光电检测技术在马铃薯品质检测中的研究进展
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摘要
近年来,我国大力推动实施马铃薯主粮化战略,品质控制是重要的支撑技术,需在马铃薯的专用品种选育、主食产品加工、储藏运输及终端消费等多个产业链环节进行检测。为此,围绕马铃薯质量安全管控方面的需求,详细介绍了机器视觉技术、近红外光谱分析技术及高光谱成像技术等光电检测技术在马铃薯外观形态、内部品质及缺陷检测方面的研究和应用现状,并分析了其发展趋势,以期为马铃薯深加工过程中原料筛选和分等分级等提供参考。
In recent years,China vigorously promote the implementation of the potato staple food strategy,quality control is the key technology in this strategy,including special breeding,product processing,storage and transportation,food consumption and many other industry chain links,test is very important is these chains. This paper focused on the potato quality and safety control requirements,introduced three photoelectric detection technologies including the machine vision technology,near infrared spectroscopy analysis technology,and hyperspectral imaging technology. And introduced their application status in the detection of potato morphology,internal quality inspection and defect,and analyzed its development trend,in order to provide reference for potato deep processing in the selection of raw materials,classification etc.
In recent years,China vigorously promote the implementation of the potato staple food strategy,quality control is the key technology in this strategy,including special breeding,product processing,storage and transportation,food consumption and many other industry chain links,test is very important is these chains. This paper focused on the potato quality and safety control requirements,introduced three photoelectric detection technologies including the machine vision technology,near infrared spectroscopy analysis technology,and hyperspectral imaging technology. And introduced their application status in the detection of potato morphology,internal quality inspection and defect,and analyzed its development trend,in order to provide reference for potato deep processing in the selection of raw materials,classification etc.
引文
[1]王秀丽,马云倩,郭燕枝,等.马铃薯的世界传播及对中国主食产业开发的启示[J].中国农学通报,2016,32(35):227-231.
[2] Deck S H,Morrow C T,Heinemann P H,et al. Comparison of a neural network and traditional classifier for machine vision inspection of potatoes[J]. Applied engineering in agriculture,1995,11(2):319-326.
[3] Tao Y,Chance L. Full scale fruit vision sorting system design-factors and considerations[C]//Proceedings of the FPAC IV Conference,1995:14-22.
[4] Heinemann P H,Pathare N P,Morrow C T. An automated inspection station for machine-vision grading of potatoes[J]. Machine Vision and Applications,1996,9:14-19.
[5] Noordam J C,Otten G W,Timmermans A J M,et al. High speed potato grading and quality inspection based on a color vision system[C]//Proceedings of SPIE,2000:206-217.
[6]郑冠楠,谭豫之,张俊雄,等.基于计算机视觉的马铃薯自动检测分级[J].农业机械学报,2009,40(4):166-168,156.
[7]郝敏,麻硕士,郝小冬.基于Zernike矩的马铃薯薯形检测[J].农业工程学报,2009,26(2):347-350.
[8]郝敏.基于机器视觉的马铃薯外部品质检测技术研究[D].呼和浩特:内蒙古农业大学,2009.
[9]李锦卫,廖桂平,金晶,等.基于灰度截留分割与十色模型的马铃薯表面缺陷检测方法[J].农业工程学报,2010,26(10):236-242.
[10]王泽京.基于机器视觉的马铃薯自动检测分级研究[D].兰州:甘肃农业大学,2011.
[11]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012(7):178-183.
[12]王红军,熊俊涛,黎邹邹,等.基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J].农业工程学报,2016,32(8):272-277.
[13]田芳,彭彦昆,魏文松,等.基于机器视觉的马铃薯黑心病检测机构设计与试验[J].农业工程学报,2017,33(5):287-294.
[14] Krivoshiev G P,Chalucova R P,Moukarev M I. A Possibility for Elimination of the Interference from the Peel in Nondestructive Determination of die Internal Quality of Fruit and Vegetables by VIS/NIR Spectroscopy,2000,133(5):344-353.
[15] Chalucova R P,Krivoshiev G P,Bojilov P. Monitoring the Internal Quality of Potatoes by NIR Transmission and Reflection Measurement[C]//NIR-international Conference on Neer-infrared Speatroscopy,2000:68-70.
