梨可溶性固形物和酸度的可见/近红外光谱静态和在线检测研究
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摘要
我国是水果生产大国,却是水果出口小国。水果商品化处理程度低是导致我国水果国际市场竞争力弱、出口量小的主要原因之一,提高我国的水果商品化处理水平能有效地增强我国水果的国际市场竞争力,扩大我国水果出口量。水果外观和内部品质的快速无损检测是水果商品化处理的重要环节。因而,实现水果外观和内部品质的快速无损检测是非常重要和必要的。
本研究课题以翠冠梨、水晶梨和贡梨为研究对象,综合利用可见/近红外光谱技术、光纤传感技术以及化学计量学方法开展梨可溶性固形物(SSC)和酸度的静态和在线检测研究,采用不同的建模方法并结合不同的光谱预处理方法建立梨可溶性固形物和酸度的静态预测模型和在线预测模型。研究了影响预测模型的各种因素包括光源-检测器角度、水果传输速度以及水果放置方位,并采用不同的特征波长选择方法对光谱波长变量进行选择研究。
本文的主要内容和研究结论如下：
(1)对USB4000微型光纤光谱仪的静态光谱采集参数和在线光谱采集参数进行优化研究。对于翠冠梨SSC检测,USB4000微型光纤光谱仪的最优静态光谱采集参数Boxcar点数和Average次数分别为6和4,USB4000微型光纤光谱仪的最优在线光谱采集参数Boxcar点数为6。
(2)研究了光源-检测器角度、水果传输速度以及水果放置方位对梨SSC预测模型性能的影响。光源-检测器角度对翠冠梨SSC预测模型性能有影响,光源-检测器角度120°优于光源-检测器角度180°。在三个水果传输速度下(0.3 m/s、0.5 m/s、0.7 m/s),随着水果传输速度的增加,翠冠梨SSC的PLS预测模型性能不断下降,但下降幅度不大。水果放置方位对贡梨SSC预测模型性能有影响。水果放置方位HH和HV优于水果放置方位VU和VD,水果放置方位HH与HV以及水果放置方位VU与VD之间则差别不大。
(3)在可见/近红外检测系统AI里,研究了2009年慈溪与横港两果园翠冠梨SSC和pH的静态检测,采用偏最小二乘回归(PLS)、主成分回归(PCR)、逐步多元线性回归(SMLR)以及最小二乘支持向量机(LS-SVM)建模方法结合不同的预处理方法建立单个果园以及两个果园统一的SSC预测模型,采用PLS方法结合不同的预处理方法建立单个果园以及两个果园统一的pH预测模型。2009年慈溪、横港以及两果园翠冠梨SSC的最优静态预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.971、0.203%、0.328%、0.322%和2.59,0.941、0.289%、0.322%、0.355%和2.58,0.946、0.277%、0.354%、0.347%和2.37。2009年慈溪、横港以及两果园翠冠梨pH的最优静态预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.765、0.113、0.112、0.128和1.60,0.885、0.095、0.123、0.117和1.63,0.835、0.105、0.115、0.123和1.65。PLS建模方法优于PCR.SMLR以及LS-SVM建模方法。
(4)在可见/近红外检测系统AI里,研究了2009年慈溪与横港两果园翠冠梨SSC和pH的在线检测,采用PLS、PCR、SMLR以及LS-SVM建模方法结合不同的预处理方法建立单个果园以及两个果园统一的SSC预测模型,采用PLS方法结合不同的预处理方法建立单个果园以及两个果园统一的pH预测模型。2009年慈溪、横港以及两果园翠冠梨SSC的最优在线预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.966、0.222%、0.253%、0.350%和3.36,0.953、0.259%·、0.326%、0.344%和2.55,0.923、0.306%、0.331%、0.376%和2.54。2009年慈溪、横港以及两果园翠冠梨pH的最优在线预测模型的相关系数、RMSEC、RMSEP、RMSECV以及RPD分别为0.814、0.105、0.127、0.123和1.41,0.881、0.098、0.104、0.117和1.92,0.854、0.102、0.120、0.121和1.58。PLS建模方法优于PCR.SMLR以及LS-SVM建模方法。建立了在0.5 m/s水果传输速度下基于可见/近红外光谱技术结合化学计量学方法在线检测不同果园不同采摘期翠冠梨可溶性固形物和酸度的方法体系和预测模型,提高了可溶性固形物预测模型的精度,为在线检测翠冠梨内部品质提供了新方法。
(5)在分级生产线上的可见/近红外检测系统里,研究了贡梨SSC的在线检测,并采用PLS方法结合不同的预处理方法建立SSC预测模型。贡梨SSC的最优在线预测模型的相关系数、RMSEC、RMSEP、RMSECV以及RPD分别为0.945、0.285%、0.290%、0.344%和2.86。
(6)提出了一种光谱特征波长选择的新方法----组合式特征波长选择方法UVE-GA-PLS,并与其他6种常用的特征波长选择方法包括(BiPLS、SiPLS、UVE-PLS、GA-PLS、SPA-MLR以及UVE-SPA-MLR)进行研究比较。对于翠冠梨SSC检测,UVE-GA-PLS为7种特征波长选择方法中的最优方法,仅采用50个波长变量建立的SSC预测模型性能与全波谱728个波长变量建立的PLS模型性能相近。翠冠梨SSC的UVE-GA-PLS模型的相关系数、RMSEC、RMSEP以及RMSECV分别为0.958、0.248%、0.290%和0.292%,RPD值为2.97。
The output of fruit in China is very large, but the volume of exports is a few. Low degree of fruit commercialized treatment is one of the reasons that caused low competitiveness in the international market and few volume of fruits exports in our country. Increasement of fruit commercialized treatment level can enhance the competitiveness of fruits in the international market, and expand the volume of fruit exports. Quick and nondestructive detection of fruit external and internal qualities are the basis of fruit commercialized treatment, so it is important and necessary to detect fruit external and internal qualities quickly and nondestructively.
'Cuiguan'pear,'Shuijing' pear and 'Gong' pear were used as objects in our research project, and the research project was to use the visible/near infrared spectroscopy technique, optical fiber sensing technique and chemometrics method to detect soluble solids content (SSC) and acidity of pears statically and on-line, and use the different calibration methods combined with different pretreatment methods to develop static calibration model and on-line calibration model for SSC and acidity of pears. Some factors including angle of light source-detector, fruit moving speed, and fruit orientation that affected the performance of calibration model were studied, also different variable selection methods were investigated.
Research contents and conclusions were listed as below:
(1) Parameter optimization of static and on-line spectra acquirement for USB4000 miniature spectrometer was investigated. For SSC detection of'Cuiguan'pears, the optimal parameters of Boxcar and Average for static spectra acquirement were 6 and 4, and the optimal parameter of Boxcar for on-line spectra acquirement was 6.
(2) Effect of Some factors including angle of light source-detector, fruit moving speed, and fruit orientation on performance of SSC calibration model of'Cuiguan'pears was investigated. The light source-detector angle of 120°was better than that of 180°for SSC detection of'Cuiguan'pears. The performance of PLS model for SSC was decreased, but not so much at the three fruit moving speeds (0.3 m/s,0.5 m/s,0.7 m/s). The fruit orientations of HH and HV were more suitable than that of VU and VD for SSC detection, and there were little difference between fruit orientations HH and HV, VU and VD.
(3) Static detection of SSC and pH of'Cuiguan'pears from Cixi and Henggang orchards in year of 2009 were investigated in Vis/NIR detection system AI. Calibration methods such as partial least squares (PLS), principal component regression (PCR), stepwise multiple linear regression (SMLR) and least squared support vector machine (LS-SVM) combined with different pretreatment methods were used to develop SSC calibration models of'Cuiguan' pears for single orchard and two orchards, and PLS combined with different pretreatment methods was used to develop pH calibration models of'Cuiguan'pears for single orchard and two orchards. The correlation coefficient (r), RMSEC, RMSEP, RMSECV and RPD of the best static SSC calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.971,0.203,0.328,0.322 and 2.59,0.941,0.289,0.322, 0.355 and 2.58,0.946,0.277,0.354,0.347 and 2.37. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best static pH calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.765,0.113,0.112, 0.128 and 1.60,0.885,0.095,0.123,0.117 and 1.63,0.835,0.105,0.115,0.123 and 1.65. PLS method was better than other methods such as PCR, SMLR and LS-SVM.
(4) On-line detection of SSC and pH of'Cuiguan'pears from Cixi and Henggang orchards in year of 2009 were investigated in Vis/NIR detection system AI. Calibration methods such as PLS, PCR, SMLR and LS-SVM combined with different pretreatment methods were used to develop SSC calibration models of'Cuiguan'pears for single orchard and two orchards, and PLS combined with different pretreatment methods was used to develop pH calibration models of'Cuiguan'pears for single orchard and two orchards. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best on-line SSC calibration model'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.966,0.222,0.253,0.350 and 3.36,0.953,0.259,0.326,0.344 and 2.55, 0.923,0.306,0.331,0.376 and 2.54. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best on-line pH calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.814,0.105,0.127,0.123 and 1.41, 0.881,0.098,0.104,0.117 and 1.92,0.854,0.102,0.120,0.121 and 1.58. PLS method was better than other methods such as PCR, SMLR and LS-SVM. The method system and prediction models of SSC and pH on-line detection of'Cuiguan'pears based on visible/near infrared (Vis/NIR) spectroscopy technique and chemometrics method were established at fruit moving speed of 0.5 m/s.
(5) On the grading machine, on-line detection of SSC of'Gong'pears was investigated in the Vis/NIR detection system AI. PLS method combined with different pretreatment methods was used to develop SSC calibration models. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best SSC on-line calibration model of'Gong'pears were 0.945,0.285,0.290,0.344 and 2.86.
(6) A new variable selection methods UVE-GA-PLS was proposed, and was compared with other variable selection methods including BiPLS, SiPLS, UVE-PLS, GA-PLS, SPA-MLR and UVE-SPA-MLR. For SSC detection, UVE-GA-PLS was the best variable selection method among these methods. The performance of SSC calibration model developed using 50 selected wavelengths was comparative to that developed using full wavelengths. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best SSC calibration models of'Cuiguan'pears developed by UVE-GA-PLS were 0.958,0.248, 0.290,0.292 and 2.97.
