Please wait a minute...
 
 
食品与发酵工业  2022, Vol. 48 Issue (15): 281-287    DOI: 10.13995/j.cnki.11-1802/ts.029024
  分析与检测 本期目录 | 过刊浏览 | 高级检索 |
融合密度与光谱特征的苹果霉心病无损检测
张佐经1,2,3*, 付新阳1, 陈柯铭1, 赵遵龙1, 张仲雄1,2,3, 赵娟1,2,3
1(西北农林科技大学 机械与电子工程学院,陕西 杨凌,712100)
2(农业农村部农业物联网重点实验室,陕西 杨凌,712100)
3(陕西省农业信息感知与智能服务重点实验室,陕西 杨凌,712100)
Research on non-destructive detection method of moldy apple core by fusing density and spectral features
ZHANG Zuojing1,2,3*, FU Xinyang1, CHEN Keming1, ZHAO Zunlong1, ZHANG Zhongxiong1,2,3, ZHAO Juan1,2,3
1(College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)
2(Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China)
3(Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China)
下载:  HTML  PDF (2808KB) 
输出:  BibTeX | EndNote (RIS)      
摘要 针对近红外漫反射光谱对苹果霉心病判别准确率较低的问题,提出了一种融合密度特征与漫反射光谱的苹果霉心病多因子无损检测方法。基于光谱采集平台获取195个富士苹果的漫反射光谱(200~1 100 nm)信息,利用WLD-600密度仪获取苹果密度信息,采用标准正态变量变换(standard normal variable transformation,SNV)对光谱数据进行预处理,竞争性自适应重加权采样法(competitive adaptive reweighted sampling,CARS)和连续投影算法(successive projection algorithm,SPA)结合用于提取与霉心病相关的特征光谱,分别以密度、特征光谱、密度+特征光谱作为模型因子,建立偏最小二乘判别(partial least squares discriminant analysis,PLS-DA)、Fisher判别、支持向量机(support vector machine,SVM)和最小二乘支持向量机(least squares support vector machine,LS-SVM)4种不同的苹果霉心病判别模型。结果表明,在不同的霉心病判别模型中基于密度与特征光谱融合的模型总体判别率均高于分别基于密度、特征光谱的模型总体判别率,其中,基于密度与特征光谱融合的SVM模型总体判别率最高,为95.56%,而基于密度、特征光谱建立的SVM模型总体判别率分别为82.22%、91.11%,因此融合密度特征可进一步提高漫反射光谱对苹果霉心病的判别准确率,同时为开发基于漫反射检测原理的便携式苹果内部病害与品质一体化无损检测设备提供了可能。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
张佐经
付新阳
陈柯铭
赵遵龙
张仲雄
赵娟
关键词:  苹果霉心病  特征融合  漫反射光谱  密度  无损检测    
Abstract: To solve the problem of low accuracy of near-infrared diffuse reflectance spectroscopy for moldy apple core discrimination, this paper proposes a multi-factor nondestructive detection method for moldy apple core by fusing density feature with diffuse reflectance spectroscopy. Based on the spectral acquisition platform to obtain the diffuse reflectance spectrum (200-1 100 nm) information of 195 Fuji apples, the WLD-600 density meter was used to obtain apple density data. In addition, the standard normal variable transformation (SNV) was used to pre-process the spectral data. Moreover, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were combined to extract the feature spectra related to moldy apple core. Four different models of moldy apple core discrimination were established by density, diffuse reflectance spectra, fusing density and diffuse reflectance spectra with partial least squares discriminant analysis (PLS-DA), Fisher’s discrimination, support vector machine (SVM) and least squares support vector machine (LS-SVM). The results showed that the overall discrimination rate of the model based on the fusion of density and feature spectra was higher than that of the model based on density and feature spectra in different moldy apple core discriminant models. The SVM model based on the fusion of density and feature spectra had the highest overall discrimination rate of 95.56%, while the overall discrimination rate of the SVM model based on density and feature spectra was 82.2% and 91.11% respectively. Hence, the fusion of density feature could further improve the discrimination accuracy of diffuse reflectance spectra for moldy apple core, and also provides the possibility of developing portable non-destructive testing equipment based on diffuse reflection detection principle for the integration of apple internal disease and quality.
Key words:  moldy apple core    feature fusion    diffuse reflection spectra    density    non-destructive detection
收稿日期:  2021-08-19      修回日期:  2021-11-22           出版日期:  2022-08-15      发布日期:  2022-09-02      期的出版日期:  2022-08-15
基金资助: 国家自然科学基金项目(31701664)
作者简介:  第一作者:硕士,讲师(通信作者,E-mail:zhangzuojing@126.com)
引用本文:    
张佐经,付新阳,陈柯铭,等. 融合密度与光谱特征的苹果霉心病无损检测[J]. 食品与发酵工业, 2022, 48(15): 281-287.
