番茄可溶性固形物和硬度的高光谱成像检测
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摘要
果蔬品质无损检测与自动化分级是农产品加工领域的热点课题,对满足消费者日益增高的食品品质和安全要求,提高果蔬市场竞争力,增加农民收入都具有重大意义。虽然我国是果蔬生产大国,但由于产后加工和处理水平不高,因而在国际市场上缺乏竞争力。与形状、尺寸、颜色和缺陷等外部品质参数一样,化学成分和质地也是评价果蔬品质的重要指标,决定着产品的品质。然而这些参数指标无法直接通过视觉观察,因此其检测难度较大。
本研究以不同产地的鲜食和加工番茄为研究对象,选取代表番茄化学成分和质地的品质参数可溶性固形物含量(SSC)和硬度(包括表皮硬度F1、果肉硬度F2和修正硬度Fxz)为检测指标,搭建反射、散射和漫透射三种高光谱成像系统,获取番茄空域光谱和纹理参数(或拟合参数),利用偏最小二乘回归(PLSR)方法建立番茄SSC和硬度的定量分析模型。探索在三种高光谱成像模式下番茄SSC和硬度的检测方法,为进一步研发番茄化学成分和质地的综合品质参数在线检测装备提供理论依据。主要研究结果和结论如下：
(1)提出了基于主成分降维思想的背景分割方法,通过对样本的主成分图像进行背景分割进而获取目标对象的光谱及纹理信息。该方法较单波段分割法可以更加有效地去除图像背景。
(2)在高光谱反射和漫透射两种成像模式下,对两种番茄光谱图像像素点光谱校正方法进行了考察。结果表明,高光谱反射成像模式下,两种校正方法对SSC的检测效果均有所改善,并且像素点光谱归一化校正效果相对较好(模型RPD=2.315)；高光谱漫透射成像模式下,像素点光谱归一化校正方法同样对SSC的检测效果有所改善(模型RPD=1.983),而像素点光谱标准正态变量校正后的模型鲁棒性差于校正前。总体而言,像素点光谱归一化校正在两种成像模式下,均能够提高番茄SSC的检测效果,具有更好的适应性。
(3)对比分析了高光谱反射成像和近红外漫反射光谱技术对番茄SSC和硬度检测效果。结果表明,采用高光谱反射成像技术获取番茄整体空间光谱信息,对番茄SSC和硬度进行检测优势明显。其中采用高光谱反射成像检测番茄SSC、 F1、 F2和Fxz模型的RPD分别为2.071,1.150,1.150,1.345。
(4)提出了新的高光谱成像模式,并同时开展了三种高光谱成像模式下对番茄SSC硬度的检测研究。研究发现：
1)三种成像模式下平均光谱相对优于纹理参数(或拟合参数)对番茄SSC和硬度测效果。其中,番茄SSC的检测效果最好,而且整体平均光谱对该指标的检测模型的RP1均接近或大于2。硬度检测中,Fxz的检测效果最好。但相对于SSC而言,光谱成像对度的检测效果不十分理想,模型的RPD均小于1.5。
2)对单一高光谱成像模式而言,高光谱漫透射成像可能由于可以获取番茄深层光信息,因此在对番茄SSC检测中较高光谱反射和高光谱散射成像具有一定优势。由于高光谱反射成像可以获取番茄较大像素区域的光谱信息,在对Fxz检测中比高光谱散射和漫透射成像技术更具优势。并且在番茄SSC和Fxz的检测中,通过多个姿态获取番茄整体平均光谱的检测方法整体优于局部小像素区域平均光谱的检测方法。
3)从不同成像模式光谱信息组合对番茄SSC和硬度检测效果来看,不同成像模式光谱信息组合对番茄SSC和硬度的检测效果较单一成像模式来说无明显的优势,在硬度检测中,依然是Fxz检测效果最好,F1和F2检测效果较差。
因此,今后在番茄SSC和硬度检测中,可以考虑采用Fxz来表征番茄的硬度特性,然后利用高光谱漫透射成像技术检测番茄SSC,利用高光谱反射成像技术检测番茄Fxz,以实现番茄SSC和硬度的准确检测。
Nondestructive quality inspection of fruits and vegetables as well as automatic grading is a hot topic in the agro-processing research area. It is of great importance to satisfy consumers" increasing demands for food quality and safety, to ensure the quality of fruits and vegetables and enhance their market competitiveness and to improve farmers'income. Although there are a great amount of fruits and vegetables in our country, the market competition capability is still left behind in the international market because of insufficient and low-tech processing of post-harvest. Internal ingredients and characters, along with external quality indicators including shape, size, color, and defects are important indexes to determine the quality of products. However, it is a difficult task to assess the indicators, because they cannot be visualized directly. Tomatoes from different regions were chosen as the study objects of this thesis. Soluble solid content (SSC) and firmness including peel firmness Fl, flesh firmness F2and corrected finnness Fxz were detected to represent internal ingredients and characters of tomatoes. Hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes were constructed and used as platforms to obtain spatial spectra and texture parameters (or fitting parameters). Then quantitative prediction models of SSC and firmness were established by using partial least square regression (PLSR). With the help of hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes, potential applications for detecting SSC and firmness were investigated to establish a theoretical basis for online detection of ingredients and characters simultaneously. The main results are as follows:
(1) A background segmentation method based on principle component score images was proposed, and was utilized to obtain mean spectra and texture information of target objects. This method is more effective in wiping out background compared with single-band segmentation.
(2) The methods for spectral correcting of pixels were applied in tomato spectral images under hyperspectral reflectance and diffuse transmittance modes. Results showed that these two correction methods could improve the detection of SSC, and the pixel spectral normalization was better (RPD=2.315) under reflectance mode; and could also enhance results which were obtained after spectral nonnalization of pixels under diffuse transmittance mode (RPD=1.983), while the robustness was slightly worse after correction using pixel spectral standard norm*variate. Overall, the pixel spectral normalization can advance the detection of SSC unde hyperspectral reflectance and diffuse transmittance modes.
(3) Comparative analysis of prediction results based on hyperspectral reflectance imaginj and near-infrared diffuse transmission spectroscopy showed that hyperspectral reflectanc imaging performed much better in predicting SSC and firmness of tomatoes by obtaining overal spatial spectra information in detecting SSC, F1, F2and Fxz, with RPD=2.071,1.150,1.1501.345, respectively.
(4) Hyperspectral diffuse transmittance imaging was proposed as a new hyperspectra imaging mode, and SSC and firmness values were measureed by three hyperspectral imaging modes. Results showed as following:
1) Mean spectra performed better than textural parameters (or fitting parameters) in detectioi of SSC and firmness under the three hyperspectral imaging modes, in which the detection o SSC was the best with RPD being close to2or larger than2. The detection of Fxz was the bes in firmness detection, however, compared with SSC detection resultes, spectral imaging foi firmness testing is not very satisfactory with the RPD<1.5.
