鲜切果品新鲜度可见/近红外快速检测装置设计与实验
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摘要
为了适应日益增长的鲜切果品消费需求,基于光谱分析技术和传感器技术,集成微型光谱仪、质量传感器、照度计和树莓派显示屏等电子元器件,设计了一套鲜切果品新鲜度可见光/近红外快速检测装置。硬件系统包括数据采集模块、光源模块、结果输出显示模块和控制器模块,软件实现处理数据、调用分级模型和反馈分级结果等功能。以鲜切苹果为例,采集24个红富士苹果样本在400～820 nm波段的光谱反射率,并在室温下分别于0～2 h、2. 5～8 h和8. 5～30 h 3个时间段,对每一个待测样品进行4组光谱数据测量,共获取288个原始样本数据。以切开时长2 h为分界线,将苹果样品分为2个等级。利用15点的S-G平滑卷积对反射光谱数据进行平滑处理后,使用核函数为高斯核函数(RBF)的支持向量机建立苹果新鲜度可见/近红外光谱检测分级模型,预测集准确率可达86. 81%。该鲜切果品新鲜度可见/近红外快速检测装置可为果品新鲜度或切后存放时长的快速无损检测提供方法与技术支持。
The consumption on the fresh-cut fruit is growing significantly, in order to satisfy the requirement of the consumption with high quality and accuracy quantity,a freshness detection device for fresh-cut fruit was developed using visible/near-infrared spectroscopy. The device was designed based on spectral analysis and sensor technology,which was integrated electronic components,including micro spectrometer,gravity sensor,illuminance meter and raspberry pie display. It was operated with hardware and software system,in which the hardware system included a data acquisition module,a light source module,a result output displays module,and a controller module. The software implements functions such as processing data,invoking a hierarchical model,and feedback grading results. Taking fresh-cut apples as an example,the spectral reflectance of 400 ～ 820 nm bands was collected from 24 red Fuji apple samples,which were in the range of 0 ～ 2 h,2. 5 ～ 8 h and 8. 5 ～ 30 h,respectively. The samples were measured for four sets of spectral data,and a total of 288 raw sample data were obtained. The apples were divided into two grades in a cut-off time of 2 h. After processing the reflected spectral data onto 15 points of S-G smooth convolution,the kernel function was used as the support vector machine of Gaussian kernel function( RBF) to establish the apple freshness visibility/near infrared spectrum detection hierarchical model. The accuracy of prediction set was 86. 81%. The freshness detection device for fresh-cut fruit using visible/near-infrared spectroscopy could provide a technical support for the freshness identification non-destructively and rapidly during the storage after cutting.
The consumption on the fresh-cut fruit is growing significantly, in order to satisfy the requirement of the consumption with high quality and accuracy quantity,a freshness detection device for fresh-cut fruit was developed using visible/near-infrared spectroscopy. The device was designed based on spectral analysis and sensor technology,which was integrated electronic components,including micro spectrometer,gravity sensor,illuminance meter and raspberry pie display. It was operated with hardware and software system,in which the hardware system included a data acquisition module,a light source module,a result output displays module,and a controller module. The software implements functions such as processing data,invoking a hierarchical model,and feedback grading results. Taking fresh-cut apples as an example,the spectral reflectance of 400 ～ 820 nm bands was collected from 24 red Fuji apple samples,which were in the range of 0 ～ 2 h,2. 5 ～ 8 h and 8. 5 ～ 30 h,respectively. The samples were measured for four sets of spectral data,and a total of 288 raw sample data were obtained. The apples were divided into two grades in a cut-off time of 2 h. After processing the reflected spectral data onto 15 points of S-G smooth convolution,the kernel function was used as the support vector machine of Gaussian kernel function( RBF) to establish the apple freshness visibility/near infrared spectrum detection hierarchical model. The accuracy of prediction set was 86. 81%. The freshness detection device for fresh-cut fruit using visible/near-infrared spectroscopy could provide a technical support for the freshness identification non-destructively and rapidly during the storage after cutting.
引文
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[14]刘文生,燕晓辉,李明珠.鸭梨褐变的近红外反射光谱分析[J].食品工业科技,2006(10):178-180,185.LIU Wensheng,YAN Xiaohui,LI Mingzhu.Near-infrared reflect spectral analysis of brown heart of"Ya Li"[J].Science and Technology of Food Industry,2006(10):178-180,185.(in Chinese)
[15]赵志磊,王艳伟,贡东军,等.近红外光谱的李果实褐变鉴别方法研究[J].光谱学与光谱分析,2016,36(7):2089-2093.ZHAO Zhilei,WANG Yanwei,GONG Dongjun,et al.Discrimination of plum browning with near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2016,36(7):2089-2093.(in Chinese)
[16]韩东海,刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88.HAN Donghai,LIU Xinxin,LU Chao,et al.Study on optical-nondestructive detection of breakdown apples[J].Transactions of the Chinese Society for Agricultural Machinery,2006,37(6):86-88.(in Chinese)
[17]KHATIWADA B P,SUBEDI P P,HAYES C,et al.Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy[J].Postharvest Biology and Technology,2016,120:103-111.
