Progress of Non-destructive Testing Technology in Mango Quality

LIU Caihua; LI Xi; ZHU Zhengjie; BAO Zhujun; XIE Xiaona; YANG Zhengzhou; WANG Yun; LIU Fang

doi:10.13386/j.issn1002-0306.2020090195

Volume 42 Issue 22

Nov. 2021

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Science and Technology of Food Industry > 2021 > 42(22): 413-422. > DOI: 10.13386/j.issn1002-0306.2020090195

LIU Caihua, LI Xi, ZHU Zhengjie, et al. Progress of Non-destructive Testing Technology in Mango Quality[J]. Science and Technology of Food Industry, 2021, 42(22): 413−422. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020090195.

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Progress of Non-destructive Testing Technology in Mango Quality

College of Agriculture and Food Engineering, Baise University, Baise 533000, China

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Received Date: September 20, 2020
Available Online: September 07, 2021

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Abstract

Abstract

Mango is one of the five most popular fruits in the world. The nondestructive testing of mango quality is beneficial to the scientific treatment of mango after harvest, and has important significance for improving its quality and market competitiveness. In this paper, the research progress of computer vision and visible/near infrared spectroscopy in mango quality detection is mainly summarized. The use of other non-destructive technology for assessing mango quality, such as electronic nose, ultrasonic testing, hyperspectral testing, mechanical sensing are also discussed. The differences of non-destructive technology for mango quality research at home and abroad are compared, and the existing problems are summarized. Furthermore, the development trend of computer vision technology and visible/near infrared spectroscopy technology in mango quality inspection and quality control in the whole process are discussed. This paper provides reference for the development and application of non-destructive technology in mango quality assessing.
- mango,
- fruit quality,
- nondestructive testing,
- computer vision technology,
- visible/near infrared spectroscopy