2) For single hyperspectral imaging modes, the hyperspectral diffuse transmittance imaging has certain advantages in predicting SSC compared with reflectance and scattering imaging because of its accessibility to spectral information in deeper tissue of tomatoes. Hyperspectra reflectance imaging was superiority in detecting Fxz compared with hyperspectral scattering anc diffuse transmittance imaging mode, because spectral information in bigger pixel areas was utilized. Overall, mean spectra of tomato obtained by combined positions was more suitable foi detecting SSC and Fxz than part pixels areas of tomato.
3) The results of predicting SSC and firmness using combined spectra information, which obtained by different imaging modes, showed no obvious advantage compared with the single imaging modes. The Fxz performed best in detecting firmness, while F1and F2detection showed poor results.
Therefore, Fxz was considered as the best parameter to characterize the firmness of tomatoes And hyperspectral diffuse transmittance imaging is more suitable for detecting SSC, while hyperspectral reflectance imaging is more suitable for Fxz detection.
本研究以不同产地的鲜食和加工番茄为研究对象,选取代表番茄化学成分和质地的品质参数可溶性固形物含量(SSC)和硬度(包括表皮硬度F1、果肉硬度F2和修正硬度Fxz)为检测指标,搭建反射、散射和漫透射三种高光谱成像系统,获取番茄空域光谱和纹理参数(或拟合参数),利用偏最小二乘回归(PLSR)方法建立番茄SSC和硬度的定量分析模型。探索在三种高光谱成像模式下番茄SSC和硬度的检测方法,为进一步研发番茄化学成分和质地的综合品质参数在线检测装备提供理论依据。主要研究结果和结论如下：
(1)提出了基于主成分降维思想的背景分割方法,通过对样本的主成分图像进行背景分割进而获取目标对象的光谱及纹理信息。该方法较单波段分割法可以更加有效地去除图像背景。
(2)在高光谱反射和漫透射两种成像模式下,对两种番茄光谱图像像素点光谱校正方法进行了考察。结果表明,高光谱反射成像模式下,两种校正方法对SSC的检测效果均有所改善,并且像素点光谱归一化校正效果相对较好(模型RPD=2.315)；高光谱漫透射成像模式下,像素点光谱归一化校正方法同样对SSC的检测效果有所改善(模型RPD=1.983),而像素点光谱标准正态变量校正后的模型鲁棒性差于校正前。总体而言,像素点光谱归一化校正在两种成像模式下,均能够提高番茄SSC的检测效果,具有更好的适应性。
(3)对比分析了高光谱反射成像和近红外漫反射光谱技术对番茄SSC和硬度检测效果。结果表明,采用高光谱反射成像技术获取番茄整体空间光谱信息,对番茄SSC和硬度进行检测优势明显。其中采用高光谱反射成像检测番茄SSC、 F1、 F2和Fxz模型的RPD分别为2.071,1.150,1.150,1.345。
(4)提出了新的高光谱成像模式,并同时开展了三种高光谱成像模式下对番茄SSC硬度的检测研究。研究发现：
1)三种成像模式下平均光谱相对优于纹理参数(或拟合参数)对番茄SSC和硬度测效果。其中,番茄SSC的检测效果最好,而且整体平均光谱对该指标的检测模型的RP1均接近或大于2。硬度检测中,Fxz的检测效果最好。但相对于SSC而言,光谱成像对度的检测效果不十分理想,模型的RPD均小于1.5。
2)对单一高光谱成像模式而言,高光谱漫透射成像可能由于可以获取番茄深层光信息,因此在对番茄SSC检测中较高光谱反射和高光谱散射成像具有一定优势。由于高光谱反射成像可以获取番茄较大像素区域的光谱信息,在对Fxz检测中比高光谱散射和漫透射成像技术更具优势。并且在番茄SSC和Fxz的检测中,通过多个姿态获取番茄整体平均光谱的检测方法整体优于局部小像素区域平均光谱的检测方法。
3)从不同成像模式光谱信息组合对番茄SSC和硬度检测效果来看,不同成像模式光谱信息组合对番茄SSC和硬度的检测效果较单一成像模式来说无明显的优势,在硬度检测中,依然是Fxz检测效果最好,F1和F2检测效果较差。
因此,今后在番茄SSC和硬度检测中,可以考虑采用Fxz来表征番茄的硬度特性,然后利用高光谱漫透射成像技术检测番茄SSC,利用高光谱反射成像技术检测番茄Fxz,以实现番茄SSC和硬度的准确检测。
Nondestructive quality inspection of fruits and vegetables as well as automatic grading is a hot topic in the agro-processing research area. It is of great importance to satisfy consumers" increasing demands for food quality and safety, to ensure the quality of fruits and vegetables and enhance their market competitiveness and to improve farmers'income. Although there are a great amount of fruits and vegetables in our country, the market competition capability is still left behind in the international market because of insufficient and low-tech processing of post-harvest. Internal ingredients and characters, along with external quality indicators including shape, size, color, and defects are important indexes to determine the quality of products. However, it is a difficult task to assess the indicators, because they cannot be visualized directly. Tomatoes from different regions were chosen as the study objects of this thesis. Soluble solid content (SSC) and firmness including peel firmness Fl, flesh firmness F2and corrected finnness Fxz were detected to represent internal ingredients and characters of tomatoes. Hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes were constructed and used as platforms to obtain spatial spectra and texture parameters (or fitting parameters). Then quantitative prediction models of SSC and firmness were established by using partial least square regression (PLSR). With the help of hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes, potential applications for detecting SSC and firmness were investigated to establish a theoretical basis for online detection of ingredients and characters simultaneously. The main results are as follows:
(1) A background segmentation method based on principle component score images was proposed, and was utilized to obtain mean spectra and texture information of target objects. This method is more effective in wiping out background compared with single-band segmentation.
(2) The methods for spectral correcting of pixels were applied in tomato spectral images under hyperspectral reflectance and diffuse transmittance modes. Results showed that these two correction methods could improve the detection of SSC, and the pixel spectral normalization was better (RPD=2.315) under reflectance mode; and could also enhance results which were obtained after spectral nonnalization of pixels under diffuse transmittance mode (RPD=1.983), while the robustness was slightly worse after correction using pixel spectral standard norm*variate. Overall, the pixel spectral normalization can advance the detection of SSC unde hyperspectral reflectance and diffuse transmittance modes.
(3) Comparative analysis of prediction results based on hyperspectral reflectance imaginj and near-infrared diffuse transmission spectroscopy showed that hyperspectral reflectanc imaging performed much better in predicting SSC and firmness of tomatoes by obtaining overal spatial spectra information in detecting SSC, F1, F2and Fxz, with RPD=2.071,1.150,1.1501.345, respectively.
(4) Hyperspectral diffuse transmittance imaging was proposed as a new hyperspectra imaging mode, and SSC and firmness values were measureed by three hyperspectral imaging modes. Results showed as following:
1) Mean spectra performed better than textural parameters (or fitting parameters) in detectioi of SSC and firmness under the three hyperspectral imaging modes, in which the detection o SSC was the best with RPD being close to2or larger than2. The detection of Fxz was the bes in firmness detection, however, compared with SSC detection resultes, spectral imaging foi firmness testing is not very satisfactory with the RPD<1.5.
