水果品质无损检测方法研究进展

  • 王顺 ,
  • 黄星奕 ,
  • 吕日琴 ,
  • 潘思慧
展开
  • (江苏大学 食品与生物工程学院,江苏 镇江,212013)
硕士研究生(黄星奕教授为通讯作者,E-mail:h_xingyi@163.com)。

收稿日期: 2017-12-28

  网络出版日期: 2018-12-25

基金资助

国家重点研发计划项目(2017YFD0400100);江苏省重点研发计划(现代农业)(BE2015308)

Research progress of nondestructive detection methods in fruit quality

  • WANG Shun ,
  • HUANG Xing-yi ,
  • LYU Ri-qin ,
  • PAN Si-hui
Expand
  • (School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

Received date: 2017-12-28

  Online published: 2018-12-25

摘要

我国是水果生产大国,提高水果品质检测技术水平有助于提升我国水果产业的竞争力。食品农产品无损检测方法以其无损、快速、准确的特性,广泛应用于农产品品质检测,尤其是水果的检测研究中。该文综述了基于声学、电学等基本特性、基于智能感官仿生技术、光谱分析技术及高光谱成像新技术等的主要无损检测方法的研究进展,展望了无损检测方法在水果品质检测中的应用前景。

关键词: 无损检测; 水果; 品质

本文引用格式

王顺 , 黄星奕 , 吕日琴 , 潘思慧 . 水果品质无损检测方法研究进展[J]. 食品与发酵工业, 2018 , 44(11) : 319 -324 . DOI: 10.13995/j.cnki.11-1802/ts.016635

Abstract

Fruit quality is a key factor in marketing competency. China is one of the largest fruit producers in the world. Improving quality of fruit detection will strengthen the competitiveness of China′s fruit industry. Nondestructive detection methods for food and agricultural products were widely used in agricultural products due to their nondestructive, rapid and accurate characteristics. This paper summarizes the research progress of main nondestructive detection methods including acoustic and electrical methods as well as novel technologies in the field, such as intelligent sensory bionic technology, spectral analysis technology, hyperspectral imaging and so on. The prospect of nondestructive detection method in fruit quality detection is also proposed.