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[1]	韦家少. 世界芒果产业发展概况[J]. 中国热带农业,2005,5:23−25. [WEI J S. Development of mango industry in the world[J]. China Tropical Agriculture,2005,5:23−25. doi: 10.3969/j.issn.1673-0658.2005.06.011
[2]	贺梅英, 赵萍, 林依颖. 越南芒果在我国的市场竞争力分析[J]. 南方农业学报,2020,51(3):722−728. [HE M Y, ZHAO P, LIN Y Y. Competitiveness of Vietnamese mango in the Chinese market[J]. Journal of Southern Agriculture,2020,51(3):722−728. doi: 10.3969/j.issn.2095-1191.2020.03.031
[3]	JHA S, NARSAIAH K, SHARMA A, et al. Quality parameters of mango and potential of non-destructive techniques for their measurement-a review[J]. Jounal of Food Science and Technology,2010,47(1):1−14. doi: 10.1007/s13197-010-0004-6
[4]	赵家桔.芒果品质构成及其发展规律的研究[D]. 杭州: 浙江大学, 2010. ZHAO J J. Study on the development of Mango (Mangifera indica L.) fruit quality characteristics[D]. Hangzhou: Zhejiang Univerity, 2010.
[5]	辛明, 张娥珍, 何全光, 等. 芒果果实品质评价因子的选择[J]. 南方农业学报,2014,45(10):1818−1824. [XIN M, ZHANG E Z, HE Q G, et al. Selection of evaluation factors for mango fruit quality[J]. Journal of Southern Agriculture,2014,45(10):1818−1824. doi: 10.3969/j:issn.2095-1191.2014.10.1818
[6]	冯建英, 李鑫, 原变鱼, 等. 智能感官技术在水果检测中的应用进展及趋势[J]. 南方农业学报,2020,51(3):636−644. [FENG J, LI X, YUAN B Y, et al. Progress and trend of fruit detection by intelligent sensory technology[J]. Journal of Southern Agriculture,2020,51(3):636−644. doi: 10.3969/j.issn.2095-1191.2020.03.021
[7]	崔秀帅, 唐荣年, 杨举华, 等. 芒果无损检测方法的研究现状及发展趋势[J]. 贵州农业科学,2013,41(7):167−170. [CUI X S, TANG R N, YANG J H, et al. Current situation and developing trend of nondestructive detecting methods for mango[J]. Guizhou Agricultural Science,2013,41(7):167−170. doi: 10.3969/j.issn.1001-3601.2013.07.046
[8]	WANG H, PENG J, XIE C, et al. Fruit quality evaluation using spectroscopy technology: A review[J]. Sensors,2015,15:11889−1192. doi: 10.3390/s150511889
[9]	徐赛, 孙潇鹏, 张倩倩. 大型厚皮水果的无损检测技术研究[J]. 农产品质量与安全,2019,5:30−35, 48. [XU S, SUN X P, ZHANG Q Q. Research progress on nondestructive testing technology applied to large thick-skinned fruit[J]. Quality and Safety of Agro-products,2019,5:30−35, 48. doi: 10.3969/j.issn.1674-8255.2019.01.006
[10]	罗印斌, 蔡艳丽, 兰菡, 等. 农产品无损检测方法应用现状[J]. 食品工业科技,2018,39(15):340−344. [LUO Y B, CAI Y L, LAN H, et al. Application status of nondestructive testing methods for agricultural products[J]. Science and Technology of Food Industry,2018,39(15):340−344.
[11]	王顺, 黄星奕, 吕日琴, 等. 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. [WANG S, HUANG X Y, LV R Q, et al. Research progress of nondestructive detection methods in fruit quality[J]. Food and Fermentation Industries,2018,44(11):319−324.
[12]	李国进, 董第永, 陈双. 基于计算机视觉的芒果检测与分级研究[J]. 农机化研究,2015,37(10):13−18. [LI G J, DONG D Y, CHEN S. Research on Mango detection and classification by computer vision[J]. Agricultural Mechanization Research,2015,37(10):13−18. doi: 10.3969/j.issn.1003-188X.2015.10.004
[13]	王江枫, 罗锡文, 洪添胜, 等. 计算机视觉技术在芒果重量及果面坏损检测中的应用[J]. 农业工程学报,1998,14(4):186−189. [WANG J F, LUO X W, HONG T S, et al. Application of computer vision technology in detecting mango weight and surface bruise[J]. Transactions of the Chinese Society of Agricultural Engineering,1998,14(4):186−189. doi: 10.3321/j.issn:1002-6819.1998.04.038
[14]	张立华. 基于计算机视觉的芒果表面缺陷检测方法研究[D]. 南宁: 广西大学, 2006. ZHANG L H. Defect detection and classification of mango’s surface based on computer vision[D]. Nanning: Guangxi University, 2006.