2) For single hyperspectral imaging modes, the hyperspectral diffuse transmittance imaging has certain advantages in predicting SSC compared with reflectance and scattering imaging because of its accessibility to spectral information in deeper tissue of tomatoes. Hyperspectra reflectance imaging was superiority in detecting Fxz compared with hyperspectral scattering anc diffuse transmittance imaging mode, because spectral information in bigger pixel areas was utilized. Overall, mean spectra of tomato obtained by combined positions was more suitable foi detecting SSC and Fxz than part pixels areas of tomato.
3) The results of predicting SSC and firmness using combined spectra information, which obtained by different imaging modes, showed no obvious advantage compared with the single imaging modes. The Fxz performed best in detecting firmness, while F1and F2detection showed poor results.
Therefore, Fxz was considered as the best parameter to characterize the firmness of tomatoes And hyperspectral diffuse transmittance imaging is more suitable for detecting SSC, while hyperspectral reflectance imaging is more suitable for Fxz detection.
引文
[1]崔家升,李晓萍.世界苹果种植概况与我国苹果生产前景展望.北方果树,2012(4)：1-3.
[2]中华人民共和国国家统计局.2013中国统计年鉴.北京：中国统计出版社,2013.
[3]单杨.中国果蔬加工产业现状及发展战略思考.中国食品学报,2010,10(1)：1-9.
[4]赵宇婷,袁占中.我国水果出口营销中遭遇绿色贸易壁垒的成因及对策,商情,2012(50)：127.
[5]Xie L, Ying Y, Ying T, et al. Discrimination of transgenic tomatoes based on visible/near-infrared spectra. Analytica Chimica Acta,2007,584(2):379-384.
[6]Dorais M, Ehret D L, Papadopoulos A P. Tomato (Solanum lycopersicum) health components:from the seed to the consumer. Phytochemistry Reviews,2008,7(2):231-250.
[7]http://www.ers.usda.gov/topics/crops/vegetables-pulses/tomatoes.aspx#.UrWi4nCbcj5
[8]曹宇光.新疆生产建设兵团番茄产业发展战略研究：[硕士学位论文].大连：大连理工大学,2008.
[9]李细平.技术壁垒对我国番茄出口的影响及对策：[硕士学位论文].长沙：湖南大学,2007.
[10]Engelhard Y N, Gazer B, Paran E. Natural antioxidants from tomato extract reduce blood pressure in patients with grade-1 hypertension:a double-blind, placebo-controlled pilot study. American Heart Journal,2006,151(1):100-106.
[11]刘静,霍建勇,冯辉.番茄风味品质相关性状研究综述.辽宁农业科学,2004,6：39-40.
[12]罗颖,薛琳,黄帅等.番茄果实可溶性固形物含量与果实指标的相关性研究.石河子大学学报(自然科学版),2010,28(1)：23-27.
[13]李君明,徐和金,周永健.有关番茄果实中可溶性固形物和番茄红素的研究进展.园艺学报,2001(S1)：661-668.
[14]Arazuri S, Jaren C, Arana J I, et al. Influence of mechanical harvest on the physical properties of processing tomato (Lycopersicon esculentum Mill.). Journal of food engineering,2007,80(1):190-198.
[15]Li C, Kan Z, Tan H, et al. Development of 4FZ-30 self-propelled tomato harvester. Transactions of the Chinese Society of Agricultural Engineering,2012,28(10):20-26.
[16]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,20]0(12)：126-129.
[17]Peiris K, Dull G G, Leffler R G, et al. 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.
[18]应义斌,刘燕德,傅霞萍.苹果有效酸度的近红外漫反射无损检测.农业机械学报,2004(6)：124-126.
[19]Peirs A, Scheerlinck N, Touchant K, et al. Comparison of Fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosystems engineering,2002, 81(3):305-311.
[20]Peiris K, Dull G G, Leffler R G, et al. 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.
[21]夏俊芳,李小昱,李培武等.基于小波变换的柑橘维生素C含量近红外光谱无损检测方法.农业工程学报,2007,23(6)：170-174.
[22]Dull G G, Leffler R G, Birth G S, et al. Instrument for nondestructive measurement of soluble solids in honeydew melons. Transactions of the ASAE,1992,35(2):735-737.
[23]Jie D, Xie L, Fu X, et al. Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique. Journal of Food Engineering,2013, 118(4):387-392.
[24]Qi S, Song S, Jiang S, et al. Establishment of a comprehensive indicator to non-destructively analyze watermelon quality at different ripening stages. Journal of Innovative Optical Health Sciences,2013, 4(6):1350031-1350034.
[25]Ortiz-Canavate J, Moya-Gonzalez A, Barreiro P. Identification and classification of out of control measurements of a NIR spectrometer under industrial use for onion quality determination.17th CIGR World Congress, Quebec, Canada,2010.
[26]Valente M, Leardi R, Self G, et al. Multivariate calibration of mango firmness using vis/NIR spectroscopy and acoustic impulse method. Journal of Food Engineering,2009,94(1):7-13.
[27]Li J, Huang W, Zhao C, et al. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Journal of Food Engineering,2012,16(2): 324-332.
[28]Zhang R, Ying Y, Rao X, et al. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture,2012,92(12): 2397-2408.
[29]应义斌,蒋亦元.机器视觉技术在农产品品质自动识别中的应用研究进展.农业工程学报,2000,16(3)：4-8.
[30]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2)：144-147.
[31]王慧慧,孙永海,张贵林等.基于压力和图像的鲜玉米果穗成熟度分级方法.农业工程学报,2010,7：67.
[32]潘磊庆,屠康,詹歌等.基于计算机视觉和声学响应信息融合的鸡蛋裂纹检测.农业工程学报,2010,26(11)：332-337.
[33]郑华东,刘木华,吴彦红等.基于计算机视觉的大米裂纹检测研究.农业工程学报,2006,22(7)：129-133.
[34]Miller B M. A color vision system for peach grading. Transactions of the ASAE,1989,32(4): 1484-1490.
[35]Choi K, Lee G, Han Y J, et al. Tomato maturity evaluation using color image analysis. Transactions of the ASAE,1995,38(1):171-176.
[36]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2)：144-147.
[37]应义斌,徐惠荣,徐正冈.用于柑桔成熟度无损检测的色度频度序列法研究.生物数学学报,2006,21(2)：306-312.
[38]曹乐平,温芝元,陈理渊.基于统计纹理的柑橘糖度与有效酸度检测.测试技术学报,2009(1)：63-67.
[39]李江波,饶秀勤,应义斌.农产品外部品质无损检测中高光谱成像技术的应用研究进展.光谱学与光谱分析,2011,31(8)：2021-2025.
[40]Zude M. Optical monitoring of fresh and processed agricultural crops. City of Boca Raton, USA:CRC Press,2008.
[41]王书志,张建华,冯全.四种成像技术在水果表面缺陷检测的应用.中国农机化,2009(6)：86-89.
[42]Hahn F. Hyperspectral mango ripeness detection. ASAE Annual International Meeting I CIGR XVth World Congress, Hyatt Regency, Chicago, Illinois, USA,2002.
[43]Lu R. Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology,2004,31(2):147-157.
[44]Ruiz-Altisent M, Lleo L, Riquelme F. Instrumental quality assessment of peaches:fusion of optical and mechanical parameters. Journal of food engineering,2006,74(4):490-499.