[16] Kang S,Lee K-J,Choi W,et al. A Near-Infrared Sensing Technique for Measuring the Quality of Potatoes[C]//In:2003 ASAE Annual International Meeting,Las Vegas,Nevada,USA,2003.
[17] Haase N U. Rapid Estimation of Potato Tuber Quality by Near-Infrared Spectroscopy[J]. Starch/Starke,2006,58:268-273.
[18]秦华俊.近红外光谱分析技术在食品、饲料中的应用研究[D].南昌:南昌大学,2007.
[19]刘翠翠,高红秀,李赞,等.马铃薯块茎钾含量近红外模型的建立[J].中国马铃薯,2011,25(2):65-71.
[20]张小燕,杨炳南,刘威,等.马铃薯主要营养成分的近红外光谱分析[J].食品科学,2013,34(2):165-169.
[21]李鑫.基于近红外光谱的马铃薯品种鉴别及干物质含量检测方法研究[D].大庆:黑龙江八一农垦大学,2016.
[22]姜微,房俊龙,王树文,等. CARS-SPA算法结合高光谱检测马铃薯还原糖含量[J].东北农业大学学报,2016,47(2):88-95.
[23] Gowen A A,O'Donnell C P,Cullen P J,et al. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control[J]. Trends in Food Science&Technology,2007,18:590-598.
[24] Angel D N,Arno F,Pilar C,et al. Common scab detection on potato using an infrared hyperspectral imaging system[J]. Image analysis and processing,2011,6979:303-312.
[25] Nguyen D N,Tsuta M,Nicolai B M,et al. Prediction of optional cooking time for boiled potatoes by hyperspectral imaging[J]. Journal of Food Engineering,2011,105(4):617-624.
[26]周竹,李小昱,陶海龙,等.基于高光谱成像技术的马铃薯外部缺陷检测[J].农业工程学报,2012,28(21):221-228.
[27]吴佳.基于高光谱成像技术的马铃薯薯形检测与算法研究[D].银川:宁夏大学,2013.
[28]张然.基于高光谱成像技术的马铃薯外部损伤识别研究[D].银川:宁夏大学,2013.
[29]苏文浩,刘贵珊,何建国,等.高光谱图像技术结合图像处理方法检测马铃薯外部缺陷[J].浙江大学学报:农业与生命科学版,2014,40(2):188-196.
[30]史崇升.基于高光谱成像技术的马铃薯外部品质无损检测建模及优化研究[D].银川:宁夏大学,2014.
[31]金瑞,李小昱,颜伊芸,等.基于高光谱图像和光谱信息融合的马铃薯多指标检测方法[J].农业工程学报,2015,31(16):258-263.
[32]邓建猛,王红军,黎邹邹,等.基于高光谱技术的马铃薯外部品质检测[J].食品与机械,2016,32(11):122-125,211.
[33]李小昱,库静,颜伊芸,等.基于高光谱成像的绿皮马铃薯检测方法[J].农业机械学报,2016,47(3):228-233.
[34]吴晨,何建国,贺晓光,等.基于近红外高光谱成像技术的马铃薯淀粉含量无损检测[J].河南工业大学学报:自然科学版,2014,35(5):11-16.
[35]吴晨,何建国,刘贵珊,等.基于近红外高光谱成像技术的马铃薯干物质含量无损检测[J].食品与机械,2014b,30(4):133-136,150.
[36]宋娟,吴晨.基于高光谱成像技术的马铃薯多种营养成分同时检测[J].河南工业大学学报:自然科学版,2016,37(1):60-66,77.
[37]姜微.高光谱技术在马铃薯品种鉴别及品质无损检测中的应用研究[D].哈尔滨:东北农业大学,2017.
[38]高海龙,李小昱,徐森淼,等.马铃薯黑心病和单薯质量的透射高光谱检测方法[J].农业工程学报,2013(15):279-285.
[39]黄涛,李小昱,徐梦玲,等.半透射高光谱成像技术与支持向量机的马铃薯空心病无损检测研究[J].光谱学与光谱分析,2015(1):198-202.