本研究课题以翠冠梨、水晶梨和贡梨为研究对象,综合利用可见/近红外光谱技术、光纤传感技术以及化学计量学方法开展梨可溶性固形物(SSC)和酸度的静态和在线检测研究,采用不同的建模方法并结合不同的光谱预处理方法建立梨可溶性固形物和酸度的静态预测模型和在线预测模型。研究了影响预测模型的各种因素包括光源-检测器角度、水果传输速度以及水果放置方位,并采用不同的特征波长选择方法对光谱波长变量进行选择研究。
本文的主要内容和研究结论如下：
(1)对USB4000微型光纤光谱仪的静态光谱采集参数和在线光谱采集参数进行优化研究。对于翠冠梨SSC检测,USB4000微型光纤光谱仪的最优静态光谱采集参数Boxcar点数和Average次数分别为6和4,USB4000微型光纤光谱仪的最优在线光谱采集参数Boxcar点数为6。
(2)研究了光源-检测器角度、水果传输速度以及水果放置方位对梨SSC预测模型性能的影响。光源-检测器角度对翠冠梨SSC预测模型性能有影响,光源-检测器角度120°优于光源-检测器角度180°。在三个水果传输速度下(0.3 m/s、0.5 m/s、0.7 m/s),随着水果传输速度的增加,翠冠梨SSC的PLS预测模型性能不断下降,但下降幅度不大。水果放置方位对贡梨SSC预测模型性能有影响。水果放置方位HH和HV优于水果放置方位VU和VD,水果放置方位HH与HV以及水果放置方位VU与VD之间则差别不大。
(3)在可见/近红外检测系统AI里,研究了2009年慈溪与横港两果园翠冠梨SSC和pH的静态检测,采用偏最小二乘回归(PLS)、主成分回归(PCR)、逐步多元线性回归(SMLR)以及最小二乘支持向量机(LS-SVM)建模方法结合不同的预处理方法建立单个果园以及两个果园统一的SSC预测模型,采用PLS方法结合不同的预处理方法建立单个果园以及两个果园统一的pH预测模型。2009年慈溪、横港以及两果园翠冠梨SSC的最优静态预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.971、0.203%、0.328%、0.322%和2.59,0.941、0.289%、0.322%、0.355%和2.58,0.946、0.277%、0.354%、0.347%和2.37。2009年慈溪、横港以及两果园翠冠梨pH的最优静态预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.765、0.113、0.112、0.128和1.60,0.885、0.095、0.123、0.117和1.63,0.835、0.105、0.115、0.123和1.65。PLS建模方法优于PCR.SMLR以及LS-SVM建模方法。
(4)在可见/近红外检测系统AI里,研究了2009年慈溪与横港两果园翠冠梨SSC和pH的在线检测,采用PLS、PCR、SMLR以及LS-SVM建模方法结合不同的预处理方法建立单个果园以及两个果园统一的SSC预测模型,采用PLS方法结合不同的预处理方法建立单个果园以及两个果园统一的pH预测模型。2009年慈溪、横港以及两果园翠冠梨SSC的最优在线预测模型的相关系数、RMSEC.RMSEP.RMSECV以及RPD分别为0.966、0.222%、0.253%、0.350%和3.36,0.953、0.259%·、0.326%、0.344%和2.55,0.923、0.306%、0.331%、0.376%和2.54。2009年慈溪、横港以及两果园翠冠梨pH的最优在线预测模型的相关系数、RMSEC、RMSEP、RMSECV以及RPD分别为0.814、0.105、0.127、0.123和1.41,0.881、0.098、0.104、0.117和1.92,0.854、0.102、0.120、0.121和1.58。PLS建模方法优于PCR.SMLR以及LS-SVM建模方法。建立了在0.5 m/s水果传输速度下基于可见/近红外光谱技术结合化学计量学方法在线检测不同果园不同采摘期翠冠梨可溶性固形物和酸度的方法体系和预测模型,提高了可溶性固形物预测模型的精度,为在线检测翠冠梨内部品质提供了新方法。
(5)在分级生产线上的可见/近红外检测系统里,研究了贡梨SSC的在线检测,并采用PLS方法结合不同的预处理方法建立SSC预测模型。贡梨SSC的最优在线预测模型的相关系数、RMSEC、RMSEP、RMSECV以及RPD分别为0.945、0.285%、0.290%、0.344%和2.86。
(6)提出了一种光谱特征波长选择的新方法----组合式特征波长选择方法UVE-GA-PLS,并与其他6种常用的特征波长选择方法包括(BiPLS、SiPLS、UVE-PLS、GA-PLS、SPA-MLR以及UVE-SPA-MLR)进行研究比较。对于翠冠梨SSC检测,UVE-GA-PLS为7种特征波长选择方法中的最优方法,仅采用50个波长变量建立的SSC预测模型性能与全波谱728个波长变量建立的PLS模型性能相近。翠冠梨SSC的UVE-GA-PLS模型的相关系数、RMSEC、RMSEP以及RMSECV分别为0.958、0.248%、0.290%和0.292%,RPD值为2.97。
The output of fruit in China is very large, but the volume of exports is a few. Low degree of fruit commercialized treatment is one of the reasons that caused low competitiveness in the international market and few volume of fruits exports in our country. Increasement of fruit commercialized treatment level can enhance the competitiveness of fruits in the international market, and expand the volume of fruit exports. Quick and nondestructive detection of fruit external and internal qualities are the basis of fruit commercialized treatment, so it is important and necessary to detect fruit external and internal qualities quickly and nondestructively.
'Cuiguan'pear,'Shuijing' pear and 'Gong' pear were used as objects in our research project, and the research project was to use the visible/near infrared spectroscopy technique, optical fiber sensing technique and chemometrics method to detect soluble solids content (SSC) and acidity of pears statically and on-line, and use the different calibration methods combined with different pretreatment methods to develop static calibration model and on-line calibration model for SSC and acidity of pears. Some factors including angle of light source-detector, fruit moving speed, and fruit orientation that affected the performance of calibration model were studied, also different variable selection methods were investigated.
Research contents and conclusions were listed as below:
(1) Parameter optimization of static and on-line spectra acquirement for USB4000 miniature spectrometer was investigated. For SSC detection of'Cuiguan'pears, the optimal parameters of Boxcar and Average for static spectra acquirement were 6 and 4, and the optimal parameter of Boxcar for on-line spectra acquirement was 6.
(2) Effect of Some factors including angle of light source-detector, fruit moving speed, and fruit orientation on performance of SSC calibration model of'Cuiguan'pears was investigated. The light source-detector angle of 120°was better than that of 180°for SSC detection of'Cuiguan'pears. The performance of PLS model for SSC was decreased, but not so much at the three fruit moving speeds (0.3 m/s,0.5 m/s,0.7 m/s). The fruit orientations of HH and HV were more suitable than that of VU and VD for SSC detection, and there were little difference between fruit orientations HH and HV, VU and VD.
(3) Static detection of SSC and pH of'Cuiguan'pears from Cixi and Henggang orchards in year of 2009 were investigated in Vis/NIR detection system AI. Calibration methods such as partial least squares (PLS), principal component regression (PCR), stepwise multiple linear regression (SMLR) and least squared support vector machine (LS-SVM) combined with different pretreatment methods were used to develop SSC calibration models of'Cuiguan' pears for single orchard and two orchards, and PLS combined with different pretreatment methods was used to develop pH calibration models of'Cuiguan'pears for single orchard and two orchards. The correlation coefficient (r), RMSEC, RMSEP, RMSECV and RPD of the best static SSC calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.971,0.203,0.328,0.322 and 2.59,0.941,0.289,0.322, 0.355 and 2.58,0.946,0.277,0.354,0.347 and 2.37. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best static pH calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.765,0.113,0.112, 0.128 and 1.60,0.885,0.095,0.123,0.117 and 1.63,0.835,0.105,0.115,0.123 and 1.65. PLS method was better than other methods such as PCR, SMLR and LS-SVM.
(4) On-line detection of SSC and pH of'Cuiguan'pears from Cixi and Henggang orchards in year of 2009 were investigated in Vis/NIR detection system AI. Calibration methods such as PLS, PCR, SMLR and LS-SVM combined with different pretreatment methods were used to develop SSC calibration models of'Cuiguan'pears for single orchard and two orchards, and PLS combined with different pretreatment methods was used to develop pH calibration models of'Cuiguan'pears for single orchard and two orchards. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best on-line SSC calibration model'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.966,0.222,0.253,0.350 and 3.36,0.953,0.259,0.326,0.344 and 2.55, 0.923,0.306,0.331,0.376 and 2.54. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best on-line pH calibration model of'Cuiguan'pears for Cixi orchard, Henggang orchard and two orchards in year of 2009 were 0.814,0.105,0.127,0.123 and 1.41, 0.881,0.098,0.104,0.117 and 1.92,0.854,0.102,0.120,0.121 and 1.58. PLS method was better than other methods such as PCR, SMLR and LS-SVM. The method system and prediction models of SSC and pH on-line detection of'Cuiguan'pears based on visible/near infrared (Vis/NIR) spectroscopy technique and chemometrics method were established at fruit moving speed of 0.5 m/s.
(5) On the grading machine, on-line detection of SSC of'Gong'pears was investigated in the Vis/NIR detection system AI. PLS method combined with different pretreatment methods was used to develop SSC calibration models. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best SSC on-line calibration model of'Gong'pears were 0.945,0.285,0.290,0.344 and 2.86.
(6) A new variable selection methods UVE-GA-PLS was proposed, and was compared with other variable selection methods including BiPLS, SiPLS, UVE-PLS, GA-PLS, SPA-MLR and UVE-SPA-MLR. For SSC detection, UVE-GA-PLS was the best variable selection method among these methods. The performance of SSC calibration model developed using 50 selected wavelengths was comparative to that developed using full wavelengths. The correlation coefficient, RMSEC, RMSEP, RMSECV and RPD of the best SSC calibration models of'Cuiguan'pears developed by UVE-GA-PLS were 0.958,0.248, 0.290,0.292 and 2.97.
引文
[1]辜青青,罗来春,徐回林.我国果业生产现状及发展趋势.现代园艺,2009,8：20-21.
[2]国家统计数据库,http://219.235.129.58/welcome.do.
[3]周蓓,徐超.浅议我国水果出口贸易.金卡工程·经济与法,2009,8：249.
[4]朱佳满,刘更森,王强.当前我国果品出口现状、存在问题与对策.山西果树,2003,5：27-28.
[5]张吉国,胡继连,张兆新.我国果品产业的发展现状与对策.山东农业大学学报(社会科学版),2002,4(3)：31-35.
[6]Norris, K. H. Reports on design and development of a new moisture meter. Agricultural Engineering, 1964,45(7):370-372.
[7]Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I,& Lammertyn, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:A review. Postharvest Biology and Technology,2007,46:99-11.
[8]Sun, T., Huang, K., Xu, H. R.,& Ying, Y. B. Research advances in nondestructive determination of internal quality in watermelon/melon:A review. Journal of Food Engineering,2010,100(4): 569-577.
[9]Karoui, R.,& De Baerdemaeker, J. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry,2007, 102(3):621-640.
[10]王田子,郑丽敏,田立军,吴平,朱虹,任发政.近红外在乳及乳制品质量检测中的研究进展.光谱学与光谱分析,2010,30(12)：3208-3212.
[11]Osborne, B. G. Applications of near infrared spectroscopy in quality screening of early-generation material in cereal breeding programmes. Journal of Near Infrared Spectroscopy,2006,14(2): 93-101.
[12]周光华,朱大洲,王成.近红外光谱在粮食作物检测中的应用进展.安徽农业科学,2010,38(28)：15475-15478.
[13]Prieto, N., Roehe, R., Lavin, P., Batten, G.,& Andres, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality:A review. Meat Science,2009,83(2): 175-186.
[14]Bochereau, L., Bourgine, P.,& Palagos, B. A method for prediction by combining data analysis and neural networks:Application to prediction of apple quality using near infra-red spectraag. Journal of Agricultural Engineering Research,1992,51(3):207-216.
[15]Cho, R. K., Sohn, M. R.,& Kwon, Y. K. New observation of nondestructive evaluation for sweetness in apple fruit using near infrared spectroscopy. Journal of near Infrared Spectroscopy, 1998,6:A75-A78.
[16]Ventura, M., de Jager, A., de Putter, H.,& Roelofs, F. P. M. M. Non-destructive determination of soluble solids in apple fruit by near infrared spectroscopy (NIRS). Postharvest Biology and Technology,1998,14(1):21-27.
[17]Lammertyn, J., Nicolai, B., Ooms, K., De Smedt, V,& De Baerdemaeker, J. Non-destructive measurement of acidity, soluble solids, and firmness of Jonagold apples using NIR-spectroscopy. Transactions of the ASAE,1998,41(4):1089-1094.