[45]Lle L, Barreiro P, Ruiz-Altisent M, et al. Multispectral images of peach related to firmness and maturity at harvest. Journal of Food Engineering,2009,93(2):229-235.
[46]Spectral Imaging Ltd. Imspector imaging spectrograph--user manual version 2.2.2011
[47]Elmasry G, Kamruzzaman M, Sun D, et al. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products:a review. Critical Reviews in Food Science and Nutrition, 2012,52(11):999-1023.
[48]Mollazade K, Omid M, Tab F A, et al. Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products:a Review. Food and Bioprocess Technology,2012,5(5): 1465-1485.
[49]马本学,肖文东,祁想想等.基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究.光谱学与光谱分析,2012,32(11)：3093-3097.
[50]Hashim N, Pflanz M, Regen C, et al. An approach for monitoring the chilling injury appearance in bananas by means of backscattering imaging. Journal of Food Engineering,2013,116(1):28-36.
[51]Abe H, Kawano S. Kiwifruit firmness by near infrared light scattering. J. Near Infrared Spectroscopy, 1997,5:83-89.
[52]Zude M, Pflanz M, Spinelli L, et al. Non-destructive analysis of anthocyanins in cherries by means of Lambert-Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis. Journal of Food Engineering,2011,103(1):68-75.
[53]Wang S, Huang M, Zhu Q. Model fusion for prediction of apple firmness using hyperspectral scattering image. Computers and Electronics in Agriculture,2012,80:1-7.
[54]Qin J W, Lu R F. Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology,2008,49(3): 355-365.
[55]Jun Q, Ning W, Michael O N, et al. Water Content and Weight Estimation for Potatoes Using Hyperspectral Imaging. American Society of Agricultural Engineers Annual Meeting, Florida, USA, 2005.
[56]Sivakumar S S, Qiao J, Wang N, et al. Detecting Maturity Parameters of Mango Using Hyperspectral Imaging Technique.2006 ASABE Annual International Meeting, Portland, Oregon,2006.
[57]洪添胜,乔军,Ngadi Michael O等.基于高光谱图像技术的雪花梨品质无损检测.农业工程学报,2007(2)：151-155.
[58]Rajkumar P, Wang N, Elmasry G, et al. Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of food engineering,2012,108(1):194-200.
[59]Nagata M, Tallada J G, Kobayashi T, et al. Predicting maturity quality parameters of strawberries using hyperspectral imaging.In:Ottawa, ON, Canada,2004:3805-3815
[60]Nagata M, Tallada J G, Kobayashi T, et al. NIR hyperspectral imaging for measurement of internal quality in strawberries.2005 ASAE Annual International Meeting, Tampa, Florida,2005.
[61]Elmasry G, Wang N, Elsayed A, et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of food engineering,2007,81(1):98-107.
[62]Taghizadeh M, Gowen A, O Donnell C. Prediction of white button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety, 2009,3(4):219-226.
[63]田海清,王春光,吴桂芳.蜜瓜糖度透射光谱检测技术.农业机械学报,2010(12)：130-133.
[64]Leiva-Valenzuela G A, Lu R, Aguilera J M. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering,2013,115(1):91-98.
[65]Peng Y, Lu R. Modeling multispectral scattering profiles for prediction of apple fruit firmness. Transactions of the ASAE,2005,48(1):235-242.
[66]Noh H K, Peng Y, Lu R. Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Transactions of the ASABE,2007,50(3):963-971.
[67]Peng Y K, Lu R F. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest biology and technology,2008,48(1):52-62.
[68]Huang M, Lu R F. Apple mealiness detection using hyperspectral scattering technique. Postharvest biology and technology,2010,58(3):168-175.
[69]汪泊锦,黄敏,朱启兵等.基于高光谱散射图像技术的UVE-LLE苹果粉质化分类.光子学报,2011,40(8)：1132-1136.
[70]陈菁菁,彭彦昆,李永玉等.基于高光谱荧光技术的叶菜农药残留快速检测.农业工程学报,2010(S2)：1-5.
[71]Lu R. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety,2007,1(1): 19.
[72]Mendoza F, Lu R, Ariana D, et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology,2011,62:149-160.
[73]Ruoyu Z, Yibin Y, Xiuqin R, et al. Nondestructive determination of the soluble solid content for tomato using hyperspectral diffuse transmittance imaging. ASABE Annual International Meeting, Kansas City, Missouri,2013.
[74]Xing J, Guyer D, Ariana D, et al. Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry. Sensing and Instrumentation for Food Quality and Safety, 2008,2(3):161-167.
[75]周竹,李小昱,高海龙等.漫反射和透射光谱检测马铃薯黑心病的比较.农业工程学报,2012(11)：237-242.
[76]Ariana D P, Lu R F. Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Computers and electronics in agriculture,2010,74(1):137-144.
[77]Saeed M, Probst L, Betz G. Assessment of diffuse transmission and reflection modes in near-infrared quantification, part 2:Diffuse reflection information depth. Journal of pharmaceutical sciences,2011, 100(3):1130-1141.
[78]Fan G, Zha J, Du R, et al. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering,2009,93(4):416-420.
[79]陆辉山,傅霞萍,谢丽娟等.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.
[80]张若宇,饶秀勤,高迎旺等.基于高光谱漫透射成像整体检测番茄可溶性固形物含量.农业工程学报,2013,12(29)：247-252.
[81]王树文.计算机视觉技术在农产品自动检测与分级中的研究：[硕士学位论文].哈尔滨：东北农业大学,2002.
[82]张嘏伟.计算机视觉系统在番茄品质识别与分类中的研究：[硕士学位论文].石家庄：河北农业大学,2005.
[83]赵杰文,刘木华,杨国彬.基于HIS颜色特征的田间成熟番茄识别技术.农业机械学报,2004,35(5)：122-124.
[84]郑喆.番茄色选机信号采集与图像处理的算法及实现：[硕士学位论文].乌鲁木齐：新疆大学,2008.
[85]坎杂,张若宇,江英兰等.加工番茄自动除杂分选工艺及专用设备.中国,发明专利,2010
[86]Zhang R Y, Kan Z, Yan X G, et al. Analysis on Actuating Mechanism for Tomato Color Sorting Robot Based on High-Speed Photography. Advanced Materials Research,2011,308:384-387.
[87]张亚静,Shibusawa Sakae,李民赞.基于机器视觉的番茄内部品质预测.农业工程学报,2010, 26(Supp.2):366-370.
[88]Gomez A H, Hu G, Wang J, et al. Evaluation of tomato maturity by electronic nose. Computers and electronics in agriculture,2006,54(1):44-52.
[89]Berna A Z, Lammertyn J, Saevels S, et al. Electronic nose systems to study shelf life and cultivar effect on tomato aroma profile. Sensors and Actuators B:Chemical,2004,97(2):324-333.
[90]Brezmes J, Fructuoso M L, Llobet E, et al. Evaluation of an electronic nose to assess fruit ripeness. Sensors Journal, IEEE,2005,5(1):97-108.
[91]周亦斌,王俊.基于电子鼻的番茄成熟度及贮藏时间评价的研究.农业工程学报,2005,21(4)：113-117.
