支持向量机具有较好的预测准确性和抗干扰性,可以得到较好的预测结果。
Rice is one of the most important crops in the world,also in China,and the total species of rice are more than 50,000.Rice varieties,stored years identification and internal quality testing are the important issues in agricultural production,crop and seed breeding.With the development of social economy,more and more people favor the high-quality rice.But there also exist phenomena like manufacturing and selling fake and shoddy seeds,which bring the enormous economic losses every year. Therefore,seed qu.ality inspection departments,rice breeding and grain enterprises and other units give more attention on rice varieties,year identification and rice internal quality prediction.Presently,the identification of rice varieties and rice internal quality are mainly by the combination of chemical methods,which are with heavy workload,long time,poor efficiency and bad test results.In the identification of rice varieties,the current methods are morphology,chemical method,seedlings, planting field plot,electrophoresis and so on.In recent years,domestic and foreign scholars have also made great progress in computing imaging technology to identify rice varieties.In rice quality prediction,the main methods like iodine colorimetry, Kjeldahl method,and some other standard methods.So,establish a simple,fast, efficient,economic and accurate detection of rice become a pressing need,and the infrared spectra technology provides the possibility for development.
In this paper,according to the above objectives,we used infrared spectroscopy, chemometrics and data mining technology to identify rice variety and different years, and internal quality prediction(starch and protein) after radiation,the main research contents and findings are as follows:
(1) A simple,fast and non-destructive method was put forward for discriminating varieties of paddy,and the method was based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics.Firstly,thousands of spectral data of different varieties were denoised by wavelet transform(WT).Secondly,principal component analysis(PCA) compressed the above data into several variables.The analysis suggested that the first four PCs(principle components) could account for 99.89%of the original spectral information.In order to set up the model for discriminating varieties of paddy,the four diagnostic PCs were applied as inputs of back propagation artificial neural network(BP-ANN),and the values of different paddy varieties were applied as the outputs of BP-ANN.The whole 150 samples were randomly divided into two parts,one of them that consisted of 100 samples was used to model,and the other one contained 50 samples were used to predict.This model had been used to predict the varieties of 50 unknown samples,and the discrimination rate 96%had been obtained.It was proved that the model was very reliable and practicable.In short,this paper could offer a new approach to discriminate varieties of paddy.
(2) A new method for discrimination stored years of rough rice from 2003-2005 based on independent component analysis was developed through using visible/near infrared spectroscopy(Vis/NIRS).First,the vis/NIR loading weight of rough rice with different years were got through using independent component analysis(ICA),set the wavelengths corresponding to the maximal correlation as inputs of artificial neural network(ANN),then build the discrimination model.120 samples(40 with each year) from three years were selected randomly as calibration set;the left 60 samples(20 with each year) were as perdition set.The discrimination rate of 100%was achieved. Synchronously,the sensitive wavelengths corresponding to the main components in rough rice were obtained through ICA.The wavelengths 770nm and 970nm are corresponding to the moisture content,880nm corresponding to the starch,and 922nm, 972nm,996nm to the protein content in rice.It indicated that the result for discrimination years of rough rice was very good based on ICA method,and it offers a new approach to the fast discrimination years of rough rice.
(3) Partial least squares(PLS) analysis and least squares-support vector machine (LS-SVM) were implemented to predict the radiation doses and components of starch and protein in rice treated with different radiation doses(0Gy,250Gy,500Gy,750Gy, 1000Gy,1500Gy,2000Gy,2500Gy,3000Gy) based on sensitive wavelengths(SWs) and chemometrics.In the PLS models,the correlation coefficient(r),root mean square error of prediction(RMSEP) and bias in prediction set were 0.978,114.902, -6.032E-03 for radiation doses detection,0.911,0.250 and-6.012E-04 for starch, 0.943,0.102 and 2.150E-05 for protein,respectively.To radiation doses detection and protein prediction,mid infrared(MIR)(400-4000 cm~(-1)) region was better than near infrared(NIR)(1100-2500nm),to starch prediction,the NIR region was better.In the LS-SVM models,the correlation coefficient(r),root mean square error of prediction (RMSEP) and bias in prediction set were 0.989,95.763,-3.621E-03 for radiation doses detection,0.951,0.201 and-1.302E-06 for starch,0.982,0.052 and -2.105E-07 for protein,respectively.To radiation doses detection and protein prediction,mid infrared(MIR)(400-4000cm~(-1)) region was better than near infrared(NIR) (1100-2500nm),to starch prediction,the NIR region was better.The overall results indicated that IR spectroscopy combined with LS-SVM could be applied as a high precision and fast way for the radiation doses detection and starch and protein prediction in rice after radiation.