[18]张孔海,孙万慧,段鸿斌.果蔬食品的褐变与控制[J].农产品加工(学刊),2005(2):40-41.ZHANG Konghai,SUN Wanhui,DUAN Hongbin.Browning and control of fruit and vegetable[J].Academic Periodical of Farm Products Processing,2005(2):40-41.(in Chinese)
[19]ZOU Xiaobo,ZHAO Jiewen,MALCOLM J W.Variables selection methods in near-infrared spectroscopy[J].Analytica Chimica Acta,2010,667(1):14-32.
[20]ZHU Q,HE C,LU R,et al.Ripeness evaluation of‘Sun Bright’tomato using optical absorption and scattering properties[J].Postharvest Biology and Technology,2015,103(5):27-34.
[21]FRASER D G,KUNNEMEYER R,MCGLONE V A,et al.Letter to the editor[J].Augmentative&Alternative Communication,2001,11(4):191-194.
[22]王凡,李永玉,彭彦昆,等.基于漫透射光谱技术的番茄可溶性固形物及总糖含量的无损检测[J].光谱学与光谱分析,2016,36(10):3185-3189.WANG Fan,LI Yongyu,PENG Yankun,et al.Determination of tomato's SSC and TS based on diffuse transmittance spectroscopy[J].Spectroscopy and Spectral Analysis,2016,36(10):3185-3189.(in Chinese)
[23]郑立华,李民赞,安晓飞,等.基于近红外光谱和支持向量机的土壤参数预测[J].农业工程学报,2010,26(增刊2):81-87.ZHENG Lihua,LI Minzan,AN Xiaofei,et al.Forecasting soil parameters based on NIR and SVM[J].Transactions of the CSAE,2010,26(Supp.2):81-87.(in Chinese)
[24]林颢,赵杰文,陈全胜,等.近红外光谱结合一类支持向量机算法检测鸡蛋的新鲜度[J].光谱学与光谱分析,2010,30(4):929-932.LIN Hao,ZHAO Jiewen,CHEN Quansheng,et al.Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm[J].Spectroscopy and Spectral Analysis,2010,30(4):929-932.(in Chinese)
[2]徐文成.有机食品消费行为研究[D].杨凌:西北农林科技大学,2017.XU Wencheng.Study on the consumption behavior of organic food[D].Yangling:Northwest A&F University,2017.(in Chinese)
[3]罗海波,姜丽,余坚勇,等.鲜切果蔬的品质及贮藏保鲜技术研究进展[J].食品科学,2010,31(3):307-311.LUO Haibo,JIANG Li,YU Jianyong,et al.Current advances in preservation technology of fresh-cut fruits and vegetables[J].Food Science,2010,31(3):307-311.(in Chinese)
[4]李琪.鲜切苹果防褐变保鲜技术研究[D].天津:天津科技大学,2015.LI Qi.Study on technology of anti-browning and preservation of fresh-cut apple[D].Tianjin:Tianjin University of Science&Technology,2015.(in Chinese)
[5]HU W,SUN D W,BLASCO J.Rapid monitoring 1-MCP-induced modulation of sugars accumulation in ripening‘Hayward’kiwifruit by vis/NIR hyperspectral imaging[J].Postharvest Biology and Technology,2017,125:168-180.
[6]SHAO L,LIU W,ZHANG X,et al.Fabrication of electronic nose system and exploration on its applications in mango fruit(M.indica cv.Datainong)quality rapid determination[J].Journal of Food Measurement&Characterization,2017,11(4):1969-1977.
[7]KHURNPOON L,SIRISOMBOON P.Rapid evaluation of the texture properties of melon(Cucumis melo L.Var.reticulata cv.Green net)using near infrared spectroscopy[J].Journal of Texture Studies,2018,49(4):387-394.