[18]Lammertyn, J., Peirs, A., De Baerdemaeker, J.,& Nicolai, B. Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology,2000,18(2):121-132.
[19]Lu, R. F., Guyer, D. E.,& Beaudry, R. M. Determination of firmness and sugar content of apples using near-infrared diffuse reflectance. Journal of Texture Studies,2000,31(6):615-630.
[20]Lu, R.,& Ariana, D. A near-infrared sensing technique for measuring internal quality of apple fruit. Applied Engineering in Agriculture,2002,18(5):585-590.
[21]Temma, T., Hanamatsu, K.,& Shinoki, F. Development of a portable near infrared sugar-measuring instrument. Journal of near Infrared Spectroscopy,2002,10(1):77-83.
[22]McGlone, V. A., Jordan, R. B.,& Martinsen, P. J. Vis/NIR estimation at harvest of pre-and post-storage quality indices for 'Royal Gala' apple. Postharvest Biology and Technology,2002,25(2): 135-144.
[23]McGlone, V. A., Jordan, R. B., Seelye, R.,& Clark, C. J. Dry-matter-a better predictor of the post-storage soluble solids in apples? Postharvest Biology and Technology,2003,28(3):431-435.
[24]McGlone, V. A.,& Martinsen, P. J. Transmission measurements on intact apples moving at high speed. Journal of near Infrared Spectroscopy,2004,12(1):37-43.
[25]Peirs, A., Scheerlinck, N., Touchant, K.,& Nicolai, B. M. Comparison of Fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosystems Engineering,2002,81(3):305-311.
[26]Peirs, A., Scheerlinck, N.,& Nicolai, B. M. Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biology and Technology,2003,30(3): 233-248.
[27]Peirs, A., Tirry, J., Verlinden, B., Darius, P.,& Nicolai, B. M. Effect of biological variability on the robustness of NIR models for soluble solids content of apples. Postharvest Biology and Technology, 2003,28(2):269-280.
[28]Park, B., Abbott, J. A., Lee, K. J., Choi, C. H.,& Choi, K. H. Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and Gala apples. Transactions of the ASAE,2003,46(6):1721-1731.
[29]Quilitzsch, R.,& Hobert, E. Fast determination of apple quality by spectroscopy in the near infrared. Journal of Applied Botany-Angewandte Botanik,2003,77(5-6):172-176.
[30]Roger, J. M., Chauchard, F.,& Bellon-Maurel, V. EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemometrics and Intelligent Laboratory Systems,2003,66(2):191-204.
[31]Sanchez, N. H., Lurol, S., Roger, J. M.,& Bellon-Maurel, W. Robustness of models based on NIR spectra for sugar content prediction in apples. Journal of near Infrared Spectroscopy,2003,11(2): 97-107.
[32]Walsh, K. B., Golic, M.,& Greensill, C. V. Sorting of fruit using near infrared spectroscopy: application to a range of fruit and vegetables for soluble solids and dry matter content. Journal of near Infrared Spectroscopy,2004,12(3):141-148.
[33]Zude, M., Herold, B., Roger, J. M., Bellon-Maurel, V.,& Landahl, S. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering,2006,77(2):254-260.
[34]Alamar, M. C., Bobelyn, E., Lammertyn, J., Nicolai, B. M.,& Molto, E. Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple. Postharvest Biology and Technology,2007,45(1):38-45.
[35]Bessho, H., Kudo, K., Omori, J., Inomata, Y., Wada, M., Masuda, T., Nakamoto, Y., Fujisawa, H.,& Suzuki, Y. A portable non-destructive quality meter for understanding fruit soluble solids in apple canopies. Acta Horticulturae,2007,732:593-597.
[36]Nicolai, B. M., Theron, K. I.,& Lammertyn, J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemometrics and Intelligent Laboratory Systems, 2007,85(2):243-252.
[37]Qing, Z., Ji, B.,& Zude, M. Wavelength selection for predicting physicochemical properties of apple fruit based on near-infrared spectroscopy. Journal of Food Quality,2007,30(4):511-526.
[38]Paz, P., Sanchez, M. T., Perez-Marin, D., Guerrero, J. E.,& Garrido-Varo, A. Evaluating NIR instruments for quantitative and qualitative assessment of intact apple quality. Journal of the Science of Food and Agriculture,2009,89(5):781-790.
[39]Jha, S. N.,& Ruchi, G. Non-destructive prediction of quality of intact apple using near infrared spectroscopy. Journal of Food Science and Technology-Mysore,2010,47(2):207-213.
[40]Bobelyn, E., Serban, A. S., Nicu, M., Lammertyn, J., Nicolai, B. M.,& Saeys, W. Postharvest quality of apple predicted by NIR-spectroscopy:Study of the effect of biological variability on spectra and model performance. Postharvest Biology and Technology,2010,55(3):133-143.
[41]Kawano, S., Fujiwara, T.,& Iwamoto, M. Nondestructive determination of sugar content in satsuma mandarine using near-infrared (NIR) transmittance. Journal of the Japanese Society for Horticultural Science,1993,62(2):465-470.
[42]Greensill, C. V.,& Walsh, K. B. Calibration transfer between miniature photodiode array-based spectrometers in the near infrared assessment of mandarin soluble solids content. Journal of near Infrared Spectroscopy,2002,10(1):27-35.
[43]McGlone, V. A., Fraser, D. G., Jordan, R. B.,& Kunnemeyer, R. Internal quality assessment of mandarin fruit by Vis/NIR spectroscopy. Journal of near Infrared Spectroscopy,2003,11(5): 323-332.
[44]Lee, K., Kim, G., Kang, S., Son, J., Choi, D.,& Choi, K. Measurement of sugar contents in citrus using near infrared transmittance. Key Engineering Materials,2004,270-273:1014-1019.
[45]Miller, W. M.,& Zude-Sasse, M. NIR-based sensing to measure soluble solids content of Florida citrus. Applied Engineering in Agriculture,2004,20(3):321-327.
[46]Guthrie, J. A., Walsh, K. B., Reid, D.J.,Liebenberg, C. J. Assessment of internal quality attributes of mandarin fruit.1. NIR calibration model development. Australian Journal of Agricultural Research,2005,56(4):405-416.
[47]Guthrie, J. A., Reid, D. J.,& Walsh, K. B. Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness. Australian Journal of Agricultural Research,2005,56(4): 417-426.
[48]Sinelli, N., Spinardi, A., Di Egidio, V., Mignani, I.,& Casiraghi, E. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Postharvest Biology and Technology,2008,50(1):31-36.
[49]Cayuela, J. A. Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biology and Technology,2008,47(1):75-80.
[50]Cayuela, J. A.,& Weiland, C. Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biology and Technology,2010,58(2):113-120.
[51]Clark, C. J., McGlone, V. A., Requejo, C., White, A.,& Woolf, A. B. Dry matter determination in 'Hass' avocado by NIR spectroscopy. Postharvest Biology and Technology,2003,29(3):301-308.
[52]Nicolai, B. M., Verlinden, B. E., Desmet, M., Saevels, S., Saeys, W., Theron, K., Cubeddu, R., Pifferi, A.,& Torricelli, A. Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biology and Technology,2008,47(1): 68-74.
[53]Martinsen, P.,& Schaare, P. Measuring soluble solids distribution in kiwifruit using near-infrared imaging spectroscopy. Postharvest Biology and Technology,1998,14(3):271-281.
[54]McGlone, V. A.,& Kawano, S. Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology,1998,13(2):131-141.
[55]McGlone, V. A., Jordan, R. B., Seelye, R.,& Martinsen, P. J. Comparing density and NIR methods for measurement of Kiwifruit dry matter and soluble solids content. Postharvest Biology and Technology,2002,26(2):191-198.
[56]McGlone, V. A., Clark, C. J.,& Jordan, R. B. Comparing density and VNIR methods for predicting quality parameters of yellow-fleshed kiwifruit (Actinidia chinensis). Postharvest Biology and Technology,2007,46(1):1-9.
[57]Osborne, S. D.,& Kunnemeyer, R. A low-cost system for the grading of kiwifruit. Journal of near Infrared Spectroscopy,1999,7(1):9-15.
[58]Schaare, P. N.,& Fraser, D. G.Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology,2000,20(2):175-184.
[59]Clark, C. J., McGlone, V. A., De Silva, H. N., Manning, M. A., Burdon, J.,& Mowat, A. D. Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest. Postharvest Biology and Technology,2004,32(2):147-158.
[60]Moghimi, A., Aghkhani, M. H., Sazgarnia, A.,& Sarmad, M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering,2010,106(3):295-302.
[61]Guthrie, J.,& Walsh, K. Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy. Australian Journal of Experimental Agriculture,1997,37(2):253-263.
[62]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Improvement of PLS calibration for Brix value and dry matter of mango using information from MLR calibration. Journal of near Infrared Spectroscopy, 2001,9(4):287-295.
[63]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Performance of a portable near infrared instrument for Brix value determination of intact mango fruit. Journal of near Infrared Spectroscopy,2003, 11(3):175-181.
[64]Saranwong, S., Sornsrivichai, J.,& Kawano, S. On-tree evaluation of harvesting quality of mango fruit using a hand-held NIR instrument. Journal of near Infrared Spectroscopy,2003,11(4):283-293.
[65]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biology and Technology,2004,31(2):137-145.
[66]Saranwong, S., Kawano, S.,& Sornsrivichai, J. Advance technique to predict eating quality of ripe-mango at unripe-stage using near infrared spectroscopy. Acta Horticulturae,2005,682: 1427-1433.
[67]Subedi, P. P., Walsh, K. B.,& Owens, G. Prediction of mango eating quality at harvest using short-wave near infrared spectrometry. Postharvest Biology and Technology,2007,43(3):326-334.
[68]Delwiche, S. R., Mekwatanakarn, W.,& Wang, C. Y. Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy. Horttechnology,2008,18(3):410-416.
[69]Dull, G. G, Leffler, R. G.,& Birth, G. S. Near-infrared spectrophotometry for measurement of soluble solids in intact honeydew melons. HortScience,1990,25:1132.
[70]Dull, G.G, Leffler, R. G., Birth, G. S.,& Smittle, D. A. Instrument for nondestructive measurement of soluble solids in honeydew melons. Transactions of the ASAE,1992,35(2),735-737.
[71]Guthrie, J., Wedding, B.,& Walsh, K. Robustness of NIR calibrations for soluble solids in intact melon and pineapple. Journal of near Infrared Spectroscopy,1998,6(1):259-265.
[72]Guthrie, J. A., Liebenberg, C. J.,& Walsh, K. B. NIR model development and robustness in prediction of melon fruit total soluble solids. Australian Journal of Agricultural Research,2006, 57(4):411-418.
[73]Slaughter, D. C., Cavaletto, C. G., Gautz, L. D.,& Paull, R. E. Non-destructive determination of soluble solids in papayas using near infrared spectroscopy. Journal of near Infrared Spectroscopy, 1999,7(4):223-228.
[74]Greensill, C. V.,& Newman, D. S. An investigation into the determination of the maturity of pawpaws (Carica papaya) from NIR transmission spectra. Journal of near Infrared Spectroscopy, 1999,7(2):109-116.
[75]Greensill, C. V.,& Walsh, K. B. A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit. Measurement Science & Technology,2000, 11(12):1674-1684.
[76]Greensill, C. V., Wolfs, P. J., Spiegelman, C. H.,& Walsh, K. B. Calibration transfer between PDA-based NIR spectrometers in the NIR assessment of melon soluble solids content. Applied Spectroscopy,2001,55(5):647-653.