引文
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34.肖昕,谢新华,陈奕等.应用近红外透射光谱法测定水稻种子直链淀粉含量的初步研究.中国农业科学,2004,37(11):1709-1712
35.谢新华,肖昕,刘彦卓等.小批量稻谷种子蛋白质含量的近红外透射光谱分析.湖北农业科学,2004,(2):16-18
36.徐广通,陆婉珍.柴油近红外光谱与性质的相关性分析.石油学报,2001,17(2):91-95
37.徐冠仁.核农学导论.北京原子能出版社,1995,6-7
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39.严衍禄.近红外光谱分析基础与应用.中国轻工业出版社,2005,108
40.杨景田,李建萍,黄庆林.乌克兰敖德萨粮食辐射检疫处理装置.粮食储藏,2002,31(1):45-47
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45.张玲,于健.基于偏最小二乘(PLS)法白酒中乙醇含量的近红外检测.食品工业,2006,(4):27-29
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50. Bra' s L P, Bernardino S A, Lopes J A, et al. Multiblock PLS as an approach to compare and combine NIR and MIR spectra in calibrations of soybean flour. Chemometrics and Intelligent Laboratory Systems, 2005, 75: 91-99
51. Champagne E T, Bett-Garber K L ,et al. Near-infrared reflectance analysis for prediction of cooked rice texture. Cereal Chemistry, 2001, 78(3): 358-362
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63. Kwon Y K, Cho R K. Identification of rice variety using near infrared spectroscopy . Journal of Near Infrared Spectroscope, 1998, 6: 67-73
64. Kim S S, Rhyu M R, kim J M, et al. Authentication of rice using near-infrared reflectance spectroscopy. Cereal Chem. , 2003, 80(3) : 346-349
65. Michio Shibayama, Tsuyoshi Akiyama. Seasonal visible,near-infrared and mid-infrared spectral of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989, (27): 119-127
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68. Reid L M, Woodcock T, CP O_Donnell, et al. Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data. Food Res. Int,2005,38: 1109-1115
69. Sasic S, Ozaki Y. Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein,and lactose in raw milk by partial least-squares regression and band assignment. Anal. Chem. , 2001, 73: 64-71
70. Shao X G, Wang G Q, Wang S F, Su Q D. Extraction of mass spectra and chromatographic profiles from overlapping GC/MS signal with background. Anal. Chem., 2004, 76(17): 5143-5148
71. Stephen R D, Ronald E P, Karl H N. Sensitivity of Near - Infrared Absorption to Moisture Contents Versus Water Activity in Starch and Cellulose. Cereal Chemistry, 2001, 69(1): 107-109
72. Tipparat JP, Lapammtnoppaldam S, Jakmunee J, et al. Detemtinafion of ethanolinliquorby nea-infrared spectrophotometry with flow injection, Talanta, 2001, 53: 1199-1204
73. Urbano-Cuadrado M, DLuquedeCastro M, Perez-Juan P M, et al. Near infrared reflectance spectroscopy and multivariate an alysis in enology:Determination or screening of fifteen parameters in different types of wines. Analca Chimiea Acta, 2004, 527: 81-88
74. Villareal C P, DE La Cruz N M, Hykuabi B 0. Rice amylose Analysis by Near - infrared transmittance spectrose. Cereal Chemistry, 1994, 71(3): 292-296
75. Wagner B,Gutser R, Schmidhalter U. NIR - spectroscopy to estimate soil nitrogen supply. Developments in Plant and Soil Sciences, 2006, 92: 752-753
76. Wang J H, Huang W J, Zhao C J, et al. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. International Journal of Remote Sensing, 2003, 7(4): 276-284
77. Wang L, Lee F S C, Wang X R, et al. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, 95: 529-536
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36.徐广通,陆婉珍.柴油近红外光谱与性质的相关性分析.石油学报,2001,17(2):91-95
37.徐冠仁.核农学导论.北京原子能出版社,1995,6-7
38.颜刚,张庆忠,徐林娟.近红外光谱分析技术在线测定催化汽油的烯烃含量.现代科学仪器,2005,(4):74-76
39.严衍禄.近红外光谱分析基础与应用.中国轻工业出版社,2005,108
40.杨景田,李建萍,黄庆林.乌克兰敖德萨粮食辐射检疫处理装置.粮食储藏,2002,31(1):45-47
41.杨庭栋,冯新泸.近红外光谱法定性分析内燃机油的粘度特性.合成润滑材料,2004,31(1):13-16
42.严莉.现代生物技术在作物品种纯度鉴定上的研究进展.种子,2002,(6):45-46
43.俞惟乐.分析测试技术与仪器,1994,(3):1-9
44.张巧杰,王一鸣,凌云等.稻谷品质检测技术与装置研制.现代科学仪器,2006,(1):128-130
45.张玲,于健.基于偏最小二乘(PLS)法白酒中乙醇含量的近红外检测.食品工业,2006,(4):27-29
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47. American Association of Cereal Chemistry. Approved methods of the American association of cereal chemcal. Ninth Edition, The Association:St Paul, MN, USA, 1995.