[92]李淑丽.番茄成熟过程中超微弱发光的研究：[硕士学位论文].西安：西北大学,2005.
[93]Triglia A, Malfa G, Musumeci F, et al. Delayed luminescence as an indicator of tomato fruit quality. Journal of food science,1998,63(3):512-515.
[94]陈志远,张继澍,刘亚龙等.番茄成熟度与其电学参数关系的研究.西北植物学报,2008,28(4)：826-830.
[95]Chen Z, Zhang J, Liu Y, et al. Relationships between Tomato Maturity and Dielectric Parameters. Acta Botanica Boreali-Occidentalia Sinica,2008,28(4):826.
[96]张敏,钟志友,赵惠忠等.番茄果实热导率测试装置参数实验.农业机械学报,2009,40(z1)：93-96.
[97]张敏,张百良,孙治强等.微探针法测试番茄各成熟阶段导热系数的研究.工程热物理学报,2005,26(z1)：213-216.
[98]Pedro A M, Ferreira M M. Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Analytical Chemistry,2005,77(8): 2505-2511.
[99]Shao Y, He Y, Gomez A H, et al. Visible/near infrared spectrometric technique for nondestructive assessment of tomato Heatwave quality characteristics. Journal of Food Engineering,2007,81(4): 672-678.
[100]scibisz I, Reich M, Bureau S, et al. Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato. Food Chemistry,2011,125(4):1390-1397.
[101]Camps C, Simone C, Gilli C. Assessment of Tomato Quality Using Portable NIR Spectroscopy and PLSR with Wavelengths Selection. XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010):International Symposium on 936,2010.
[102]董守龙,任芊,黄友之.近红外光谱分析技术的发展和应用.化工生产与技术,2004,11(6)：44-46.
[103]http://www.maselli.com/en/product/sv01-tomato-grading-station/
[104]http://www.oceanoptics.cn/product/spectrasuite
[105]蔡健荣,王建黑,黄星奕等.高光谱图像技术检测柑橘果锈.光电工程,2009(6)：26-30.
[106]王海华,李长缨,梅树立等.农产品线扫描高光谱成像系统的集成标定方法.农业工程学报,2012(14)：244-249.
[107]王听,李建桥,任露泉等.番茄果实采收后的硬度测定及其变化规律.农业机械学报,2005,36(6)：65-67,64.
[108]高程程,惠晓威.基于灰度共生矩阵的纹理特征提取.计算机系统应用,2010,19(6)：195-198.
[109]刘丽,匡纲要.图像纹理特征提取方法综述.中国图象图形学报,2009(04)：622-635.
[110]Tuceryan M, Jain A K. Texture analysis. World Scientific,1993:235-276.
[111]http://wenku.baidu.com/view/c2ffi141d650e52ea551898b9.html
[112]刘刚.灰度共生矩阵和BP神经网络在肝癌CT图像诊断中的应用：[硕士学位论文].徐州：徐州医学院,2011.
[113]侯海苗,冀小平.基于灰度共生矩阵的纹理特征.长治学院学报,2008(05)：31-32.
[114]于海鹏.基于数字图像处理学的木材纹理定量化研究：[博士学位论文].哈尔滨：东北林业大学,2005.
[115]Kaur G. Texture Analysis for Classification of SAR Images:[Master Dissertation]. Patiala:Thapar University,2010.
[116]严衍禄,赵龙莲,韩东海等.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.
[117]刘燕德.水果糖度和酸度的近红外光谱无损检测研究：[博士学位论文].杭州：浙江大学,2006.
[118]傅霞萍.水果内部品质可见／近红外光谱无损检测方法的实验研究：[博士学位论文].杭州：浙江大学,2008.
[119]徐惠荣,汪辉君,黄康等.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(11)：2523-2526.
[120]闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析,2004(10)：1205-1209.
[121]陆婉珍.现代近红外光谱分析技术.中国石化出版社,2007.
[122]吴琼,原忠虎,王晓宁.基于偏最小二乘回归分析综述.沈阳大学学报,2007,19(2)：33-35.
[123]刘便霞.PLS校正集对FT-IR光谱定量分析精度的影响：[硕士学位论文].合肥：中国科学技术大学,2011.
[124]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.
[125]Xiaobo Z, Jiewen Z, Povey M J, et al. Variables selection methods in near-infrared spectroscopy. Analytica chimica acta,2010,667(1):14-32.
[126]Karoui R, Mouazen A M, Dufour E, et al. Chemical characterisation of European Emmental cheeses by near infrared spectroscopy using chemometric tools. International dairy journal,2006,16(10): 1211-1217.
[127]http://www.esrichina-bj.cn/old/produce/ENVI/index.htm
[128]http://www.esrichina-bj.cn/old/produce/ENVI/IDL.htm
[129]http://www.docin.com/p-96831947.html
[130]http://www.docin.com/p-427043636.html
[131]http://zh.wikipedia.org/zh-cn/MATLAB
[132]Richards J A, Jia X. Remote sensing digital image analysis:an introduction. Springer Verlag,2006.
[133]http://wenku.baidu.com/view/902af4fbf705cc 17552709fa.html
[134]李江波.脐橙表面缺陷的快速检测方法研究：[博士学位论文].杭州：浙江大学,2012.
[135]Gonzalez Rafael C., Woods Richard E., Eddins Steven L.数字图像处理.北京：电子工业出版社,2006
[136]郝颖明,朱枫.2维Otsu自适应阈值的快速算法.中国图象图形学报,2005,10(4)：485-488.
[137]林开颜,吴军辉,徐立鸿.彩色图像分割方法综述.中国图象图形学报,2005,10(1)：1-10.
[138]韩思奇,王蕾.图像分割的闽值法综述.系统工程与电子技术,2002,24(6)：91-94.
[139]Tao Y, Wen Z. An adaptive spherical image transform for high-speed fruit defect detection. Transactions of the ASAE,1999,42(1):241-246.
[140]付峰,应义斌.球体图像灰度变换模型及其在柑桔图像校正中的应用.农业工程学报,2004,20(4)：117-120.
[141]Gomez-Sanchis J, Molto E, Camps-Valls G, et al. Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of food engineering,2008,85(2):191-200.
[142]Kawano S, Haff R, Saranwong S. Methods for correcting morphological-based deficiencies in hyperspectral images of round objects. Journal of Near Infrared Spectroscopy,2011,19(6):431-441.
[143]李江波,饶秀勤,应义斌.水果表面亮度不均校正及单阈值缺陷提取研究.农业机械学报,2011,42(8)： 159-163.
[144]Gaston E, Frias J M, Cullen P J, et al. Prediction of Polyphenol Oxidase Activity Using Visible Near-Infrared Hyperspectral Imaging on Mushroom (Agaricus bisporus) Caps. Journal of Agricultural and Food Chemistry,2010,58(10):6226-6233.
[145]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展, 2004(04)：528-542.
[146]Candolfi A, Wu W, Massart D L, et al. Comparison of classification approaches applied to NIR-spectra of clinical study lots. Journal of pharmaceutical and biomedical analysis,1998,16(8):1329-1347.