[77]Walsh, K. B., Guthrie, J. A.,& Burney, J. W. Application of commercially available, low-cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit. Australian Journal of Plant Physiology,2000,27(12):1175-1186.
[78]Ito, H., Morimoto, S., Yamauchi, R., Ippoushi, K., Azuma, K.,& Hugashio, H. Potential of near infrared spectroscopy for nondestructive estimation of soluble solids in watermelons. Acta Horticulturae,2002,588,353-356.
[79]Tsuta, M., Sugiyama, J.,& Sagara, Y. Near-infrared imaging spectroscopy based on the sugar absorption band for melons. Journal of Agricultural and Food Chemistry,2002,50(1):48-52.
[80]Long, R. L. Improving fruit soluble solids content in melon (Cucumis melo L.) (reticulatus group) in the Australian production system [Doctoral dissertation]. Australia, Central Queensland University, 2005,72-96,121-135.
[81]Long, R. L.,& Walsh, K. B. Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy. Australian Journal of Agricultural Research,2006,57(4):403-410.
[82]Abebe, A. T. Total sugar and maturity evaluation of intact watermelon using near infrared spectroscopy. Journal of Near Infrared Spectroscopy,2006,14(1),67-70.
[83]Flores, K., Sanchez, M. T., Perez-Marin, D. C., Lopez, M. D., Guerrero, J. E., Garrido-Varo, A. Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy. Journal of Near Infrared Spectroscopy,2008,16(2):91-98.
[84]Slaughter, D. C. Nondestructive determination of internal quality in peaches and nectarines. Transactions of the ASAE,1995,38(2):617-623.
[85]Slaughter, D. C., Thompson, J. F.,& Tan, E. S. Nondestructive determination of total and soluble solids in fresh prune using near infrared spectroscopy. Postharvest Biology and Technology,2003, 28(3):437-444.
[86]Peiris, K. H. S., Dull, G. G., Leffler, R. G.,& Kays, S. J. Near-infrared spectrometric method for nondestructive determination of soluble solids content of peaches. Journal of the American Society for Horticultural Science,1998,123(5):898-905.
[87]Guthrie, J.,& Walsh, K. Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy. Australian Journal of Experimental Agriculture,1999,39(1):73-80.
[88]Carlini, P., Massantini, R.,& Mencarelli, F. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. Journal of Agricultural and Food Chemistry, 2000,48(11):5236-5242.
[89]Tarkosova, J.,& Copikova, J. Determination of carbohydrate content in bananas during ripening and storage by near infrared spectroscopy. Journal of near Infrared Spectroscopy,2000,8(1):21-26.
[90]Lu, R. Predicting firmness and sugar content of sweet cherries using near-infrared diffuse reflectance spectroscopy. Transactions of the ASAE,2001,44(5):1265-1271.
[91]Herrera, J., Guesalaga, A.,& Agosin, E. Shortwave-near infrared spectroscopy for non-destructive determination of maturity of wine grapes. Measurement Science & Technology,2003,14(5): 689-697.
[92]Chauchard, F.,Cogdill, R., Roussel, S., Roger, J. M.,& Bellon-Maurel, V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy:development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems,2004,71(2): 141-150.
[93]Cozzolino, D., Esler, M. B., Dambergs, R. G., Cynkar, W. U., Boehm, D. R., Francis, I. L.,& Gishen, M. Prediction of colour and pH in grapes using a diode array spectrophotometer (400-1100 nm). Journal of near Infrared Spectroscopy,2004,12(2):105-111.
[94]Manley, M., Joubert, E., Myburgh, L., Lotz, E.,& Kidd, M. Prediction of soluble solids content and post-storage internal quality of Bulida apricots using near infrared spectroscopy. Journal of near Infrared Spectroscopy,2007,15(3):179-188.
[95]Paz, P., Sanchez, M. T., Perez-Marin, D., Guerrero, J. E.,& Garrido-Varo, A. Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry,2008,56(8):2565-2570.
[96]Reita, G., Peano, C., Saranwong, S.,& Kawano, S. An evaluating technique for variety compatibility of fruit applied to a near infrared Brix calibration system:a case study using Brix calibration for nectarines. Journal of near Infrared Spectroscopy,2008,16(2):83-89.
[97]Bureau, S., Ruiz, D., Reich, M., Gouble, B., Bertrand, D., Audergon, J. M.,& Renard, C. M. G C. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chemistry,2009,113(4):1323-1328.
[98]Camps, C.,& Christen, D. On-tree follow-up of apricot fruit development using a hand-held NIR instrument. Journal of Food Agriculture & Environment,2009,7(2):394-400.
[99]Camps, C.,& Christen, D. Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT-Food Science and Technology,2009,42(6):1125-1131.
[100]Fernandez-Novales, J., Lopez, M. I., Sanchez, M. T., Garcia-Mesa, J. A.,& Gonzalez-Caballero, V. Assessment of quality parameters in grapes during ripening using a miniature fiber-optic near-infrared spectrometer. International Journal of Food Sciences and Nutrition,2009,60(S7): 265-277.
[101]Nishizawa, T., Mori, Y., Fukushima, S., Natsuga, M.,& Maruyama, Y. Non-destructive analysis of soluble sugar components in strawberry fruits using near-infrared spectroscopy. Journal of the Japanese Society for Food Science and Technology-Nippon Shokuhin Kagaku Kogaku Kaishi,2009, 56(4):229-235.
[102]Perez-Marin, D., Sanchez, M. T., Paz, P., Soriano, M. A., Guerrero, J. E.,& Garrido-Varo, A. Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biology and Technology,2009,52(2):180-188.
[103]Perez-Marin, D., Paz, P., Guerrero, J. E., Garrido-Varo, A.,& Sanchez, M. T. Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums. Journal of Food Engineering,2010,93(3):294-302.
[104]Berardinelli, A., Cevoli, C., Silaghi, F. A., Fabbri, A., Ragni, L., Giunchi, A.,& Bassi, D. FT-NIR Spectroscopy for the Quality Characterization of Apricots (Prunus Armeniaca L.). Journal of Food Science,2010,75(7):E462-E468.
[105]Guidetti, R., Beghi, R.,& Bodria, L. Evaluation of grape quality parameters by a simple Vis/NIRsystem. Transactions of the ASABE,2010,52(3):477-484.
[106]Louw, E. D.,& Theron, K. I. Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biology and Technology,2010,58(3): 176-184.
[107]金同铭,崔洪昌,河野澄夫.近红外(NIR)光谱法测定完整苹果糖的含量.华北农学报,1995,10(2)：87-90.
[108]金同铭,崔洪昌.苹果中蔗糖、葡萄糖、果糖、苹果酸的非破坏检测.华北农学报,1997,12(1)：91-96.
[109]Liu, Y. D.,& Ying, Y. B. Use of FT-NIR spectrometry in non-invasive measurements of internal quality of 'Fuji' apples. Postharvest Biology and Technology,2005,37(1):65-71.
[110]刘燕德,应义斌,傅霞萍.近红外漫反射用于检测苹果糖度及有效酸度的研究.光谱学与光谱分析,2005,25(11)：1793-1796.
[111]Ying, Y. B., Liu, Y. D.,& Tao, Y. Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy. Applied Optics,2005, 44(25):5224-5229.
[112]Liu, Y. D., Ying, Y., Yu, H. Y,& Fu, X. P. Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose, and sucrose in intact apple fruits. Journal of Agricultural and Food Chemistry,2006,54(8):2810-2815.
[113]Liu, Y. D., Ying, Y. B., Fu, X. P.,& Lu, H. S. Experiments on predicting sugar content in apples by FT-NIR technique. Journal of Food Engineering,2007,80(3):986-989.
[114]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的的苹果糖度近红外光谱检测.食品科学,2005,26(6)：189-192.
[115]张海东,赵杰文,刘木华.利用净分析物预处理法简化苹果糖度预测模型.江苏大学学报(自然科学版),2005,26(4)：277-280.
[116]张海东,赵杰文,刘木华.基于混合线性分析的苹果糖度近红外光谱检测.农业机械学报,2006,37(4)：149-151,163.
[117]邹小波,赵杰文.独立分量分析预处理法提高苹果糖度模型预测精度研究.分析化学研究简报,2006,34(9)：1291-1294.
[118]Zou, X. B., Li, Y. X.,& Zhao, J. W. Using genetic algorithm interval partial least squares selection of the optimal near infrared wavelength regions for determination of the soluble solids content of "Fuji" apple. Journal of near Infrared Spectroscopy,2007,15(3):153-159.
[119]Zou, X. B., Zhao, J. W., Huang, X. Y,& Li, Y. X. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of 'Fuji' apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
[120]Zou, X. B., Zhao, J. W.,& Li, Y.X. Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of 'Fuji' apple based on BiPLS and FiPLS models. Vibrational Spectroscopy,2007,44(2):220-227.
[121]Shi, B. L., Ji, B. P., Zhu, D. Z., Tu, Z. H.,& Qing, Z. S. Study on genetic algorithms-based nir wavelength selection for determination of soluble solids content in fuji apples. Journal of Food Quality,2008,31(2):232-249.
[122]Shi, B., Ji, B., Tu, Z.,& Zhu, D. Determination of soluble solids content in fuji apples based on near infrared spectroscopy and artificial neural networks. Italian Journal of Food Science,2008,20(1): 23-37.
[123]王加华,韩东海.基于遗传算法的苹果糖度近红外光谱分析.光谱学与光谱分析,2008,28(10)：2308-2311.
[124]王加华,潘璐,李鹏飞,韩东海.苹果糖度近红外光谱分析模型的温度补偿.光谱学与光谱分析,2009,29(6)：1517-1520.
[125]Fan, G. Q., Zha, J. W., Du, R.,& Gao, L. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering,2009,93(4):416-420.
[126]Zhu, D. Z., Ji, B. P., Meng, C. Y., Shi, B. L., Tu, Z. H.,& Qing, Z. S. A comparison of linear regression methods for the detection of apple internal quality by near infrared spectroscopy. Computer and Computing Technologies In Agriculture II,2009,3:1671-1680.
[127]Gomez, A. H., He, Y.,& Pereira, A. G. Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 2006,77(2):313-319.
[128]陆辉山,傅霞萍,谢丽娟,应义斌.可见/近红外光估测完整柑橘水果可溶性固形物含量的研究.光谱学与光谱分析,2007,27(9)：1727-1730.
[129]Lu, H. S., Jiang, H. Y., Fu, X. P., Yu, H. Y, Xu, H. R.,& Ying, Y. B.2008. Non-invasive measurements of the internal quality of intact 'Gannan' navel orange by VIS/NIR spectroscopy. Transactions of the ASABE,51(3):1009-1014.
[130]夏俊芳.基于近红外光谱的贮藏脐橙品质无损检测方法研究[博士学位论文].武汉,华中农业大学,2008.
[131]刘燕德,罗吉,陈兴苗.可见/近红外光谱的南丰蜜桔可溶性固形物含量定量分析.红外与毫米波学报,2008,27(2)：119-122.
[132]Liu, Y. D., Sun, X. D.,& Ouyang, A. G. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Science and Technology,2010,43(4):602-607.
[133]Liu, Y. D., Sun, X. D., Zhang, H. L.,& Aiguo, O. Y. Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy. Computers and Electronics in Agriculture,2010,71(S1):10-14.
[134]Shao, Y. N., He, Y, Bao, Y. D.,& Mao, J. Y. Near-infrared spectroscopy for classification of oranges and prediction of the sugar content. International Journal of Food Properties,2009,12(3):644-658.