[15]	张烈平, 曾爱群, 陈婷. 基于计算机视觉和神经网络的芒果检测与等级分类[J]. 农机化研究,2008,30(10):57−60. [ZHANG L P, ZENG AI Q, CHEN T. Mango examination and rank classification research based on computer vision and neural network[J]. Agricultural Mechanization Research,2008,30(10):57−60. doi: 10.3969/j.issn.1003-188X.2008.10.017
[16]	刘静, 黄勇平, 章程辉. 视觉系统开发模块在芒果果面缺陷检测中的应用[J]. 食品与机械,2009,25(2):82−85. [LIU J, HUANG Y P, ZHANG C H. Application of NI vision development module in mango surface defect detection[J]. Food & Machinery,2009,25(2):82−85.
[17]	Pham V H, Lee B R. An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm[J]. Vietnam Journal of Computer Science,2015,2(1):25−33. doi: 10.1007/s40595-014-0028-3
[18]	Ashok V, Vinod D S. A comparative study of feature extraction methods in defect classification of mangoes using neural network[C]// 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysore, IEEE, 2016: 1−6.
[19]	徐玉琼, 娄柯. 基于计算机视觉的芒果自动分级方法设计[J]. 荆楚理工学院学报,2019,34(3):24−29, 44. [XU Y Q, LOU K. Design of mango automatic grading method based on computer vision[J]. Journal of Jingchu University of Technology,2019,34(3):24−29, 44. doi: 10.3969/j.issn.1008-4657.2019.03.004
[20]	张光龙. 基于芒果流水线的智能化检测分级系统研究[D]. 成都: 成都大学, 2020. ZHANG G L. Research of mango intel-ligent detection and classification system based on pipeline[D]. Chengdu: Chengdu University, 2020.
[21]	KUMARI N, KR BHATT A, KR DWIVEDI R, et al. Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer[J]. Multimedia Tools and Applications, 2020, https://doi.org/10.1007/s11042-020-09747-z.
[22]	孙树亮. 基于计算机视觉的芒果分类[D]. 南宁: 广西大学, 2006. SUN S L. Mango classification based on computer vision[D]. Nanning: Guangxi University, 2006.
[23]	SA’AD F, IBRAHIM M, SHAKAFF A, et al. Shape and weight grading of mangoes using visible imaging[J]. Computers and Electronics in Agriculture,2015,115:51−56. doi: 10.1016/j.compag.2015.05.006
[24]	IBRAHIM M, SA’AD F, ZAKARIA A, et al. In-Line sorting of Harumanis mango based on external quality using visible imaging[J]. Sensors,2016,16:1753. doi: 10.3390/s16111753
[25]	MAKINO Y, GOTO K, OSHITA S, et al. A grading method for mangoes on the basis of peel color measurement using a computer vision system[J]. Agricultural Sciences,2016,7:327−334. doi: 10.4236/as.2016.76033
[26]	NANDI C S, TUDU B, COLEY C, et al. A machine vision technique for grading of harvested mangoes based on maturit and quality[J]. IEEE Sensors Journal,2016,16(16):6387−6396. doi: 10.1109/JSEN.2016.2580221
[27]	辛华健. 计算机视觉在芒果品质检测中的应用研究[J]. 农机化研究,2019(9):190−193. [XIN H. Application of computer vision in mango quality testing[J]. Agricultural Mechanization Research,2019(9):190−193. doi: 10.3969/j.issn.1003-188X.2019.01.034
[28]	向阳, 林洁雯, 李亚军, 等. 芒果双面成熟度在线检测分级系统[J]. 农业工程学报,2019,35(10):259−266. [XIANG Y, LIN J W, LI Y J, et al. Mango double-sided maturity online detection and classification system[J]. Transactions of the Chinese Society of Agricultural Engineering,2019,35(10):259−266. doi: 10.11975/j.issn.1002-6819.2019.10.033
[29]	ANUREKHA D, SANKARAN R. Efficient classification and grading of Mangoes wih ganfis for improved performance[J]. Multimedia Tools and Applications,2020,79:4169−4184. doi: 10.1007/s11042-019-07784-x
[30]	PAYNE A, WALSH K, SUBEDI P, et al. Estimation of mango crop yield using image analysis-segmentation method[J]. Computers and Electronics in Agriculture,2013,91:57−64. doi: 10.1016/j.compag.2012.11.009