[40]黄涛.基于半透射高光谱成像技术的马铃薯内外部缺陷检测方法研究[D].武汉:华中农业大学,2015.
[41]王丽艳,薛河儒,姜新华,等.基于高光谱图像技术的马铃薯种类的鉴别[J].内蒙古农业大学学报:自然科学版,2016,37(2):102.
[42]李小昱,陶海龙,高海龙,等.基于多源信息融合技术的马铃薯痂疮病无损检测方法[J].农业工程学报,2013,29(19):277-284.
[43]陈争光,李鑫,范学佳.基于可见近红外光谱分析技术的马铃薯品种鉴别方法[J].光谱学与光谱分析,2016,36(8):2474-2478.
[44]代芬,Mads Sylvest Bergholt,Arnold Julian Vinoj Benjamin,等.近红外激发荧光光谱与拉曼光谱快速鉴别马铃薯品种[J].光谱学与光谱分析,2014,34(3):677-680.
[2] Deck S H,Morrow C T,Heinemann P H,et al. Comparison of a neural network and traditional classifier for machine vision inspection of potatoes[J]. Applied engineering in agriculture,1995,11(2):319-326.
[3] Tao Y,Chance L. Full scale fruit vision sorting system design-factors and considerations[C]//Proceedings of the FPAC IV Conference,1995:14-22.
[4] Heinemann P H,Pathare N P,Morrow C T. An automated inspection station for machine-vision grading of potatoes[J]. Machine Vision and Applications,1996,9:14-19.
[5] Noordam J C,Otten G W,Timmermans A J M,et al. High speed potato grading and quality inspection based on a color vision system[C]//Proceedings of SPIE,2000:206-217.
[6]郑冠楠,谭豫之,张俊雄,等.基于计算机视觉的马铃薯自动检测分级[J].农业机械学报,2009,40(4):166-168,156.
[7]郝敏,麻硕士,郝小冬.基于Zernike矩的马铃薯薯形检测[J].农业工程学报,2009,26(2):347-350.
[8]郝敏.基于机器视觉的马铃薯外部品质检测技术研究[D].呼和浩特:内蒙古农业大学,2009.
[9]李锦卫,廖桂平,金晶,等.基于灰度截留分割与十色模型的马铃薯表面缺陷检测方法[J].农业工程学报,2010,26(10):236-242.
[10]王泽京.基于机器视觉的马铃薯自动检测分级研究[D].兰州:甘肃农业大学,2011.
[11]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012(7):178-183.
[12]王红军,熊俊涛,黎邹邹,等.基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J].农业工程学报,2016,32(8):272-277.
[13]田芳,彭彦昆,魏文松,等.基于机器视觉的马铃薯黑心病检测机构设计与试验[J].农业工程学报,2017,33(5):287-294.
[14] Krivoshiev G P,Chalucova R P,Moukarev M I. A Possibility for Elimination of the Interference from the Peel in Nondestructive Determination of die Internal Quality of Fruit and Vegetables by VIS/NIR Spectroscopy,2000,133(5):344-353.
[15] Chalucova R P,Krivoshiev G P,Bojilov P. Monitoring the Internal Quality of Potatoes by NIR Transmission and Reflection Measurement[C]//NIR-international Conference on Neer-infrared Speatroscopy,2000:68-70.
[16] Kang S,Lee K-J,Choi W,et al. A Near-Infrared Sensing Technique for Measuring the Quality of Potatoes[C]//In:2003 ASAE Annual International Meeting,Las Vegas,Nevada,USA,2003.
[17] Haase N U. Rapid Estimation of Potato Tuber Quality by Near-Infrared Spectroscopy[J]. Starch/Starke,2006,58:268-273.
[18]秦华俊.近红外光谱分析技术在食品、饲料中的应用研究[D].南昌:南昌大学,2007.
[19]刘翠翠,高红秀,李赞,等.马铃薯块茎钾含量近红外模型的建立[J].中国马铃薯,2011,25(2):65-71.
[20]张小燕,杨炳南,刘威,等.马铃薯主要营养成分的近红外光谱分析[J].食品科学,2013,34(2):165-169.