[135]刘燕德,应义斌.傅里叶近红外光谱的雪青梨酸度偏最小二乘法定量分析.光谱学与光谱分析,2006,26(8)：1454-1456.
[136]Liu, Y. D.,& Ying, Y. B. Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry. International Journal of Food Properties,2007,10(4):877-886.
[137]Liu, Y. D., Chen, X. M., Sun, X. D.,& Ying, Y. B. Non-destructive measurement of pear internal quality indices by visible and near-infrared spectrometric techniques. New Zealand Journal of Agricultural Research,2007,50(5):1051-1057.
[138]刘燕德,彭彦颖,高荣杰,孙旭东,郝勇.基于LED组合光源的水晶梨可溶性固形物和大小在线检测.农业工程学报,2010,26(11)：338-343.
[139]杨磊.梨子内在品质的近红外漫反射光谱无损检测技术研究[硕士学位论文].南京,南京农业大学,2008.
[140]孙通,应义斌,刘魁武,胡雷秀.梨可溶性固形物含量的在线近红外光谱检测.光谱学与光谱分析,2008,28(11)：2536-2539.
[141]Sun, T., Lin, H. J., Xu, H. R.,& Ying, Y. B. Effect of fruit moving speed on predicting soluble solids content of 'Cuiguan' pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biology and Technology,2009,51(1):86-90.
[142]李东华.南果梨内在品质近红外光谱无损检测技术研究[博士学位论文].沈阳,沈阳农业大学,2009.
[143]潘璐,王加华,李鹏飞,孙谦,张勇,韩东海.砂梨糖度近红外光谱波段遗传算法优化.光谱学与光谱分析,2009,29(5)：1246-1250
[144]王加华,潘璐,孙谦,李鹏飞,韩东海.遗传算法结合偏最小二乘法无损评价西洋梨糖度.光谱学与光谱分析,2009,29(3)：678-681.
[145]徐惠荣,陈晓伟,应义斌.基于多元校正法的香梨糖度可见/近红外光谱检测.农业机械学报,2010,41(12)：126-128,147.
[146]陈香维.猕猴桃近红外光谱无损检测技术研究[博士学位论文].咸阳,西北农林科技大学,2009.
[147]Lue, Q., Tang, M. J., Cai, J. R.,& Lu, H. Z. Long-term prediction of Zhonghua kiwifruit dry matter by near infrared spectroscopy. Scienceasia,2010,36(3):210-215.
[148]Lue, Q., Tang, M. J., Cai, J. R., Lu, H. Z.,& Sumpun, C. Selection of efficient wavelengths in NIR spectrum for determination of dry matter in kiwifruit. Maejo International Journal of Science and Technology,2010,4(1):113-124.
[149]虞佳佳,何勇,鲍一丹.基于光谱技术的芒果糖度酸度无损检测方法研究.光谱学与光谱分析,2008,28(12)：2839-2842.
[150]屠振华,籍保平,孟超英,朱大洲,史波林,庆兆珅.第十五届全国分子光谱学术报告会论文集,中国北京,2008.
[151]Tao, X. M,& Bao, Y. D. Measurement of sugar content of watermelon using near-infrared reflectance spectroscopy in comparison with dielectric property. Proceeding of SPIE,2006,6047: 60473V(1-7).
[152]田海清,应义斌,徐惠荣,陆辉山,傅霞萍.瓜可溶性固形物含量近红外透射检测技术.农业机械学报,2007,38(5)：111-113.
[153]田海清,应义斌,徐惠荣,陆辉山,谢丽娟.运动西瓜可见/近红外光谱采集系统及品质检测试验研究.光谱学与光谱分析,2009,29(6)：1536-1540.
[154]袁琳.网纹瓜可溶性固形物、总酸和坚实度近红外光谱检测技术研究[硕士学位论文].咸阳,西北农林科技大学,2010.
[155]Ying, Y. B., Liu, Y. D., Wang, J. P., Fu, X. P.,& Li, Y. B. Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches. Transactions of the ASAE, 2005,48(1):229-234.
[156]马广,傅霞萍,周莹,应义斌,徐惠荣,谢丽娟,林涛.大白桃糖度的近红外漫反射光谱无损检测试验研究.光谱学与光谱分析,2007,27(5)：907-910.
[157]徐惠荣,汪辉君,黄康,应义斌,杨诚,钱豪,胡俊.PLS和SMLR建模方法在水蜜桃糖度无损检测中的比较研究.光谱学与光谱分析,2008,28(11)：2523-2526.
[158]Fu, X. P., Li, J. P., Zhou, Y, Ying, Y. B., Xie, L. J., Niu, X. Y., Yan, Z. K.,& Yu, H. Y. Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy. Journal of Zhejiang University-Science B,2009,10(2):120-125.
[159]王加华,李鹏飞,曹楠宁,韩东海.基于iPLS原理最优化信息区间的桃糖度组合权重PLS模型研究.红外与毫米波学报,2009,28(5)：386-391.
[160]http://www.towagp.co.jp/amamir_index.html.
[161]http://www.unitec-group.com/.
[162]http://www.sacmi.it/.
[163]http://www.fpi-inc.com/.
[164]http://www.si-seiko.co.jp/.
[165]http://www.cvs.com.au/.
[166]http://www.hgjd888.cn/.
[167]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.
[168]Norris, K. H.,& Butler W. L. Techniques for obtaining absorption spectra on intact biological samples. Rre Transactions on Biomedical Electronics,1961,8(3):153-157.
[169]Hart, J. R., Golumbic, C.,& Norris, K. H. Determination of moisture content of seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chemistry,1962,39(2):94-99.
[170]Bengera, I.,& Norris, K. H. Determination of moisture content in soybeans by direct spectrophotometry. Israel Journal of Agricultural Research,1968,18(3):125-132.
[171]Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I.,& Lammertyn, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:A review. Postharvest Biology and Technology,2007,46(2):99-11.
[172]Osborne, B. G.2006. Applications of near infrared spectroscopy in quality screening of early-generation material in cereal breeding programmes. Journal of Near Infrared Spectroscopy,14(2): 93-101.
[173]Karoui, R.,& De Baerdemaeker, J. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry,2007, 102(3):621-640.
[174]史苏佳.近红外的光学特性及在食品中的检测原理和应用.山西食品工业,2005,4：39-40,42.
[175]段民孝,邢锦丰,郭景伦,王元东,赵久然.近红外光谱(NIRS)分析技术及其在农业中的应用.北京农业科学,2002,1：11-14.
[176]梁逸曾,俞汝勤.分析化学手册(10)——化学计量学.北京：化工出版社,2001.
[177]芦永军,曲艳玲,宋敏.近红外相关光谱的多元散射校正处理研究.光谱学与光谱分析,2007,27(5)：877-880.
[178]尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展.药物分析杂志,2008,28(5)：824-829.
[179]Zou, X. B., Zhao, J. W., Malcolm, J. W. P., Mel, H.,& Mao, H. P. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta,2010,667(1-2):14-32.
[180]冯红年,甘彬,金尚忠.棉涤混合纺织面料含量的近红外光谱检测.激光与红外,2005,35(10)：768-770.
[181]陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用.分析测试学报,2007,26(1)：66-69.
[182]Araujo, M. C. U., Saldanha, T. C. B., Galvao, R. K. H., Yoneyama, T., Chame, H. C.,& Visani, V. The successive projections algorithm for variable selection in spectroscopic multicomponent. Chemometrics and Intelligent Laboratory Systems,2001,57(2):65-73.
[183]Galvao, R. K. H., Araujo, M. C. U., Fragoso, W. D., Silva, E. C., Jose, G. E., Soares, S. F. C.,& Paiva, H. M. A variable elimination method to improve the parsimony of MLR Models Using the Successive Projections Algorithm. Chemometrics and Intelligent Laboratory Systems,2008,92(1): 83-91.
[184]Centner, V., Massart, D. L., deNoord, O. E., deJong, S., Vandeginste, B. M.,& Sterna, C. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry,1996, 68(21):3851-3858.
[185]Koshoubu, J., Iwata, T.,& Minami, S. Application of the modified UVE-PLS method for a mid-infrared absorption spectral data set of water-ethanol mixtures. Applied Spectroscopy,2000, 54(1):148-152.
[186]Cai, W. S., Li, Y. K.,& Shao, X. G. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems,2008,90(2):188-194.
[187]N(?)rgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L.,& Engelsen, S. B. Interval partial least squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy,2000,54(3):413-419.
[188]Leardi, R.,& N(?)rgaard, L. Sequential application of backward interval-PLS and genetic algorithms for the selection of relevant spectral regions. Journal of Chemometrics,2004,18(11):486-497.
[189]李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006.
[190]陆婉珍.现代近红外光谱分析技术(第二版).北京：中国石化出版社,2006.
[191]张录达,金泽宸,沈晓南,赵龙莲,李军会,严衍禄.SVM回归法在近红外光谱定量分析中的应用研究.光谱学与光谱分析,2005,25(9)：1400-1403.
[192]陈全胜,赵杰文,张海东.王新宇基于支持向量机的近红外光谱鉴别茶叶的真伪.光学学报,2006,26(6)：933-937.
[193]王艳斌,袁洪福,陆婉珍.一种基于目标因子分析的模型传递方法.光谱学与光谱分析,2005,25(3)：398-401.
[194]褚小立,袁洪福,陆婉珍.光谱多元校正中的模型传递.光谱学与光谱分析,2001,21(6)：881-885.
[195]Williams, P.,& Norris, K. Near-infrared technology in the agricultural and food industries. USA: American Association of Cereal Chemists,1987.
[196]Cubeddu, R., Andrea, C. D., Pifferi, A., Taroni, P., Torricelli, A., Valentini, G., Dover, C., Johnson, D., Ruiz-Altisent, M.,& Valero, C. Nondestructive quantification of chemical and physical properties of fruits by time-resolved reflectance spectroscopy in the wavelength range 650-1000 nm. Applied optics,2001,40(4):538-543.
[197]http://www.models.kvl.dk/algorithms.
[198]http://www.vub.ac.be/fabi/.
[199]http://www.eigenvector.com/.
[200]http://www.ele.ita.br/～kawakami/spa/.
[2]国家统计数据库,http://219.235.129.58/welcome.do.
[3]周蓓,徐超.浅议我国水果出口贸易.金卡工程·经济与法,2009,8：249.
[4]朱佳满,刘更森,王强.当前我国果品出口现状、存在问题与对策.山西果树,2003,5：27-28.
[5]张吉国,胡继连,张兆新.我国果品产业的发展现状与对策.山东农业大学学报(社会科学版),2002,4(3)：31-35.
[6]Norris, K. H. Reports on design and development of a new moisture meter. Agricultural Engineering, 1964,45(7):370-372.
[7]Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I,& Lammertyn, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:A review. Postharvest Biology and Technology,2007,46:99-11.
[8]Sun, T., Huang, K., Xu, H. R.,& Ying, Y. B. Research advances in nondestructive determination of internal quality in watermelon/melon:A review. Journal of Food Engineering,2010,100(4): 569-577.
[9]Karoui, R.,& De Baerdemaeker, J. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry,2007, 102(3):621-640.
[10]王田子,郑丽敏,田立军,吴平,朱虹,任发政.近红外在乳及乳制品质量检测中的研究进展.光谱学与光谱分析,2010,30(12)：3208-3212.
[11]Osborne, B. G. Applications of near infrared spectroscopy in quality screening of early-generation material in cereal breeding programmes. Journal of Near Infrared Spectroscopy,2006,14(2): 93-101.
[12]周光华,朱大洲,王成.近红外光谱在粮食作物检测中的应用进展.安徽农业科学,2010,38(28)：15475-15478.