[147]Zhang R, Ying Y, Rao X, et al. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture,2012,92(12): 2397-2408.
[148]刘晓.基于多光谱图像的柑橘糖度含量在线无损检测研究：[硕士学位论文].南昌：江西农业大学,2011.
[149]周竹,李小昱,高海龙等.漫反射和透射光谱检测马铃薯黑心病的比较.农业工程学报,2012,28(011)：237-242.
[2]中华人民共和国国家统计局.2013中国统计年鉴.北京：中国统计出版社,2013.
[3]单杨.中国果蔬加工产业现状及发展战略思考.中国食品学报,2010,10(1)：1-9.
[4]赵宇婷,袁占中.我国水果出口营销中遭遇绿色贸易壁垒的成因及对策,商情,2012(50)：127.
[5]Xie L, Ying Y, Ying T, et al. Discrimination of transgenic tomatoes based on visible/near-infrared spectra. Analytica Chimica Acta,2007,584(2):379-384.
[6]Dorais M, Ehret D L, Papadopoulos A P. Tomato (Solanum lycopersicum) health components:from the seed to the consumer. Phytochemistry Reviews,2008,7(2):231-250.
[7]http://www.ers.usda.gov/topics/crops/vegetables-pulses/tomatoes.aspx#.UrWi4nCbcj5
[8]曹宇光.新疆生产建设兵团番茄产业发展战略研究：[硕士学位论文].大连：大连理工大学,2008.
[9]李细平.技术壁垒对我国番茄出口的影响及对策：[硕士学位论文].长沙：湖南大学,2007.
[10]Engelhard Y N, Gazer B, Paran E. Natural antioxidants from tomato extract reduce blood pressure in patients with grade-1 hypertension:a double-blind, placebo-controlled pilot study. American Heart Journal,2006,151(1):100-106.
[11]刘静,霍建勇,冯辉.番茄风味品质相关性状研究综述.辽宁农业科学,2004,6：39-40.
[12]罗颖,薛琳,黄帅等.番茄果实可溶性固形物含量与果实指标的相关性研究.石河子大学学报(自然科学版),2010,28(1)：23-27.
[13]李君明,徐和金,周永健.有关番茄果实中可溶性固形物和番茄红素的研究进展.园艺学报,2001(S1)：661-668.
[14]Arazuri S, Jaren C, Arana J I, et al. Influence of mechanical harvest on the physical properties of processing tomato (Lycopersicon esculentum Mill.). Journal of food engineering,2007,80(1):190-198.
[15]Li C, Kan Z, Tan H, et al. Development of 4FZ-30 self-propelled tomato harvester. Transactions of the Chinese Society of Agricultural Engineering,2012,28(10):20-26.
[16]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,20]0(12)：126-129.
[17]Peiris K, Dull G G, Leffler R G, et al. 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.
[18]应义斌,刘燕德,傅霞萍.苹果有效酸度的近红外漫反射无损检测.农业机械学报,2004(6)：124-126.
[19]Peirs A, Scheerlinck N, Touchant K, et al. Comparison of Fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosystems engineering,2002, 81(3):305-311.
[20]Peiris K, Dull G G, Leffler R G, et al. 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.
[21]夏俊芳,李小昱,李培武等.基于小波变换的柑橘维生素C含量近红外光谱无损检测方法.农业工程学报,2007,23(6)：170-174.
[22]Dull G G, Leffler R G, Birth G S, et al. Instrument for nondestructive measurement of soluble solids in honeydew melons. Transactions of the ASAE,1992,35(2):735-737.
[23]Jie D, Xie L, Fu X, et al. Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique. Journal of Food Engineering,2013, 118(4):387-392.
[24]Qi S, Song S, Jiang S, et al. Establishment of a comprehensive indicator to non-destructively analyze watermelon quality at different ripening stages. Journal of Innovative Optical Health Sciences,2013, 4(6):1350031-1350034.
[25]Ortiz-Canavate J, Moya-Gonzalez A, Barreiro P. Identification and classification of out of control measurements of a NIR spectrometer under industrial use for onion quality determination.17th CIGR World Congress, Quebec, Canada,2010.
[26]Valente M, Leardi R, Self G, et al. Multivariate calibration of mango firmness using vis/NIR spectroscopy and acoustic impulse method. Journal of Food Engineering,2009,94(1):7-13.
[27]Li J, Huang W, Zhao C, et al. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Journal of Food Engineering,2012,16(2): 324-332.
[28]Zhang R, Ying Y, Rao X, et al. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture,2012,92(12): 2397-2408.
[29]应义斌,蒋亦元.机器视觉技术在农产品品质自动识别中的应用研究进展.农业工程学报,2000,16(3)：4-8.
[30]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2)：144-147.
[31]王慧慧,孙永海,张贵林等.基于压力和图像的鲜玉米果穗成熟度分级方法.农业工程学报,2010,7：67.
[32]潘磊庆,屠康,詹歌等.基于计算机视觉和声学响应信息融合的鸡蛋裂纹检测.农业工程学报,2010,26(11)：332-337.
[33]郑华东,刘木华,吴彦红等.基于计算机视觉的大米裂纹检测研究.农业工程学报,2006,22(7)：129-133.
[34]Miller B M. A color vision system for peach grading. Transactions of the ASAE,1989,32(4): 1484-1490.
[35]Choi K, Lee G, Han Y J, et al. Tomato maturity evaluation using color image analysis. Transactions of the ASAE,1995,38(1):171-176.
[36]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究.农业工程学报,2004,20(2)：144-147.
[37]应义斌,徐惠荣,徐正冈.用于柑桔成熟度无损检测的色度频度序列法研究.生物数学学报,2006,21(2)：306-312.
[38]曹乐平,温芝元,陈理渊.基于统计纹理的柑橘糖度与有效酸度检测.测试技术学报,2009(1)：63-67.
[39]李江波,饶秀勤,应义斌.农产品外部品质无损检测中高光谱成像技术的应用研究进展.光谱学与光谱分析,2011,31(8)：2021-2025.
[40]Zude M. Optical monitoring of fresh and processed agricultural crops. City of Boca Raton, USA:CRC Press,2008.
[41]王书志,张建华,冯全.四种成像技术在水果表面缺陷检测的应用.中国农机化,2009(6)：86-89.
[42]Hahn F. Hyperspectral mango ripeness detection. ASAE Annual International Meeting I CIGR XVth World Congress, Hyatt Regency, Chicago, Illinois, USA,2002.
[43]Lu R. Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology,2004,31(2):147-157.
[44]Ruiz-Altisent M, Lleo L, Riquelme F. Instrumental quality assessment of peaches:fusion of optical and mechanical parameters. Journal of food engineering,2006,74(4):490-499.
[45]Lle L, Barreiro P, Ruiz-Altisent M, et al. Multispectral images of peach related to firmness and maturity at harvest. Journal of Food Engineering,2009,93(2):229-235.
[46]Spectral Imaging Ltd. Imspector imaging spectrograph--user manual version 2.2.2011
[47]Elmasry G, Kamruzzaman M, Sun D, et al. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products:a review. Critical Reviews in Food Science and Nutrition, 2012,52(11):999-1023.