[8]武太鹏,马康.无损快速检测技术在生鲜食品品质鉴定中的应用[J].食品安全质量检测学报,2017,8(3):729-736.WU Taipeng,MA Kang.Application of non-destructive rapid testing technology in quality identification of flesh food[J].Journal of Food Safety and Quality,2017,8(3):729-736.(in Chinese)
[9]王文秀,彭彦昆,孙宏伟,等.二维相关可见-近红外光谱结合支持向量机评价猪肉新鲜度[J].食品科学,2018,39(18):273-279.WANG Wenxiu,PENG Yankun,SUN Hongwei,et al.Evaluation of pork freshness using two-dimensional correlation visible/near-infrared spectroscopy combined with support vector machine[J].Food Science,2018,39(18):273-279.(in Chinese)
[10]王丽,励建荣.红外光谱技术在肉品品质鉴别中的应用[J].中国食品学报,2010,9(5):232-236.WANG Li,LI Jianrong.Application of infrared spectroscopy in meat quality identification[J].Journal of Chinese Institute of Food Science and Technology,2010,9(5):232-236.(in Chinese)
[11]杨晓玉,丁佳兴,房盟盟,等.基于可见/近红外高光谱成像技术的鸡蛋新鲜度无损检测[J].食品与机械,2017,33(11):131-136.YANG Xiaoyu,DING Jiaxing,FANG Mengmeng,et al.Non-destructive determination of eggs freshness by vis/NIRhyperspectral imaging technology[J].Food&Machinery,2017,33(11):131-136.(in Chinese)
[12]SCHMUTZLER M,HUCK C W.Automatic sample rotation for simultaneous determination of geographical origin and quality characteristics of apples based on near infrared spectroscopy(NIRS)[J].Vibrational Spectroscopy,2014,72:97-104.
[13]龚志远,李轶凡,刘燕德,等.基于可见近红外光谱的水果糖酸度分级控制[J].仪表技术与传感器,2015(8):73-75.GONG Zhiyuan,LI Yifan,LIU Yande,et al.Fruit sugar acidity degree separation control based on visible near infrared spectrum[J].Instrument Technique and Sensor,2015(8):73-75.(in Chinese)
[14]刘文生,燕晓辉,李明珠.鸭梨褐变的近红外反射光谱分析[J].食品工业科技,2006(10):178-180,185.LIU Wensheng,YAN Xiaohui,LI Mingzhu.Near-infrared reflect spectral analysis of brown heart of"Ya Li"[J].Science and Technology of Food Industry,2006(10):178-180,185.(in Chinese)
[15]赵志磊,王艳伟,贡东军,等.近红外光谱的李果实褐变鉴别方法研究[J].光谱学与光谱分析,2016,36(7):2089-2093.ZHAO Zhilei,WANG Yanwei,GONG Dongjun,et al.Discrimination of plum browning with near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2016,36(7):2089-2093.(in Chinese)
[16]韩东海,刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88.HAN Donghai,LIU Xinxin,LU Chao,et al.Study on optical-nondestructive detection of breakdown apples[J].Transactions of the Chinese Society for Agricultural Machinery,2006,37(6):86-88.(in Chinese)
[17]KHATIWADA B P,SUBEDI P P,HAYES C,et al.Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy[J].Postharvest Biology and Technology,2016,120:103-111.
[18]张孔海,孙万慧,段鸿斌.果蔬食品的褐变与控制[J].农产品加工(学刊),2005(2):40-41.ZHANG Konghai,SUN Wanhui,DUAN Hongbin.Browning and control of fruit and vegetable[J].Academic Periodical of Farm Products Processing,2005(2):40-41.(in Chinese)
[19]ZOU Xiaobo,ZHAO Jiewen,MALCOLM J W.Variables selection methods in near-infrared spectroscopy[J].Analytica Chimica Acta,2010,667(1):14-32.
[20]ZHU Q,HE C,LU R,et al.Ripeness evaluation of‘Sun Bright’tomato using optical absorption and scattering properties[J].Postharvest Biology and Technology,2015,103(5):27-34.
[21]FRASER D G,KUNNEMEYER R,MCGLONE V A,et al.Letter to the editor[J].Augmentative&Alternative Communication,2001,11(4):191-194.
[22]王凡,李永玉,彭彦昆,等.基于漫透射光谱技术的番茄可溶性固形物及总糖含量的无损检测[J].光谱学与光谱分析,2016,36(10):3185-3189.WANG Fan,LI Yongyu,PENG Yankun,et al.Determination of tomato's SSC and TS based on diffuse transmittance spectroscopy[J].Spectroscopy and Spectral Analysis,2016,36(10):3185-3189.(in Chinese)
[23]郑立华,李民赞,安晓飞,等.基于近红外光谱和支持向量机的土壤参数预测[J].农业工程学报,2010,26(增刊2):81-87.ZHENG Lihua,LI Minzan,AN Xiaofei,et al.Forecasting soil parameters based on NIR and SVM[J].Transactions of the CSAE,2010,26(Supp.2):81-87.(in Chinese)
[24]林颢,赵杰文,陈全胜,等.近红外光谱结合一类支持向量机算法检测鸡蛋的新鲜度[J].光谱学与光谱分析,2010,30(4):929-932.LIN Hao,ZHAO Jiewen,CHEN Quansheng,et al.Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm[J].Spectroscopy and Spectral Analysis,2010,30(4):929-932.(in Chinese)