[31]	薛月菊, 黄宁, 涂淑琴, 等. 未成熟芒果的改进YOLOv2识别方法[J]. 农业工程学报,2018,34(7):173−179. [XUE Y J, HUANG N, TU S Q, et al. Immature mango detection based on improved YOLOv2[J]. Transactions of the Chinese Society of Agricultural Engineering,2018,34(7):173−179. doi: 10.11975/j.issn.1002-6819.2018.07.022
[32]	熊俊涛, 刘振, 林睿, 等. 自然环境下树上绿色芒果的无人机视觉检测技术[J]. 农业机械学报,2018,49(11):24−29. [XIONG J T, LIU Z, LIN R, et al. Unmanned aerial vehicle vision detection technology of green Mango on tree in natural Environment[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(11):24−29.
[33]	BEHERA S K, SANGITA S, RATH A K, et al. Advances in computer, communication and control[M]. Singapore: Springer International Publishing, 2019.
[34]	SAHANA M, ANITA H B. Automatic classification of south Indian regional fruits using image processing[J]. Indian Journal of Science and Technology, 2017, 10(13): 1−4.
[35]	ANUREKHA D, SANKARAN R A. Efficient classification and grading of MANGOES with GANFIS for improved performance[J]. Multimedia Tools and Applications, 2020, 79: 4169–4184.
[36]	HASNA H M, HELAL S M, SHAMIM R M, et al. Smart Trends in Computing and Communications: Proceedings of Smart Com 2020 [M]. Singapore: Springer International Publishing, 2020.
[37]	苏东林, 李高阳, 何建新, 等. 近红外光谱分析技术在我国大宗水果品质无损检测中的应用研究进展[J]. 食品工业科技,2012,33(6):460−464. [SU D L, LI G Y, HE J X, et al. Progress in application of near infrared spectroscopy to nondestructive detection of big yield fruits’quality in China[J]. Science and Technology of Food Industry,2012,33(6):460−464.
[38]	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的研究[J]. 光谱学与光谱分析,2008,28(12):2839−2842. [YU J J, HE Y, BAO Y D. Nondestructive test on predicting sugar content and valid acidity of mango by spectroscopy technology[J]. Spectroscopy and Spectral Analysis,2008,28(12):2839−2842. doi: 10.3964/j.issn.1000-0593(2008)12-2839-04
[39]	屠振华, 籍保平, 孟超英, 等. 芒果内部品质的CCD-NIR无损检测试验研究[J]. 光谱学与光谱分析,2008,28(10):111−112. [TU Z H, JI B P, MENG C Y, et al. Non-destructive determination of internal quality of mango by CCD-NIR spectroscopy[J]. Spectroscopy and Spectral Analysis,2008,28(10):111−112.
[40]	JHA S, JAISWAL P, NARSAIAH K, et al. Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy[J]. Scientia Horticulturae,2012,138:171−175. doi: 10.1016/j.scienta.2012.02.031
[41]	曹霞, 周学成, 范品良. 基于近红外漫反射光谱技术的芒果糖度无损检测方法研究[J]. 农机化研究,2013(1):177−180. [CAO X, ZHOU X C, FAN P L. Nondestructive detection on predicting sugar content of mango by near-infrared diffuse renectance[J]. Agricultural Mechanization Research,2013(1):177−180. doi: 10.3969/j.issn.1003-188X.2013.01.043
[42]	THEANJUMPOL P, SELF G, RITTIRON R, et al. Selecting variables for near infrared spectroscopy (NIRS) evaluation of mango fruit quality[J]. Journal of Agricultural Science,2013,5(7):146−159.
[43]	WATANAWAN C, WASUSRI T, SRILAONG V, et al. Near infrared spectroscopic evaluation of fruit maturity and quality of export Thai mango (Mangifera indica L. var. Namdokmai)[J]. International Food Research Journal,2014,21(3):1073−1078.
[44]	CORTÉS V, ORTIZ C, ALEIXOS N, et al. A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy[J]. Postharvest Biology and Technology,2016,118:148−158. doi: 10.1016/j.postharvbio.2016.04.011
[45]	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 and Analysis,2015,38:32−41. doi: 10.1016/j.jfca.2014.10.013