[13]Prieto, N., Roehe, R., Lavin, P., Batten, G.,& Andres, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality:A review. Meat Science,2009,83(2): 175-186.
[14]Bochereau, L., Bourgine, P.,& Palagos, B. A method for prediction by combining data analysis and neural networks:Application to prediction of apple quality using near infra-red spectraag. Journal of Agricultural Engineering Research,1992,51(3):207-216.
[15]Cho, R. K., Sohn, M. R.,& Kwon, Y. K. New observation of nondestructive evaluation for sweetness in apple fruit using near infrared spectroscopy. Journal of near Infrared Spectroscopy, 1998,6:A75-A78.
[16]Ventura, M., de Jager, A., de Putter, H.,& Roelofs, F. P. M. M. Non-destructive determination of soluble solids in apple fruit by near infrared spectroscopy (NIRS). Postharvest Biology and Technology,1998,14(1):21-27.
[17]Lammertyn, J., Nicolai, B., Ooms, K., De Smedt, V,& De Baerdemaeker, J. Non-destructive measurement of acidity, soluble solids, and firmness of Jonagold apples using NIR-spectroscopy. Transactions of the ASAE,1998,41(4):1089-1094.
[18]Lammertyn, J., Peirs, A., De Baerdemaeker, J.,& Nicolai, B. Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology,2000,18(2):121-132.
[19]Lu, R. F., Guyer, D. E.,& Beaudry, R. M. Determination of firmness and sugar content of apples using near-infrared diffuse reflectance. Journal of Texture Studies,2000,31(6):615-630.
[20]Lu, R.,& Ariana, D. A near-infrared sensing technique for measuring internal quality of apple fruit. Applied Engineering in Agriculture,2002,18(5):585-590.
[21]Temma, T., Hanamatsu, K.,& Shinoki, F. Development of a portable near infrared sugar-measuring instrument. Journal of near Infrared Spectroscopy,2002,10(1):77-83.
[22]McGlone, V. A., Jordan, R. B.,& Martinsen, P. J. Vis/NIR estimation at harvest of pre-and post-storage quality indices for 'Royal Gala' apple. Postharvest Biology and Technology,2002,25(2): 135-144.
[23]McGlone, V. A., Jordan, R. B., Seelye, R.,& Clark, C. J. Dry-matter-a better predictor of the post-storage soluble solids in apples? Postharvest Biology and Technology,2003,28(3):431-435.
[24]McGlone, V. A.,& Martinsen, P. J. Transmission measurements on intact apples moving at high speed. Journal of near Infrared Spectroscopy,2004,12(1):37-43.
[25]Peirs, A., Scheerlinck, N., Touchant, K.,& Nicolai, B. M. Comparison of Fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosystems Engineering,2002,81(3):305-311.
[26]Peirs, A., Scheerlinck, N.,& Nicolai, B. M. Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biology and Technology,2003,30(3): 233-248.
[27]Peirs, A., Tirry, J., Verlinden, B., Darius, P.,& Nicolai, B. M. Effect of biological variability on the robustness of NIR models for soluble solids content of apples. Postharvest Biology and Technology, 2003,28(2):269-280.
[28]Park, B., Abbott, J. A., Lee, K. J., Choi, C. H.,& Choi, K. H. Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and Gala apples. Transactions of the ASAE,2003,46(6):1721-1731.
[29]Quilitzsch, R.,& Hobert, E. Fast determination of apple quality by spectroscopy in the near infrared. Journal of Applied Botany-Angewandte Botanik,2003,77(5-6):172-176.
[30]Roger, J. M., Chauchard, F.,& Bellon-Maurel, V. EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits. Chemometrics and Intelligent Laboratory Systems,2003,66(2):191-204.
[31]Sanchez, N. H., Lurol, S., Roger, J. M.,& Bellon-Maurel, W. Robustness of models based on NIR spectra for sugar content prediction in apples. Journal of near Infrared Spectroscopy,2003,11(2): 97-107.
[32]Walsh, K. B., Golic, M.,& Greensill, C. V. Sorting of fruit using near infrared spectroscopy: application to a range of fruit and vegetables for soluble solids and dry matter content. Journal of near Infrared Spectroscopy,2004,12(3):141-148.
[33]Zude, M., Herold, B., Roger, J. M., Bellon-Maurel, V.,& Landahl, S. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering,2006,77(2):254-260.
[34]Alamar, M. C., Bobelyn, E., Lammertyn, J., Nicolai, B. M.,& Molto, E. Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple. Postharvest Biology and Technology,2007,45(1):38-45.
[35]Bessho, H., Kudo, K., Omori, J., Inomata, Y., Wada, M., Masuda, T., Nakamoto, Y., Fujisawa, H.,& Suzuki, Y. A portable non-destructive quality meter for understanding fruit soluble solids in apple canopies. Acta Horticulturae,2007,732:593-597.
[36]Nicolai, B. M., Theron, K. I.,& Lammertyn, J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemometrics and Intelligent Laboratory Systems, 2007,85(2):243-252.
[37]Qing, Z., Ji, B.,& Zude, M. Wavelength selection for predicting physicochemical properties of apple fruit based on near-infrared spectroscopy. Journal of Food Quality,2007,30(4):511-526.
[38]Paz, P., Sanchez, M. T., Perez-Marin, D., Guerrero, J. E.,& Garrido-Varo, A. Evaluating NIR instruments for quantitative and qualitative assessment of intact apple quality. Journal of the Science of Food and Agriculture,2009,89(5):781-790.
[39]Jha, S. N.,& Ruchi, G. Non-destructive prediction of quality of intact apple using near infrared spectroscopy. Journal of Food Science and Technology-Mysore,2010,47(2):207-213.
[40]Bobelyn, E., Serban, A. S., Nicu, M., Lammertyn, J., Nicolai, B. M.,& Saeys, W. Postharvest quality of apple predicted by NIR-spectroscopy:Study of the effect of biological variability on spectra and model performance. Postharvest Biology and Technology,2010,55(3):133-143.
[41]Kawano, S., Fujiwara, T.,& Iwamoto, M. Nondestructive determination of sugar content in satsuma mandarine using near-infrared (NIR) transmittance. Journal of the Japanese Society for Horticultural Science,1993,62(2):465-470.
[42]Greensill, C. V.,& Walsh, K. B. Calibration transfer between miniature photodiode array-based spectrometers in the near infrared assessment of mandarin soluble solids content. Journal of near Infrared Spectroscopy,2002,10(1):27-35.
[43]McGlone, V. A., Fraser, D. G., Jordan, R. B.,& Kunnemeyer, R. Internal quality assessment of mandarin fruit by Vis/NIR spectroscopy. Journal of near Infrared Spectroscopy,2003,11(5): 323-332.
[44]Lee, K., Kim, G., Kang, S., Son, J., Choi, D.,& Choi, K. Measurement of sugar contents in citrus using near infrared transmittance. Key Engineering Materials,2004,270-273:1014-1019.
[45]Miller, W. M.,& Zude-Sasse, M. NIR-based sensing to measure soluble solids content of Florida citrus. Applied Engineering in Agriculture,2004,20(3):321-327.
[46]Guthrie, J. A., Walsh, K. B., Reid, D.J.,Liebenberg, C. J. Assessment of internal quality attributes of mandarin fruit.1. NIR calibration model development. Australian Journal of Agricultural Research,2005,56(4):405-416.
[47]Guthrie, J. A., Reid, D. J.,& Walsh, K. B. Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness. Australian Journal of Agricultural Research,2005,56(4): 417-426.
[48]Sinelli, N., Spinardi, A., Di Egidio, V., Mignani, I.,& Casiraghi, E. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Postharvest Biology and Technology,2008,50(1):31-36.
[49]Cayuela, J. A. Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biology and Technology,2008,47(1):75-80.
[50]Cayuela, J. A.,& Weiland, C. Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biology and Technology,2010,58(2):113-120.
[51]Clark, C. J., McGlone, V. A., Requejo, C., White, A.,& Woolf, A. B. Dry matter determination in 'Hass' avocado by NIR spectroscopy. Postharvest Biology and Technology,2003,29(3):301-308.
[52]Nicolai, B. M., Verlinden, B. E., Desmet, M., Saevels, S., Saeys, W., Theron, K., Cubeddu, R., Pifferi, A.,& Torricelli, A. Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biology and Technology,2008,47(1): 68-74.
[53]Martinsen, P.,& Schaare, P. Measuring soluble solids distribution in kiwifruit using near-infrared imaging spectroscopy. Postharvest Biology and Technology,1998,14(3):271-281.
[54]McGlone, V. A.,& Kawano, S. Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology,1998,13(2):131-141.
[55]McGlone, V. A., Jordan, R. B., Seelye, R.,& Martinsen, P. J. Comparing density and NIR methods for measurement of Kiwifruit dry matter and soluble solids content. Postharvest Biology and Technology,2002,26(2):191-198.
[56]McGlone, V. A., Clark, C. J.,& Jordan, R. B. Comparing density and VNIR methods for predicting quality parameters of yellow-fleshed kiwifruit (Actinidia chinensis). Postharvest Biology and Technology,2007,46(1):1-9.
[57]Osborne, S. D.,& Kunnemeyer, R. A low-cost system for the grading of kiwifruit. Journal of near Infrared Spectroscopy,1999,7(1):9-15.
[58]Schaare, P. N.,& Fraser, D. G.Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology,2000,20(2):175-184.
[59]Clark, C. J., McGlone, V. A., De Silva, H. N., Manning, M. A., Burdon, J.,& Mowat, A. D. Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest. Postharvest Biology and Technology,2004,32(2):147-158.
[60]Moghimi, A., Aghkhani, M. H., Sazgarnia, A.,& Sarmad, M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering,2010,106(3):295-302.
[61]Guthrie, J.,& Walsh, K. Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy. Australian Journal of Experimental Agriculture,1997,37(2):253-263.
[62]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Improvement of PLS calibration for Brix value and dry matter of mango using information from MLR calibration. Journal of near Infrared Spectroscopy, 2001,9(4):287-295.
[63]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Performance of a portable near infrared instrument for Brix value determination of intact mango fruit. Journal of near Infrared Spectroscopy,2003, 11(3):175-181.
[64]Saranwong, S., Sornsrivichai, J.,& Kawano, S. On-tree evaluation of harvesting quality of mango fruit using a hand-held NIR instrument. Journal of near Infrared Spectroscopy,2003,11(4):283-293.
[65]Saranwong, S., Sornsrivichai, J.,& Kawano, S. Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biology and Technology,2004,31(2):137-145.
[66]Saranwong, S., Kawano, S.,& Sornsrivichai, J. Advance technique to predict eating quality of ripe-mango at unripe-stage using near infrared spectroscopy. Acta Horticulturae,2005,682: 1427-1433.
[67]Subedi, P. P., Walsh, K. B.,& Owens, G. Prediction of mango eating quality at harvest using short-wave near infrared spectrometry. Postharvest Biology and Technology,2007,43(3):326-334.
[68]Delwiche, S. R., Mekwatanakarn, W.,& Wang, C. Y. Soluble solids and simple sugars measurement in intact mango using near infrared spectroscopy. Horttechnology,2008,18(3):410-416.
[69]Dull, G. G, Leffler, R. G.,& Birth, G. S. Near-infrared spectrophotometry for measurement of soluble solids in intact honeydew melons. HortScience,1990,25:1132.
[70]Dull, G.G, Leffler, R. G., Birth, G. S.,& Smittle, D. A. Instrument for nondestructive measurement of soluble solids in honeydew melons. Transactions of the ASAE,1992,35(2),735-737.