[48]Mollazade K, Omid M, Tab F A, et al. Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products:a Review. Food and Bioprocess Technology,2012,5(5): 1465-1485.
[49]马本学,肖文东,祁想想等.基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究.光谱学与光谱分析,2012,32(11)：3093-3097.
[50]Hashim N, Pflanz M, Regen C, et al. An approach for monitoring the chilling injury appearance in bananas by means of backscattering imaging. Journal of Food Engineering,2013,116(1):28-36.
[51]Abe H, Kawano S. Kiwifruit firmness by near infrared light scattering. J. Near Infrared Spectroscopy, 1997,5:83-89.
[52]Zude M, Pflanz M, Spinelli L, et al. Non-destructive analysis of anthocyanins in cherries by means of Lambert-Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis. Journal of Food Engineering,2011,103(1):68-75.
[53]Wang S, Huang M, Zhu Q. Model fusion for prediction of apple firmness using hyperspectral scattering image. Computers and Electronics in Agriculture,2012,80:1-7.
[54]Qin J W, Lu R F. Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology,2008,49(3): 355-365.
[55]Jun Q, Ning W, Michael O N, et al. Water Content and Weight Estimation for Potatoes Using Hyperspectral Imaging. American Society of Agricultural Engineers Annual Meeting, Florida, USA, 2005.
[56]Sivakumar S S, Qiao J, Wang N, et al. Detecting Maturity Parameters of Mango Using Hyperspectral Imaging Technique.2006 ASABE Annual International Meeting, Portland, Oregon,2006.
[57]洪添胜,乔军,Ngadi Michael O等.基于高光谱图像技术的雪花梨品质无损检测.农业工程学报,2007(2)：151-155.
[58]Rajkumar P, Wang N, Elmasry G, et al. Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of food engineering,2012,108(1):194-200.
[59]Nagata M, Tallada J G, Kobayashi T, et al. Predicting maturity quality parameters of strawberries using hyperspectral imaging.In:Ottawa, ON, Canada,2004:3805-3815
[60]Nagata M, Tallada J G, Kobayashi T, et al. NIR hyperspectral imaging for measurement of internal quality in strawberries.2005 ASAE Annual International Meeting, Tampa, Florida,2005.
[61]Elmasry G, Wang N, Elsayed A, et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of food engineering,2007,81(1):98-107.
[62]Taghizadeh M, Gowen A, O Donnell C. Prediction of white button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety, 2009,3(4):219-226.
[63]田海清,王春光,吴桂芳.蜜瓜糖度透射光谱检测技术.农业机械学报,2010(12)：130-133.
[64]Leiva-Valenzuela G A, Lu R, Aguilera J M. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering,2013,115(1):91-98.
[65]Peng Y, Lu R. Modeling multispectral scattering profiles for prediction of apple fruit firmness. Transactions of the ASAE,2005,48(1):235-242.
[66]Noh H K, Peng Y, Lu R. Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Transactions of the ASABE,2007,50(3):963-971.
[67]Peng Y K, Lu R F. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest biology and technology,2008,48(1):52-62.
[68]Huang M, Lu R F. Apple mealiness detection using hyperspectral scattering technique. Postharvest biology and technology,2010,58(3):168-175.
[69]汪泊锦,黄敏,朱启兵等.基于高光谱散射图像技术的UVE-LLE苹果粉质化分类.光子学报,2011,40(8)：1132-1136.
[70]陈菁菁,彭彦昆,李永玉等.基于高光谱荧光技术的叶菜农药残留快速检测.农业工程学报,2010(S2)：1-5.
[71]Lu R. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety,2007,1(1): 19.
[72]Mendoza F, Lu R, Ariana D, et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology,2011,62:149-160.
[73]Ruoyu Z, Yibin Y, Xiuqin R, et al. Nondestructive determination of the soluble solid content for tomato using hyperspectral diffuse transmittance imaging. ASABE Annual International Meeting, Kansas City, Missouri,2013.
[74]Xing J, Guyer D, Ariana D, et al. Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry. Sensing and Instrumentation for Food Quality and Safety, 2008,2(3):161-167.
[75]周竹,李小昱,高海龙等.漫反射和透射光谱检测马铃薯黑心病的比较.农业工程学报,2012(11)：237-242.
[76]Ariana D P, Lu R F. Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles. Computers and electronics in agriculture,2010,74(1):137-144.
[77]Saeed M, Probst L, Betz G. Assessment of diffuse transmission and reflection modes in near-infrared quantification, part 2:Diffuse reflection information depth. Journal of pharmaceutical sciences,2011, 100(3):1130-1141.
[78]Fan G, Zha J, Du R, et al. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering,2009,93(4):416-420.
[79]陆辉山,傅霞萍,谢丽娟等.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.
[80]张若宇,饶秀勤,高迎旺等.基于高光谱漫透射成像整体检测番茄可溶性固形物含量.农业工程学报,2013,12(29)：247-252.
[81]王树文.计算机视觉技术在农产品自动检测与分级中的研究：[硕士学位论文].哈尔滨：东北农业大学,2002.
[82]张嘏伟.计算机视觉系统在番茄品质识别与分类中的研究：[硕士学位论文].石家庄：河北农业大学,2005.
[83]赵杰文,刘木华,杨国彬.基于HIS颜色特征的田间成熟番茄识别技术.农业机械学报,2004,35(5)：122-124.
[84]郑喆.番茄色选机信号采集与图像处理的算法及实现：[硕士学位论文].乌鲁木齐：新疆大学,2008.
[85]坎杂,张若宇,江英兰等.加工番茄自动除杂分选工艺及专用设备.中国,发明专利,2010
[86]Zhang R Y, Kan Z, Yan X G, et al. Analysis on Actuating Mechanism for Tomato Color Sorting Robot Based on High-Speed Photography. Advanced Materials Research,2011,308:384-387.
[87]张亚静,Shibusawa Sakae,李民赞.基于机器视觉的番茄内部品质预测.农业工程学报,2010, 26(Supp.2):366-370.
[88]Gomez A H, Hu G, Wang J, et al. Evaluation of tomato maturity by electronic nose. Computers and electronics in agriculture,2006,54(1):44-52.
[89]Berna A Z, Lammertyn J, Saevels S, et al. Electronic nose systems to study shelf life and cultivar effect on tomato aroma profile. Sensors and Actuators B:Chemical,2004,97(2):324-333.
[90]Brezmes J, Fructuoso M L, Llobet E, et al. Evaluation of an electronic nose to assess fruit ripeness. Sensors Journal, IEEE,2005,5(1):97-108.
[91]周亦斌,王俊.基于电子鼻的番茄成熟度及贮藏时间评价的研究.农业工程学报,2005,21(4)：113-117.
[92]李淑丽.番茄成熟过程中超微弱发光的研究：[硕士学位论文].西安：西北大学,2005.
[93]Triglia A, Malfa G, Musumeci F, et al. Delayed luminescence as an indicator of tomato fruit quality. Journal of food science,1998,63(3):512-515.
[94]陈志远,张继澍,刘亚龙等.番茄成熟度与其电学参数关系的研究.西北植物学报,2008,28(4)：826-830.