[46]	MUNAWAR A A, KUSUMIYATI, WAHYUNI D. Near infrared spectroscopic data for rapid and simultaneous prediction of quality attributes in intact mango fruits[J]. Data in Brief,2019,27:104789. doi: 10.1016/j.dib.2019.104789
[47]	HAYATI R, MUNAWAR A, FACHRUDDIN F. Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango[J]. Data in Brief,2020,30:105571. doi: 10.1016/j.dib.2020.105571
[48]	SUBEDI P, WALSH K. Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy[J]. Postharvest Biology and Technology,2011,62:238−245. doi: 10.1016/j.postharvbio.2011.06.014
[49]	IZNEID B, FADHEL M, AL-KHARAZI T, et al. Design and develop a nondestructive infrared spectroscopy instrument for assessment of mango (Mangifera indica) quality[J]. Journal of Food Science and Technology,2014,51(11):3244−3252. doi: 10.1007/s13197-012-0880-z
[50]	JHA S, JAISWAL P, NARSAIAH K, et al. Nondestructive prediction of maturity of mango using near infrared spectroscopy[J]. Journal of Food Engineering,2014,124:152−157. doi: 10.1016/j.jfoodeng.2013.10.012
[51]	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 and Technology,2016,111:31−40. doi: 10.1016/j.postharvbio.2015.07.006
[52]	MARQUES E, FREITAS S, PIMENTEL M, et al. Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer[J]. Food Chemistry,2016,197(part B):1207−1214.
[53]	李敏, 高兆银, 朱迎迎, 等. 基于电子鼻的“贵妃” 芒果糖度酸度无损伤检测技术应用[J]. 热带作物学报,2016,37(8):1553−1557. [LI M, GAO Z Y, ZHU Y Y, et al. Non-destructive test on predicting sugar content and acidity of mango by electronic nose technology[J]. Chinese Journal of Tropical Crops,2016,37(8):1553−1557. doi: 10.3969/j.issn.1000-2561.2016.08.018
[54]	廖宇兰, 翁绍杰, 张海德, 等. 芒果的成熟与腐烂对介电特性的影响[J]. 农机化研究,2010,11:174−177. [LIAO Y L, WENG S J, ZHANG H D, et al. Effect of dielectric properties of maturity and decay of mango[J]. Agricultural Mechanization Research,2010,11:174−177. doi: 10.3969/j.issn.1003-188X.2010.02.051
[55]	YAHAYA N Z, AYOB N S, RAMLI R M, et al. Microwave dielectric properties for detection of ‘Harumanis’ mangoes ripeness[J]. Journal of Physics: Conference Series,2018,1083(1):012020.
[56]	浦宏杰, 汪迪松, 王辉, 等. 基于 zNose^TM电子鼻的芒果腐烂及成熟度检测[J]. 现代食品科技,2017,33(3):304−310. [PU H J, WANG D S, WANG H, et al. Detection of rot and ripeness of mango with zNose^TM electronic nose[J]. Modern Food Science and Technology,2017,33(3):304−310.
[57]	潘俊洋, 项辉宇, 薛真. 基于DSP系统的芒果品质检测平台构建[J]. 食品与机械,2017,33(11):127−136. [PAN J Y, XIANG H Y, XUE Z. Construction of mango quality inspection platform based on DSP system[J]. Food and Machinery,2017,33(11):127−136.
[58]	林瑜, 左江林, 罗宇强. BP 神经网络在超声波芒果分级检测中的应用[J]. 工业控制计算机,2016,29(10):52, 55. [LIN Y, ZUO J L, LUO Y Q. Application of BP neural network in fruit grading with ultrasonic testing[J]. Industrial Control Computer,2016,29(10):52, 55.
[59]	MAKINO Y, ISAMI A, SUHARA T, et al. Nondestructive evaluation of anthocyanin concentration and soluble solid content at the vine and blossom ends of green mature mangoes during storage by hyperspectral spectroscopy[J]. Food Science and Technology Research,2015,21(1):59−65. doi: 10.3136/fstr.21.59
[60]	HUANG X, LV R, WANG S, et al. Integration of computer vision and colorimetric sensor array for nondestructive detection of mango quality[J]. Journal of Food Process Engineering,2018,41(4):e12873.
[61]	BLANES C, CORTÉS V, ORTIZ C, et al. Non-destructive assessment of mango firmness and ripeness using a robotic gripper[J]. Food Bioprocess Technology,2015,8:1914−1924. doi: 10.1007/s11947-015-1548-2

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