Predicting early bruise susceptibility of apples using the optical properties

WU Xiao-hua; ZHANG Shu-hui; CHEN Si-yu; TAN Zuo-jun; ZHANG Shu; CHEN Jian-jun

doi:10.13386/j.issn1002-0306.2017.18.049

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Science and Technology of Food Industry > 2017 > (18): 258-263. > DOI: 10.13386/j.issn1002-0306.2017.18.049

WU Xiao-hua, ZHANG Shu-hui, CHEN Si-yu, TAN Zuo-jun, ZHANG Shu, CHEN Jian-jun. Predicting early bruise susceptibility of apples using the optical properties[J]. Science and Technology of Food Industry, 2017, (18): 258-263. DOI: 10.13386/j.issn1002-0306.2017.18.049

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Predicting early bruise susceptibility of apples using the optical properties

College of Engineering, Huazhong Agricultural University
College of Science, Huazhong Agricultural University

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Received Date: April 04, 2017

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Abstract

The positions of tiny bruising apples are easily invaded by pathogenic microorganisms, causing themselves and surrounding fruits to rot.The early detection can effectively reduce the loss from tiny bruise.In this study, the single integrating sphere setup and the inverse adding-doubling ( IAD) method were used to investigate the optical properties of Fuji apples at1450¹800 nm.The data of optical properties were preprocessed by the multiplicative scatter correction and standard normal variables methods.A discriminate model that combines principal component analysis with Probabilistic neural networks ( PNN) to judge and predict the bruising of apples was established based on the absorption coefficient and the reduced scattering coefficient of 1450¹800 nm.The accuracy of the model for no bruising judgment was above 96%.The accuracy of the model of absorption coefficients for early bruising judgment was 100% when spread was less 0.7.The experimental results showed optical properties can judge the early tiny bruising of apples and provide application prospect of detecting the bruise of fruits.
- early tiny bruise,
- apples,
- integrating spheres,
- optical properties,
- probabilistic neural networks

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[1]	张敏, 解越.采后果蔬低温贮藏冷害研究进展[J].食品与生物技术学报, 2016, 35 (1) :1-11.
[2]	孙宝霞, 汤林越, 何志良, 等.基于机器视觉的采后荔枝表皮微损伤实时检测[J].农业机械学报, 2016, 47 (7) :35-41.
[3]	Moscetti R, Haff R P, Monarca D, et al.Near-infrared spectroscopy for detection of hailstorm damage on olive fruit[J].Postharvest Biology&Technology, 2016, 120:204-212.
[4]	Haff R P, Slaughter D C.Real-time X-ray inspection of wheat for infestation by the granary weevil, Sitophilusgranarius (L.) [J].Transactions of the Asae, 2004, 47 (2) :531-537.
[5]	Clark C J, Macfall J S.Quantitative magnetic resonance imaging of‘Fuyu’persimmon fruit during development and ripening[J].Magnetic Resonance Imaging, 2003, 21 (6) :679-685.
[6]	Omid Doosti-Irani, Mahmood Reza Golzarian.Development of multiple regression model to estimate the apple’sbruise depth using thermal maps[J].Postharvest Biology and Technology, 2016, 116:75-79.
[7]	Keresztes J C, Goodarzi M, Saeys W.Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques[J].Food Control, 2016, 66 (1) :215-226.
[8]	Cen H, Lu R, Mendoza F, Beaudry RM.Relationship of the optical absorption and scattering properties with mechanical and structural properties of apple tissue[J].Postharvest Biology&Technology, 2013, 85 (11) :30-38.
[9]	Rowe P I, Künnemeyer R, Mcglone A, et al.Relationship between tissue firmness and optical properties of‘Royal Gala’apples from 400 to 1050 nm[J].Postharvest Biology&Technology, 2014, 94:89-96.
[10]	Rizzolo A, Vanoli M, Spinelli L, et al.Sensory characteristics, quality and optical properties measured by timeresolved reflectance spectroscopy in stored apples[J].Postharvest Biology&Technology, 2010, 58 (1) :1-12.
[11]	.石敏, 吴正国, 徐袭.基于概率神经网络和双小波的电能质量扰动自动识别[J].电力自动化设备, 2006, 26 (3) :5-8.
[12]	韩蕾, 李晨曦, 孙承涛, 等.基于双积分球的宽光谱组织光学参数测量系统与方法研究[J].光谱学与光谱分析, 2016, 36 (2) :561-566.
[13]	Scott Prahl.Everything I think you should know about Inverse Adding Doubling[M].Oregon Medical Laser Center, 2011.
[14]	Staveren H JV, Moes C J M, Marle J V, et al.Lights cattering in Intralipid-10%in the wavelength range of 400-1100 nm[J].Applied Optics, 1991, 30 (31) :4507-4514.
[15]	Deng R R, He Y Q, Qin Y, et al.Pure water absorption coefficient measurement after eliminating the impact of suspended substance in spectrum from 400 nm to 900 nm[J].Journal of Remote Sensing, 2012, 16 (1) :174-191.
[16]	梁晓燕, 郭中华, 线文瑶, 等.基于高光谱和极限学习机的冷鲜羊肉颜色无损检测[J].食品工业科技, 2016, 37 (24) :69-73.
[17]	丁佳兴, 吴龙国, 何建国, 等.高光谱成像技术对灵武长枣果皮强度的无损检测[J].食品工业科技, 2016, 37 (24) :58-68.
[18]	莫剑冬, 徐章遂.应用概率神经网络诊断自行火炮发动机的故障[J].测试技术学报, 2000, 14 (1) :7-11.
[19]	Sun Q, Wu C, Li Y L.A new probabilistic neural network model based on back propagation algorithm[J].Journal of Intelligent&Fuzzy Systems, 2017, 32 (1) :215-227.
[20]	Saeys W, Velazco-Roa M A, Thennadil S N, et al.Optical properties of apple skin and flesh in the wavelength range from350 to 2200 nm[J].Applied Optics, 2008, 47 (7) :908-919.
[21]	Osborne B G, Fearn T, Hindle P H.Practical NIR spectroscopy with applications in food and beverage analysis[M].Longman Scientific&Technical, 1993.