[95]Chen Z, Zhang J, Liu Y, et al. Relationships between Tomato Maturity and Dielectric Parameters. Acta Botanica Boreali-Occidentalia Sinica,2008,28(4):826.
[96]张敏,钟志友,赵惠忠等.番茄果实热导率测试装置参数实验.农业机械学报,2009,40(z1)：93-96.
[97]张敏,张百良,孙治强等.微探针法测试番茄各成熟阶段导热系数的研究.工程热物理学报,2005,26(z1)：213-216.
[98]Pedro A M, Ferreira M M. Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Analytical Chemistry,2005,77(8): 2505-2511.
[99]Shao Y, He Y, Gomez A H, et al. Visible/near infrared spectrometric technique for nondestructive assessment of tomato Heatwave quality characteristics. Journal of Food Engineering,2007,81(4): 672-678.
[100]scibisz I, Reich M, Bureau S, et al. Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato. Food Chemistry,2011,125(4):1390-1397.
[101]Camps C, Simone C, Gilli C. Assessment of Tomato Quality Using Portable NIR Spectroscopy and PLSR with Wavelengths Selection. XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010):International Symposium on 936,2010.
[102]董守龙,任芊,黄友之.近红外光谱分析技术的发展和应用.化工生产与技术,2004,11(6)：44-46.
[103]http://www.maselli.com/en/product/sv01-tomato-grading-station/
[104]http://www.oceanoptics.cn/product/spectrasuite
[105]蔡健荣,王建黑,黄星奕等.高光谱图像技术检测柑橘果锈.光电工程,2009(6)：26-30.
[106]王海华,李长缨,梅树立等.农产品线扫描高光谱成像系统的集成标定方法.农业工程学报,2012(14)：244-249.
[107]王听,李建桥,任露泉等.番茄果实采收后的硬度测定及其变化规律.农业机械学报,2005,36(6)：65-67,64.
[108]高程程,惠晓威.基于灰度共生矩阵的纹理特征提取.计算机系统应用,2010,19(6)：195-198.
[109]刘丽,匡纲要.图像纹理特征提取方法综述.中国图象图形学报,2009(04)：622-635.
[110]Tuceryan M, Jain A K. Texture analysis. World Scientific,1993:235-276.
[111]http://wenku.baidu.com/view/c2ffi141d650e52ea551898b9.html
[112]刘刚.灰度共生矩阵和BP神经网络在肝癌CT图像诊断中的应用：[硕士学位论文].徐州：徐州医学院,2011.
[113]侯海苗,冀小平.基于灰度共生矩阵的纹理特征.长治学院学报,2008(05)：31-32.
[114]于海鹏.基于数字图像处理学的木材纹理定量化研究：[博士学位论文].哈尔滨：东北林业大学,2005.
[115]Kaur G. Texture Analysis for Classification of SAR Images:[Master Dissertation]. Patiala:Thapar University,2010.
[116]严衍禄,赵龙莲,韩东海等.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005.
[117]刘燕德.水果糖度和酸度的近红外光谱无损检测研究：[博士学位论文].杭州：浙江大学,2006.
[118]傅霞萍.水果内部品质可见／近红外光谱无损检测方法的实验研究：[博士学位论文].杭州：浙江大学,2008.
[119]徐惠荣,汪辉君,黄康等.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(11)：2523-2526.
[120]闵顺耕,李宁,张明祥.近红外光谱分析中异常值的判别与定量模型优化.光谱学与光谱分析,2004(10)：1205-1209.
[121]陆婉珍.现代近红外光谱分析技术.中国石化出版社,2007.
[122]吴琼,原忠虎,王晓宁.基于偏最小二乘回归分析综述.沈阳大学学报,2007,19(2)：33-35.
[123]刘便霞.PLS校正集对FT-IR光谱定量分析精度的影响：[硕士学位论文].合肥：中国科学技术大学,2011.
[124]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.
[125]Xiaobo Z, Jiewen Z, Povey M J, et al. Variables selection methods in near-infrared spectroscopy. Analytica chimica acta,2010,667(1):14-32.
[126]Karoui R, Mouazen A M, Dufour E, et al. Chemical characterisation of European Emmental cheeses by near infrared spectroscopy using chemometric tools. International dairy journal,2006,16(10): 1211-1217.
[127]http://www.esrichina-bj.cn/old/produce/ENVI/index.htm
[128]http://www.esrichina-bj.cn/old/produce/ENVI/IDL.htm
[129]http://www.docin.com/p-96831947.html
[130]http://www.docin.com/p-427043636.html
[131]http://zh.wikipedia.org/zh-cn/MATLAB
[132]Richards J A, Jia X. Remote sensing digital image analysis:an introduction. Springer Verlag,2006.
[133]http://wenku.baidu.com/view/902af4fbf705cc 17552709fa.html
[134]李江波.脐橙表面缺陷的快速检测方法研究：[博士学位论文].杭州：浙江大学,2012.
[135]Gonzalez Rafael C., Woods Richard E., Eddins Steven L.数字图像处理.北京：电子工业出版社,2006
[136]郝颖明,朱枫.2维Otsu自适应阈值的快速算法.中国图象图形学报,2005,10(4)：485-488.
[137]林开颜,吴军辉,徐立鸿.彩色图像分割方法综述.中国图象图形学报,2005,10(1)：1-10.
[138]韩思奇,王蕾.图像分割的闽值法综述.系统工程与电子技术,2002,24(6)：91-94.
[139]Tao Y, Wen Z. An adaptive spherical image transform for high-speed fruit defect detection. Transactions of the ASAE,1999,42(1):241-246.
[140]付峰,应义斌.球体图像灰度变换模型及其在柑桔图像校正中的应用.农业工程学报,2004,20(4)：117-120.
[141]Gomez-Sanchis J, Molto E, Camps-Valls G, et al. Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of food engineering,2008,85(2):191-200.
[142]Kawano S, Haff R, Saranwong S. Methods for correcting morphological-based deficiencies in hyperspectral images of round objects. Journal of Near Infrared Spectroscopy,2011,19(6):431-441.
[143]李江波,饶秀勤,应义斌.水果表面亮度不均校正及单阈值缺陷提取研究.农业机械学报,2011,42(8)： 159-163.
[144]Gaston E, Frias J M, Cullen P J, et al. Prediction of Polyphenol Oxidase Activity Using Visible Near-Infrared Hyperspectral Imaging on Mushroom (Agaricus bisporus) Caps. Journal of Agricultural and Food Chemistry,2010,58(10):6226-6233.
[145]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展, 2004(04)：528-542.
[146]Candolfi A, Wu W, Massart D L, et al. Comparison of classification approaches applied to NIR-spectra of clinical study lots. Journal of pharmaceutical and biomedical analysis,1998,16(8):1329-1347.
[147]Zhang R, Ying Y, Rao X, et al. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. Journal of the Science of Food and Agriculture,2012,92(12): 2397-2408.
[148]刘晓.基于多光谱图像的柑橘糖度含量在线无损检测研究：[硕士学位论文].南昌：江西农业大学,2011.
[149]周竹,李小昱,高海龙等.漫反射和透射光谱检测马铃薯黑心病的比较.农业工程学报,2012,28(011)：237-242.