48. Barton F E, Windham W R, et al. Optimal genometrics for the development of rice quality spectroscopic chemometric Models. Cereal Chemistry, 1998, 75(3):315-319
49. Ben-Dor E., Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 1995, 56 (2): 364-372
50. Bra' s L P, Bernardino S A, Lopes J A, et al. Multiblock PLS as an approach to compare and combine NIR and MIR spectra in calibrations of soybean flour. Chemometrics and Intelligent Laboratory Systems, 2005, 75: 91-99
51. Champagne E T, Bett-Garber K L ,et al. Near-infrared reflectance analysis for prediction of cooked rice texture. Cereal Chemistry, 2001, 78(3): 358-362
52. Chen J, Wang X Z. A new approach to near-infrared spectral data analysis using independent component analysis. J. Chem. Inf. Comput. Sci., 2001, 41: 992-1001
53. Cozzolino J D, Kwiatkowsk M J, Parker M, et al. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 2004, 513: 73-80
54. David D, Yvette E, Danny C. Chemometrics and Intelligent Laboratory Syatems, 2005(7): 32
55. Delwiche S R, Bean M M, Miller RE, et al. Apparent amylose content of milled rice by near-infrared reflectance spectrophotometry. Cereal Chemistry, 1995, 72(2): 182-187
56. Delwiche S R, Mekenzie K S, Webb B D. Quality characteristics in rice by near - infrared reflectance analysis of whole grain milled samples. Cereal Chemistry, 1996, 73: 257-263
57. Esteban-Diez I, Gonzalez-Saiz J. M Pizarro C. Analytica. Chimica. Acta. , 2004, 514(1): 57
58. Hoyer P O, Hyvarinen A. Independent component analysis applied to feature extraction from colour and stereo images. Network: Computation in Neural Systems, 2000, 11(3): 191-210
59. Hyvarinen A. Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation. Neural Computation, 1999, 11(7): 1739-1768
60. Hyvarinen A, Hoyer P 0. Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neural Computation, 2000, 12(7): 1705-1720
61. Iwamoto M, Suzuki T and Uozumi J. Analysis of protein and amino acid contents in rice flour by near-infrared spectroscopy. Nippon Shokuhin Kogyo Gakkai-Shi, 1986, 33: 846-854
62. Kano Y, McClure W F, Skaggs R W. A near infrared reflectance soil moisture meter. Transactions of the ASAE, 1985, 28(6): 1852-1855
63. Kwon Y K, Cho R K. Identification of rice variety using near infrared spectroscopy . Journal of Near Infrared Spectroscope, 1998, 6: 67-73
64. Kim S S, Rhyu M R, kim J M, et al. Authentication of rice using near-infrared reflectance spectroscopy. Cereal Chem. , 2003, 80(3) : 346-349
65. Michio Shibayama, Tsuyoshi Akiyama. Seasonal visible,near-infrared and mid-infrared spectral of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989, (27): 119-127
66. Osborne B G, Fearn T. Near infrared Spectroscopy in Food Analysis; Longman Scientific and Technical; Essex, U.K., 1986
67. Roychoudhury P, Harvey L M, McNeil B. At-line monitoring of ammonium, glucose, methyl oleate and biomass in a complex antibiotic fermentation process using attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy. Analytica Chimica Acta, 2006, 561: 218-224
68. Reid L M, Woodcock T, CP O_Donnell, et al. Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data. Food Res. Int,2005,38: 1109-1115
69. Sasic S, Ozaki Y. Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein,and lactose in raw milk by partial least-squares regression and band assignment. Anal. Chem. , 2001, 73: 64-71
70. Shao X G, Wang G Q, Wang S F, Su Q D. Extraction of mass spectra and chromatographic profiles from overlapping GC/MS signal with background. Anal. Chem., 2004, 76(17): 5143-5148
71. Stephen R D, Ronald E P, Karl H N. Sensitivity of Near - Infrared Absorption to Moisture Contents Versus Water Activity in Starch and Cellulose. Cereal Chemistry, 2001, 69(1): 107-109
72. Tipparat JP, Lapammtnoppaldam S, Jakmunee J, et al. Detemtinafion of ethanolinliquorby nea-infrared spectrophotometry with flow injection, Talanta, 2001, 53: 1199-1204
73. Urbano-Cuadrado M, DLuquedeCastro M, Perez-Juan P M, et al. Near infrared reflectance spectroscopy and multivariate an alysis in enology:Determination or screening of fifteen parameters in different types of wines. Analca Chimiea Acta, 2004, 527: 81-88
74. Villareal C P, DE La Cruz N M, Hykuabi B 0. Rice amylose Analysis by Near - infrared transmittance spectrose. Cereal Chemistry, 1994, 71(3): 292-296
75. Wagner B,Gutser R, Schmidhalter U. NIR - spectroscopy to estimate soil nitrogen supply. Developments in Plant and Soil Sciences, 2006, 92: 752-753
76. Wang J H, Huang W J, Zhao C J, et al. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. International Journal of Remote Sensing, 2003, 7(4): 276-284
77. Wang L, Lee F S C, Wang X R, et al. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, 95: 529-536