[71]Guthrie, J., Wedding, B.,& Walsh, K. Robustness of NIR calibrations for soluble solids in intact melon and pineapple. Journal of near Infrared Spectroscopy,1998,6(1):259-265.
[72]Guthrie, J. A., Liebenberg, C. J.,& Walsh, K. B. NIR model development and robustness in prediction of melon fruit total soluble solids. Australian Journal of Agricultural Research,2006, 57(4):411-418.
[73]Slaughter, D. C., Cavaletto, C. G., Gautz, L. D.,& Paull, R. E. Non-destructive determination of soluble solids in papayas using near infrared spectroscopy. Journal of near Infrared Spectroscopy, 1999,7(4):223-228.
[74]Greensill, C. V.,& Newman, D. S. An investigation into the determination of the maturity of pawpaws (Carica papaya) from NIR transmission spectra. Journal of near Infrared Spectroscopy, 1999,7(2):109-116.
[75]Greensill, C. V.,& Walsh, K. B. A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit. Measurement Science & Technology,2000, 11(12):1674-1684.
[76]Greensill, C. V., Wolfs, P. J., Spiegelman, C. H.,& Walsh, K. B. Calibration transfer between PDA-based NIR spectrometers in the NIR assessment of melon soluble solids content. Applied Spectroscopy,2001,55(5):647-653.
[77]Walsh, K. B., Guthrie, J. A.,& Burney, J. W. Application of commercially available, low-cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit. Australian Journal of Plant Physiology,2000,27(12):1175-1186.
[78]Ito, H., Morimoto, S., Yamauchi, R., Ippoushi, K., Azuma, K.,& Hugashio, H. Potential of near infrared spectroscopy for nondestructive estimation of soluble solids in watermelons. Acta Horticulturae,2002,588,353-356.
[79]Tsuta, M., Sugiyama, J.,& Sagara, Y. Near-infrared imaging spectroscopy based on the sugar absorption band for melons. Journal of Agricultural and Food Chemistry,2002,50(1):48-52.
[80]Long, R. L. Improving fruit soluble solids content in melon (Cucumis melo L.) (reticulatus group) in the Australian production system [Doctoral dissertation]. Australia, Central Queensland University, 2005,72-96,121-135.
[81]Long, R. L.,& Walsh, K. B. Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy. Australian Journal of Agricultural Research,2006,57(4):403-410.
[82]Abebe, A. T. Total sugar and maturity evaluation of intact watermelon using near infrared spectroscopy. Journal of Near Infrared Spectroscopy,2006,14(1),67-70.
[83]Flores, K., Sanchez, M. T., Perez-Marin, D. C., Lopez, M. D., Guerrero, J. E., Garrido-Varo, A. Prediction of total soluble solid content in intact and cut melons and watermelons using near infrared spectroscopy. Journal of Near Infrared Spectroscopy,2008,16(2):91-98.
[84]Slaughter, D. C. Nondestructive determination of internal quality in peaches and nectarines. Transactions of the ASAE,1995,38(2):617-623.
[85]Slaughter, D. C., Thompson, J. F.,& Tan, E. S. Nondestructive determination of total and soluble solids in fresh prune using near infrared spectroscopy. Postharvest Biology and Technology,2003, 28(3):437-444.
[86]Peiris, K. H. S., Dull, G. G., Leffler, R. G.,& Kays, S. J. Near-infrared spectrometric method for nondestructive determination of soluble solids content of peaches. Journal of the American Society for Horticultural Science,1998,123(5):898-905.
[87]Guthrie, J.,& Walsh, K. Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy. Australian Journal of Experimental Agriculture,1999,39(1):73-80.
[88]Carlini, P., Massantini, R.,& Mencarelli, F. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. Journal of Agricultural and Food Chemistry, 2000,48(11):5236-5242.
[89]Tarkosova, J.,& Copikova, J. Determination of carbohydrate content in bananas during ripening and storage by near infrared spectroscopy. Journal of near Infrared Spectroscopy,2000,8(1):21-26.
[90]Lu, R. Predicting firmness and sugar content of sweet cherries using near-infrared diffuse reflectance spectroscopy. Transactions of the ASAE,2001,44(5):1265-1271.
[91]Herrera, J., Guesalaga, A.,& Agosin, E. Shortwave-near infrared spectroscopy for non-destructive determination of maturity of wine grapes. Measurement Science & Technology,2003,14(5): 689-697.
[92]Chauchard, F.,Cogdill, R., Roussel, S., Roger, J. M.,& Bellon-Maurel, V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy:development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems,2004,71(2): 141-150.
[93]Cozzolino, D., Esler, M. B., Dambergs, R. G., Cynkar, W. U., Boehm, D. R., Francis, I. L.,& Gishen, M. Prediction of colour and pH in grapes using a diode array spectrophotometer (400-1100 nm). Journal of near Infrared Spectroscopy,2004,12(2):105-111.
[94]Manley, M., Joubert, E., Myburgh, L., Lotz, E.,& Kidd, M. Prediction of soluble solids content and post-storage internal quality of Bulida apricots using near infrared spectroscopy. Journal of near Infrared Spectroscopy,2007,15(3):179-188.
[95]Paz, P., Sanchez, M. T., Perez-Marin, D., Guerrero, J. E.,& Garrido-Varo, A. Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry,2008,56(8):2565-2570.
[96]Reita, G., Peano, C., Saranwong, S.,& Kawano, S. An evaluating technique for variety compatibility of fruit applied to a near infrared Brix calibration system:a case study using Brix calibration for nectarines. Journal of near Infrared Spectroscopy,2008,16(2):83-89.
[97]Bureau, S., Ruiz, D., Reich, M., Gouble, B., Bertrand, D., Audergon, J. M.,& Renard, C. M. G C. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chemistry,2009,113(4):1323-1328.
[98]Camps, C.,& Christen, D. On-tree follow-up of apricot fruit development using a hand-held NIR instrument. Journal of Food Agriculture & Environment,2009,7(2):394-400.
[99]Camps, C.,& Christen, D. Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT-Food Science and Technology,2009,42(6):1125-1131.
[100]Fernandez-Novales, J., Lopez, M. I., Sanchez, M. T., Garcia-Mesa, J. A.,& Gonzalez-Caballero, V. Assessment of quality parameters in grapes during ripening using a miniature fiber-optic near-infrared spectrometer. International Journal of Food Sciences and Nutrition,2009,60(S7): 265-277.
[101]Nishizawa, T., Mori, Y., Fukushima, S., Natsuga, M.,& Maruyama, Y. Non-destructive analysis of soluble sugar components in strawberry fruits using near-infrared spectroscopy. Journal of the Japanese Society for Food Science and Technology-Nippon Shokuhin Kagaku Kogaku Kaishi,2009, 56(4):229-235.
[102]Perez-Marin, D., Sanchez, M. T., Paz, P., Soriano, M. A., Guerrero, J. E.,& Garrido-Varo, A. Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biology and Technology,2009,52(2):180-188.
[103]Perez-Marin, D., Paz, P., Guerrero, J. E., Garrido-Varo, A.,& Sanchez, M. T. Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums. Journal of Food Engineering,2010,93(3):294-302.
[104]Berardinelli, A., Cevoli, C., Silaghi, F. A., Fabbri, A., Ragni, L., Giunchi, A.,& Bassi, D. FT-NIR Spectroscopy for the Quality Characterization of Apricots (Prunus Armeniaca L.). Journal of Food Science,2010,75(7):E462-E468.
[105]Guidetti, R., Beghi, R.,& Bodria, L. Evaluation of grape quality parameters by a simple Vis/NIRsystem. Transactions of the ASABE,2010,52(3):477-484.
[106]Louw, E. D.,& Theron, K. I. Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy. Postharvest Biology and Technology,2010,58(3): 176-184.
[107]金同铭,崔洪昌,河野澄夫.近红外(NIR)光谱法测定完整苹果糖的含量.华北农学报,1995,10(2)：87-90.
[108]金同铭,崔洪昌.苹果中蔗糖、葡萄糖、果糖、苹果酸的非破坏检测.华北农学报,1997,12(1)：91-96.
[109]Liu, Y. D.,& Ying, Y. B. Use of FT-NIR spectrometry in non-invasive measurements of internal quality of 'Fuji' apples. Postharvest Biology and Technology,2005,37(1):65-71.
[110]刘燕德,应义斌,傅霞萍.近红外漫反射用于检测苹果糖度及有效酸度的研究.光谱学与光谱分析,2005,25(11)：1793-1796.
[111]Ying, Y. B., Liu, Y. D.,& Tao, Y. Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy. Applied Optics,2005, 44(25):5224-5229.
[112]Liu, Y. D., Ying, Y., Yu, H. Y,& Fu, X. P. Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose, and sucrose in intact apple fruits. Journal of Agricultural and Food Chemistry,2006,54(8):2810-2815.
[113]Liu, Y. D., Ying, Y. B., Fu, X. P.,& Lu, H. S. Experiments on predicting sugar content in apples by FT-NIR technique. Journal of Food Engineering,2007,80(3):986-989.
[114]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的的苹果糖度近红外光谱检测.食品科学,2005,26(6)：189-192.
[115]张海东,赵杰文,刘木华.利用净分析物预处理法简化苹果糖度预测模型.江苏大学学报(自然科学版),2005,26(4)：277-280.
[116]张海东,赵杰文,刘木华.基于混合线性分析的苹果糖度近红外光谱检测.农业机械学报,2006,37(4)：149-151,163.
[117]邹小波,赵杰文.独立分量分析预处理法提高苹果糖度模型预测精度研究.分析化学研究简报,2006,34(9)：1291-1294.
[118]Zou, X. B., Li, Y. X.,& Zhao, J. W. Using genetic algorithm interval partial least squares selection of the optimal near infrared wavelength regions for determination of the soluble solids content of "Fuji" apple. Journal of near Infrared Spectroscopy,2007,15(3):153-159.
[119]Zou, X. B., Zhao, J. W., Huang, X. Y,& Li, Y. X. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of 'Fuji' apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
[120]Zou, X. B., Zhao, J. W.,& Li, Y.X. Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of 'Fuji' apple based on BiPLS and FiPLS models. Vibrational Spectroscopy,2007,44(2):220-227.
[121]Shi, B. L., Ji, B. P., Zhu, D. Z., Tu, Z. H.,& Qing, Z. S. Study on genetic algorithms-based nir wavelength selection for determination of soluble solids content in fuji apples. Journal of Food Quality,2008,31(2):232-249.
[122]Shi, B., Ji, B., Tu, Z.,& Zhu, D. Determination of soluble solids content in fuji apples based on near infrared spectroscopy and artificial neural networks. Italian Journal of Food Science,2008,20(1): 23-37.
[123]王加华,韩东海.基于遗传算法的苹果糖度近红外光谱分析.光谱学与光谱分析,2008,28(10)：2308-2311.
[124]王加华,潘璐,李鹏飞,韩东海.苹果糖度近红外光谱分析模型的温度补偿.光谱学与光谱分析,2009,29(6)：1517-1520.
[125]Fan, G. Q., Zha, J. W., Du, R.,& Gao, L. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering,2009,93(4):416-420.
[126]Zhu, D. Z., Ji, B. P., Meng, C. Y., Shi, B. L., Tu, Z. H.,& Qing, Z. S. A comparison of linear regression methods for the detection of apple internal quality by near infrared spectroscopy. Computer and Computing Technologies In Agriculture II,2009,3:1671-1680.
[127]Gomez, A. H., He, Y.,& Pereira, A. G. Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 2006,77(2):313-319.