参考文献

[1] 吴雯. 只为一颗好水果:保障水果安全我们都做了什么[EB/OL]. [2016-06-4]. https://www.zg3n.com.cn/article-15826-1.html
[2] 中国产业信息网. 2016年我国水果行业产量、消费量以及供需平衡表预测[EB/OL]. http://www.chyxx.com/industry/201609/448941.html.
[3] 周秋瑜. 中国主要热带水果国际竞争力研究[D]. 海口:海南大学, 2011.
[4] 姚文苇. 基于超声医学应用的声传播的研究[J]. 中国医学物理学杂志, 2014, 31(4):5 077-5 080.
[5] 葛明. 基于小波变换的西瓜成熟度声学检测方法[D]. 西安:陕西师范大学, 2014.
[6] 张帅,史磊,张本华. 基于声学特性的香瓜成熟度检测方法[J]. 农机化研究, 2011, 33(10):126-129.
[7] MORRISON D S, ABEYRATNE U R. Ultrasonic technique for non-destructive quality evaluation of oranges[J]. Journal of Food Engineering, 2014, 141(141):107-112.
[8] SRIVASTAVA S, VADDADI S, SADISTAP S. Non-Contact ultrasonic based stiffness evaluation system for tomatoes during shelf-life storage[J]. Nutrition & Food Sciences, 2014, 4(2).
[9] 孔繁荣,郭文川. 发育后期苹果的介电特性与理化特性的关系[J]. 食品科学, 2016, 37(9):13-17.
[10] SOLTANI M, ALIMARDANI R, OMID M. Evaluating banana ripening status from measuring dielectric properties[J]. Journal of Food Engineering, 2011, 105(4):625-631.
[11] 郭文川. 基于介电特性的油桃糖度无损检测方法[J]. 农业工程学报. 2013, 29(17):257-264.
[12] 李子文,张海红,马雪莲,等. 灵武长枣的成熟度与其电学特性关系研究[J]. 食品科技, 2015(10):286-290.
[13] TRAFFANO-SCHIFFO M V, CASTRO-GIRALDEZ M, COLOM R J, et al. New spectrophotometric system to segregate tissues in mandarin fruit[J]. Food & Bioprocess Technology, 2017(3):1-8.
[14] 岳静. 仿生传感智能感官检测技术在食品感官评价中的应用及研究进展[J]. 中国调味品, 2013, 38(12):54-57.
[15] 赵杰文,刘少鹏,邹小波,等. 基于支持向量机的缺陷红枣机器视觉识别[J]. 农业机械学报, 2008, 39(3):113-115.
[16] 蒋益女,徐从富. 基于机器视觉的苹果质量等级识别方法的研究[J]. 计算机应用与软件, 2010, 27(11):99-101.
[17] SOFU M M, ER O, KAYACAN M C,et al. Design of an automatic apple sorting system using machine vision[J]. Computers & Electronics in Agriculture, 2016, 127:395-405.
[18] CÁRDENAS-PÉREZ S, CHANONA-PÉREZ J, MÉNDEZ- MÉNDEZ J V, et al. Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system[J]. Biosystems Engineering, 2017, 159:46-58.
[19] 叶晋涛,王运祥,杨杰,等. 哈密瓜颜色特征提取及成熟度分级的研究[J]. 石河子大学学报(自科版), 2016, 34(1):106-111.
[20] 徐赛,陆华忠,周志艳,等. 基于电子鼻的果园荔枝成熟阶段监测[J]. 农业工程学报, 2015(18):240-246.
[21] SANAEIFAR A, MOHTASEBI S S, GHASEMI-VARNAMKHASTI M, et al. Application of MOS based electronic nose for the prediction of banana quality properties[J]. Measurement, 2016, 82:105-114.
[22] 贺艳楠,魏永胜,郑颖. 水果成熟度无损检测技术研究进展[J]. 北方园艺,2010(3):208-212.
[23] OLAREWAJU O O, BERTLING I, MAGWAZA L S. Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models[J]. Scientia Horticulturae, 2016, 199:229-236.
[24] RUNGPICHAYAPICHET P, MAHAYOTHEE B, KHUWIJITJARU P, et al. Non-destructive determination of β-carotene content in mango by near-infrared spectroscopy compared with colorimetric measurements[J]. Journal of Food Composition & Analysis, 2015, 38:32-41.
[25] 郎雷. 水果糖度可见/近红外光谱检测仪的研发[D]. 杭州:浙江大学, 2016.
[26] 翟建龙. 基于android系统的脐橙品质近红外光谱无损检测技术[D]. 南昌:华东交通大学, 2015.
[27] DONG Jin-lei, GUO Wen-chuan. Nondestructive determination of apple internal qualities using near-infrared hyperspectral reflectance imaging[J]. Food Analytical Methods, 2015, 8(10):1-12.
[28] 詹映,彭云发,彭海根,等. 近红外光谱在南疆红枣糖度无损检测中的应用[J]. 农机化研究, 2014(6):179-183.
[29] 孙通. 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[D]. 杭州:浙江大学, 2011.
[30] RUNGPICHAYAPICHET P, MAHAYOTHEE B, NAGLE M, et al. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango[J]. Postharvest Biology & Technology, 2016, 111:31-40.
[31] 罗枫,鲁晓翔,张鹏,等. 冷藏过程中樱桃VC含量的近红外检测[J]. 食品与发酵工业, 2015, 41(5):173-176.
[32] MUNAWAR A A, HÖRSTEN D V, WEGENER J K, et al. Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics[J]. Engineering in Agriculture Environment & Food, 2015, 9(3):208-215.
[33] BLAKEY R J. Evaluation of avocado fruit maturity with a portable near-infrared spectrometer[J]. Postharvest Biology & Technology, 2016, 121(2016):101-105.
[34] 赵凡. 基于高光谱图像技术无损检测猕猴桃的内部品质[D]. 杨凌:西北农林科技大学, 2016.
[35] RAJKUMAR P, WANG N, EIMASRY G, et al. Studies on banana fruit quality and maturity stages using hyperspectral imaging[J]. Journal of Food Engineering, 2012, 108(1):194-200.
[36] PAN Lei-qing, ZHANG Qiang, ZHANG Wei, et al. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network[J]. Food Chemistry, 2016, 192:134-41.
[37] 黄星奕,辛君伟,赵杰文,等. 可视化传感技术在桃子质量评价中的应用[J]. 江苏大学学报自然科学版, 2009, 30(5):433-436.
[38] KALNE A, KOTWALIWALE N, SINGH K, et al. Non-destructive inspection of mango fruit using digital radiography, computed tomography and magnetic resonance imaging[J]. Indian Society of Agricultural Engineers, 2013, 49(4):33-41.
[39] VELASCO L R I, MEDINA C. Soft x-ray imaging for non-destructive detection of the mango pulp weevil Sternochetus frigidus (Fabr.) infestation in fresh green 'Carabao' mango fruits[J]. Thesis-The University of Texas at Austin, 2004, 44(4):716-717.
[40] 周小芳,方炎,张鹏翔. 水果表面残留农药的拉曼光谱研究[C]//全国光散射学术会议, 2003.
[41] MCGLONE V A, JORDAN R B. Kiwifruit and apricot firmness measurement by the non-contact laser air-puff method[J]. Postharvest Biology & Technology, 2000, 19(1):47-54.
[42] ZUDE M. Detection of fruit tissue browning using laser-induced fluorescence spectroscopy[J]. Acta Horticulturae, 2003, 628(628):85-90.
[43] 向春燕,樊丽,亢键,等. 低温胁迫下嘎拉苹果果实叶绿素荧光参数与其他生理和品质指标的相关性[J]. 西北农业学报, 2014, 23(8):135-141.
[44] 周建民,周其显,刘燕德. 红外热成像技术在农业生产中的应用[J]. 农机化研究, 2010, 32(2):1-4.
[45] 周建民,刘娟娟,舒丽丽. 苹果表面缺陷红外热成像检测装置及处理方法[J]. 中国农机化学报, 2012(2):113-115.
[46] DANNO A, MIYAZATO M, ISHIGURO E. Quality evaluation of agricultural products by infrared imaging method. III. Maturity evaluation of fruits and vegetables.[J]. Memoirs of the Faculty of Agriculture Kagoshima University, 1980, 16:157-164.
[47] 陈路平,高杨文,杨培强,等. 低场核磁共振成像分析技术与应用[J]. 现代科学仪器, 2014(6):60-64.
[48] 王梦娇,杨菊梅,王松磊,等. 基于低场核磁共振技术冷鲜羊肉品质快速无损检测研究[J]. 宁夏工程技术, 2017, 16(1):10-14.
[49] 庞林江,王允祥,何志平,等. 核磁共振技术在水果品质检测中的应用[J]. 农机化研究, 2006(8):176-180.
文章导航

/