[21]李鑫.基于近红外光谱的马铃薯品种鉴别及干物质含量检测方法研究[D].大庆:黑龙江八一农垦大学,2016.
[22]姜微,房俊龙,王树文,等. CARS-SPA算法结合高光谱检测马铃薯还原糖含量[J].东北农业大学学报,2016,47(2):88-95.
[23] Gowen A A,O'Donnell C P,Cullen P J,et al. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control[J]. Trends in Food Science&Technology,2007,18:590-598.
[24] Angel D N,Arno F,Pilar C,et al. Common scab detection on potato using an infrared hyperspectral imaging system[J]. Image analysis and processing,2011,6979:303-312.
[25] Nguyen D N,Tsuta M,Nicolai B M,et al. Prediction of optional cooking time for boiled potatoes by hyperspectral imaging[J]. Journal of Food Engineering,2011,105(4):617-624.
[26]周竹,李小昱,陶海龙,等.基于高光谱成像技术的马铃薯外部缺陷检测[J].农业工程学报,2012,28(21):221-228.
[27]吴佳.基于高光谱成像技术的马铃薯薯形检测与算法研究[D].银川:宁夏大学,2013.
[28]张然.基于高光谱成像技术的马铃薯外部损伤识别研究[D].银川:宁夏大学,2013.
[29]苏文浩,刘贵珊,何建国,等.高光谱图像技术结合图像处理方法检测马铃薯外部缺陷[J].浙江大学学报:农业与生命科学版,2014,40(2):188-196.
[30]史崇升.基于高光谱成像技术的马铃薯外部品质无损检测建模及优化研究[D].银川:宁夏大学,2014.
[31]金瑞,李小昱,颜伊芸,等.基于高光谱图像和光谱信息融合的马铃薯多指标检测方法[J].农业工程学报,2015,31(16):258-263.
[32]邓建猛,王红军,黎邹邹,等.基于高光谱技术的马铃薯外部品质检测[J].食品与机械,2016,32(11):122-125,211.
[33]李小昱,库静,颜伊芸,等.基于高光谱成像的绿皮马铃薯检测方法[J].农业机械学报,2016,47(3):228-233.
[34]吴晨,何建国,贺晓光,等.基于近红外高光谱成像技术的马铃薯淀粉含量无损检测[J].河南工业大学学报:自然科学版,2014,35(5):11-16.
[35]吴晨,何建国,刘贵珊,等.基于近红外高光谱成像技术的马铃薯干物质含量无损检测[J].食品与机械,2014b,30(4):133-136,150.
[36]宋娟,吴晨.基于高光谱成像技术的马铃薯多种营养成分同时检测[J].河南工业大学学报:自然科学版,2016,37(1):60-66,77.
[37]姜微.高光谱技术在马铃薯品种鉴别及品质无损检测中的应用研究[D].哈尔滨:东北农业大学,2017.
[38]高海龙,李小昱,徐森淼,等.马铃薯黑心病和单薯质量的透射高光谱检测方法[J].农业工程学报,2013(15):279-285.
[39]黄涛,李小昱,徐梦玲,等.半透射高光谱成像技术与支持向量机的马铃薯空心病无损检测研究[J].光谱学与光谱分析,2015(1):198-202.
[40]黄涛.基于半透射高光谱成像技术的马铃薯内外部缺陷检测方法研究[D].武汉:华中农业大学,2015.
[41]王丽艳,薛河儒,姜新华,等.基于高光谱图像技术的马铃薯种类的鉴别[J].内蒙古农业大学学报:自然科学版,2016,37(2):102.
[42]李小昱,陶海龙,高海龙,等.基于多源信息融合技术的马铃薯痂疮病无损检测方法[J].农业工程学报,2013,29(19):277-284.
[43]陈争光,李鑫,范学佳.基于可见近红外光谱分析技术的马铃薯品种鉴别方法[J].光谱学与光谱分析,2016,36(8):2474-2478.
[44]代芬,Mads Sylvest Bergholt,Arnold Julian Vinoj Benjamin,等.近红外激发荧光光谱与拉曼光谱快速鉴别马铃薯品种[J].光谱学与光谱分析,2014,34(3):677-680.