[128]陆辉山,傅霞萍,谢丽娟,应义斌.可见/近红外光估测完整柑橘水果可溶性固形物含量的研究.光谱学与光谱分析,2007,27(9)：1727-1730.
[129]Lu, H. S., Jiang, H. Y., Fu, X. P., Yu, H. Y, Xu, H. R.,& Ying, Y. B.2008. Non-invasive measurements of the internal quality of intact 'Gannan' navel orange by VIS/NIR spectroscopy. Transactions of the ASABE,51(3):1009-1014.
[130]夏俊芳.基于近红外光谱的贮藏脐橙品质无损检测方法研究[博士学位论文].武汉,华中农业大学,2008.
[131]刘燕德,罗吉,陈兴苗.可见/近红外光谱的南丰蜜桔可溶性固形物含量定量分析.红外与毫米波学报,2008,27(2)：119-122.
[132]Liu, Y. D., Sun, X. D.,& Ouyang, A. G. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Science and Technology,2010,43(4):602-607.
[133]Liu, Y. D., Sun, X. D., Zhang, H. L.,& Aiguo, O. Y. Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy. Computers and Electronics in Agriculture,2010,71(S1):10-14.
[134]Shao, Y. N., He, Y, Bao, Y. D.,& Mao, J. Y. Near-infrared spectroscopy for classification of oranges and prediction of the sugar content. International Journal of Food Properties,2009,12(3):644-658.
[135]刘燕德,应义斌.傅里叶近红外光谱的雪青梨酸度偏最小二乘法定量分析.光谱学与光谱分析,2006,26(8)：1454-1456.
[136]Liu, Y. D.,& Ying, Y. B. Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry. International Journal of Food Properties,2007,10(4):877-886.
[137]Liu, Y. D., Chen, X. M., Sun, X. D.,& Ying, Y. B. Non-destructive measurement of pear internal quality indices by visible and near-infrared spectrometric techniques. New Zealand Journal of Agricultural Research,2007,50(5):1051-1057.
[138]刘燕德,彭彦颖,高荣杰,孙旭东,郝勇.基于LED组合光源的水晶梨可溶性固形物和大小在线检测.农业工程学报,2010,26(11)：338-343.
[139]杨磊.梨子内在品质的近红外漫反射光谱无损检测技术研究[硕士学位论文].南京,南京农业大学,2008.
[140]孙通,应义斌,刘魁武,胡雷秀.梨可溶性固形物含量的在线近红外光谱检测.光谱学与光谱分析,2008,28(11)：2536-2539.
[141]Sun, T., Lin, H. J., Xu, H. R.,& Ying, Y. B. Effect of fruit moving speed on predicting soluble solids content of 'Cuiguan' pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biology and Technology,2009,51(1):86-90.
[142]李东华.南果梨内在品质近红外光谱无损检测技术研究[博士学位论文].沈阳,沈阳农业大学,2009.
[143]潘璐,王加华,李鹏飞,孙谦,张勇,韩东海.砂梨糖度近红外光谱波段遗传算法优化.光谱学与光谱分析,2009,29(5)：1246-1250
[144]王加华,潘璐,孙谦,李鹏飞,韩东海.遗传算法结合偏最小二乘法无损评价西洋梨糖度.光谱学与光谱分析,2009,29(3)：678-681.
[145]徐惠荣,陈晓伟,应义斌.基于多元校正法的香梨糖度可见/近红外光谱检测.农业机械学报,2010,41(12)：126-128,147.
[146]陈香维.猕猴桃近红外光谱无损检测技术研究[博士学位论文].咸阳,西北农林科技大学,2009.
[147]Lue, Q., Tang, M. J., Cai, J. R.,& Lu, H. Z. Long-term prediction of Zhonghua kiwifruit dry matter by near infrared spectroscopy. Scienceasia,2010,36(3):210-215.
[148]Lue, Q., Tang, M. J., Cai, J. R., Lu, H. Z.,& Sumpun, C. Selection of efficient wavelengths in NIR spectrum for determination of dry matter in kiwifruit. Maejo International Journal of Science and Technology,2010,4(1):113-124.
[149]虞佳佳,何勇,鲍一丹.基于光谱技术的芒果糖度酸度无损检测方法研究.光谱学与光谱分析,2008,28(12)：2839-2842.
[150]屠振华,籍保平,孟超英,朱大洲,史波林,庆兆珅.第十五届全国分子光谱学术报告会论文集,中国北京,2008.
[151]Tao, X. M,& Bao, Y. D. Measurement of sugar content of watermelon using near-infrared reflectance spectroscopy in comparison with dielectric property. Proceeding of SPIE,2006,6047: 60473V(1-7).
[152]田海清,应义斌,徐惠荣,陆辉山,傅霞萍.瓜可溶性固形物含量近红外透射检测技术.农业机械学报,2007,38(5)：111-113.
[153]田海清,应义斌,徐惠荣,陆辉山,谢丽娟.运动西瓜可见/近红外光谱采集系统及品质检测试验研究.光谱学与光谱分析,2009,29(6)：1536-1540.
[154]袁琳.网纹瓜可溶性固形物、总酸和坚实度近红外光谱检测技术研究[硕士学位论文].咸阳,西北农林科技大学,2010.
[155]Ying, Y. B., Liu, Y. D., Wang, J. P., Fu, X. P.,& Li, Y. B. Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches. Transactions of the ASAE, 2005,48(1):229-234.
[156]马广,傅霞萍,周莹,应义斌,徐惠荣,谢丽娟,林涛.大白桃糖度的近红外漫反射光谱无损检测试验研究.光谱学与光谱分析,2007,27(5)：907-910.
[157]徐惠荣,汪辉君,黄康,应义斌,杨诚,钱豪,胡俊.PLS和SMLR建模方法在水蜜桃糖度无损检测中的比较研究.光谱学与光谱分析,2008,28(11)：2523-2526.
[158]Fu, X. P., Li, J. P., Zhou, Y, Ying, Y. B., Xie, L. J., Niu, X. Y., Yan, Z. K.,& Yu, H. Y. Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy. Journal of Zhejiang University-Science B,2009,10(2):120-125.
[159]王加华,李鹏飞,曹楠宁,韩东海.基于iPLS原理最优化信息区间的桃糖度组合权重PLS模型研究.红外与毫米波学报,2009,28(5)：386-391.
[160]http://www.towagp.co.jp/amamir_index.html.
[161]http://www.unitec-group.com/.
[162]http://www.sacmi.it/.
[163]http://www.fpi-inc.com/.
[164]http://www.si-seiko.co.jp/.
[165]http://www.cvs.com.au/.
[166]http://www.hgjd888.cn/.
[167]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.
[168]Norris, K. H.,& Butler W. L. Techniques for obtaining absorption spectra on intact biological samples. Rre Transactions on Biomedical Electronics,1961,8(3):153-157.
[169]Hart, J. R., Golumbic, C.,& Norris, K. H. Determination of moisture content of seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chemistry,1962,39(2):94-99.
[170]Bengera, I.,& Norris, K. H. Determination of moisture content in soybeans by direct spectrophotometry. Israel Journal of Agricultural Research,1968,18(3):125-132.
[171]Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I.,& Lammertyn, J. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:A review. Postharvest Biology and Technology,2007,46(2):99-11.
[172]Osborne, B. G.2006. Applications of near infrared spectroscopy in quality screening of early-generation material in cereal breeding programmes. Journal of Near Infrared Spectroscopy,14(2): 93-101.
[173]Karoui, R.,& De Baerdemaeker, J. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry,2007, 102(3):621-640.
[174]史苏佳.近红外的光学特性及在食品中的检测原理和应用.山西食品工业,2005,4：39-40,42.
[175]段民孝,邢锦丰,郭景伦,王元东,赵久然.近红外光谱(NIRS)分析技术及其在农业中的应用.北京农业科学,2002,1：11-14.
[176]梁逸曾,俞汝勤.分析化学手册(10)——化学计量学.北京：化工出版社,2001.
[177]芦永军,曲艳玲,宋敏.近红外相关光谱的多元散射校正处理研究.光谱学与光谱分析,2007,27(5)：877-880.
[178]尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展.药物分析杂志,2008,28(5)：824-829.
[179]Zou, X. B., Zhao, J. W., Malcolm, J. W. P., Mel, H.,& Mao, H. P. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta,2010,667(1-2):14-32.
[180]冯红年,甘彬,金尚忠.棉涤混合纺织面料含量的近红外光谱检测.激光与红外,2005,35(10)：768-770.
[181]陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用.分析测试学报,2007,26(1)：66-69.
[182]Araujo, M. C. U., Saldanha, T. C. B., Galvao, R. K. H., Yoneyama, T., Chame, H. C.,& Visani, V. The successive projections algorithm for variable selection in spectroscopic multicomponent. Chemometrics and Intelligent Laboratory Systems,2001,57(2):65-73.
[183]Galvao, R. K. H., Araujo, M. C. U., Fragoso, W. D., Silva, E. C., Jose, G. E., Soares, S. F. C.,& Paiva, H. M. A variable elimination method to improve the parsimony of MLR Models Using the Successive Projections Algorithm. Chemometrics and Intelligent Laboratory Systems,2008,92(1): 83-91.
[184]Centner, V., Massart, D. L., deNoord, O. E., deJong, S., Vandeginste, B. M.,& Sterna, C. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry,1996, 68(21):3851-3858.
[185]Koshoubu, J., Iwata, T.,& Minami, S. Application of the modified UVE-PLS method for a mid-infrared absorption spectral data set of water-ethanol mixtures. Applied Spectroscopy,2000, 54(1):148-152.
[186]Cai, W. S., Li, Y. K.,& Shao, X. G. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems,2008,90(2):188-194.
[187]N(?)rgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L.,& Engelsen, S. B. Interval partial least squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy,2000,54(3):413-419.
[188]Leardi, R.,& N(?)rgaard, L. Sequential application of backward interval-PLS and genetic algorithms for the selection of relevant spectral regions. Journal of Chemometrics,2004,18(11):486-497.
[189]李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006.
[190]陆婉珍.现代近红外光谱分析技术(第二版).北京：中国石化出版社,2006.
[191]张录达,金泽宸,沈晓南,赵龙莲,李军会,严衍禄.SVM回归法在近红外光谱定量分析中的应用研究.光谱学与光谱分析,2005,25(9)：1400-1403.
[192]陈全胜,赵杰文,张海东.王新宇基于支持向量机的近红外光谱鉴别茶叶的真伪.光学学报,2006,26(6)：933-937.
[193]王艳斌,袁洪福,陆婉珍.一种基于目标因子分析的模型传递方法.光谱学与光谱分析,2005,25(3)：398-401.
[194]褚小立,袁洪福,陆婉珍.光谱多元校正中的模型传递.光谱学与光谱分析,2001,21(6)：881-885.
[195]Williams, P.,& Norris, K. Near-infrared technology in the agricultural and food industries. USA: American Association of Cereal Chemists,1987.
[196]Cubeddu, R., Andrea, C. D., Pifferi, A., Taroni, P., Torricelli, A., Valentini, G., Dover, C., Johnson, D., Ruiz-Altisent, M.,& Valero, C. Nondestructive quantification of chemical and physical properties of fruits by time-resolved reflectance spectroscopy in the wavelength range 650-1000 nm. Applied optics,2001,40(4):538-543.
[197]http://www.models.kvl.dk/algorithms.
[198]http://www.vub.ac.be/fabi/.
[199]http://www.eigenvector.com/.
[200]http://www.ele.ita.br/～kawakami/spa/.
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