ZHANG Zuojing,FU Xinyang,CHEN Keming,et al. Research on non-destructive detection method of moldy apple core by fusing density and spectral features[J]. Food and Fermentation Industries, 2022, 48(15): 281-287.
链接本文:  
http://sf1970.cnif.cn/CN/10.13995/j.cnki.11-1802/ts.029024  或          http://sf1970.cnif.cn/CN/Y2022/V48/I15/281
[1] LI S F, ZHANG L H, LIU X H.Effects of mouldy core and core rot on physiological and biochemical responses of apple fruit[J].Journal of the Science of Food and Agriculture, 2011, 91(14):2 674-2 678.
[2] 李芳,蔡骋,马惠玲,等.基于生物阻抗特性分析的苹果霉心病无损检测[J].食品科学, 2013, 34(18):197-202.LI F, CAI C, MA H L, et al.Nondestructive detection of apple mouldy core based on bioimpedance properties[J].Food Science, 2013, 34(18):197-202.
[3] VANDENDRIESSCHE T, SCHÄFER H, VERLINDEN B E, et al.High-throughput NMR based metabolic profiling of Braeburn apple in relation to internal browning[J].Postharvest Biology and Technology, 2013, 80:18-24.
[4] HU W Y, LI J T, ZHU X Q, et al.Nondestructive detection of underlying moldy lesions of apple using frequency domain diffuse optical tomography[J].Postharvest Biology and Technology, 2019, 153:31-42.
[5] GUO Z M, GUO C, CHEN Q S, et al.Classification for Penicillium expansum spoilage and defect in apples by electronic nose combined with chemometrics[J].Sensors (Basel, Switzerland), 2020, 20(7):2130.
[6] 袁鸿飞. FT-NIR和电子鼻技术对苹果霉心病、水心病的无损检测研究[D].杨凌:西北农林科技大学, 2017.YUAN H F.Non-destructive detection of apple moldy core and watercore by FT-NIR and electronic nose[D].Yangling:Northwest A & F University, 2017.
[7] 张卫园. 基于密度特征的苹果霉心病无损检测方法研究[D].杨凌:西北农林科技大学, 2015.ZHANG W Y.Research on nondenstructive detection of apple mould core disease based on density character[D].Yangling:Northwest A & F University, 2015.
[8] LI L, PENG Y K, LI Y Y, et al.Rapid and low-cost detection of moldy apple core based on an optical sensor system[J].Postharvest Biology and Technology, 2020, 168:111276.
[9] 张海辉, 田世杰, 马敏娟, 等.考虑直径影响的苹果霉心病透射光谱修正及检测[J].农业机械学报, 2019, 50(1):313-320.ZHANG H H, TIAN S J, MA M J, et al.Detection method of moldy core in apples using modified transmission spectrum based on size of fruit[J].Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(1):313-320.
[10] TIAN S J, ZHANG J H, ZHANG Z X, et al.Effective modification through transmission Vis/NIR spectra affected by fruit size to improve the prediction of moldy apple core[J].Infrared Physics & Technology, 2019, 100:117-124.
[11] 苏东, 张海辉, 陈克涛, 等.基于透射光谱的苹果霉心病多因子无损检测[J].食品科学, 2016, 37(8):207-211.SU D, ZHANG H H, CHEN K T, et al.Multiple-factor nondestructive detection of moldy core in apples based on transmission spectra[J].Food Science, 2016, 37(8):207-211.
[12] 雷雨, 何东健, 周兆永, 等.苹果霉心病可见/近红外透射能量光谱识别方法[J].农业机械学报, 2016, 47(4):193-200.LEI Y, HE D J, ZHOU Z Y, et al.Detection of moldy core of apples based on visible/near infrared transmission energy spectroscopy[J].Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(4):193-200.
[13] 李顺峰, 张丽华, 刘兴华, 等.基于主成分分析的苹果霉心病近红外漫反射光谱判别[J].农业机械学报, 2011, 42(10):158-161.LI S F, ZHANG L H, LIU X H, et al.Discriminant analysis of apple moldy core using near infrared diffuse reflectance spectroscopy based on principal component analysis[J].Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(10):158-161.
[14] LU X G, LIU X H, LI S F, et al.Possible mechanisms of warming effects for amelioration of superficial scald development on ‘Fuji’ apples[J].Postharvest Biology and Technology, 2011, 62(1):43-49.
[15] 张建超. 霉心病苹果品质分析及无损检测模型的建立[D].沈阳:辽宁大学, 2020.ZHANG J C.Quality analysis of core rot apple and establishment of nondestructive testing model for core rot apple[D].Shenyang:Liaoning University, 2020.
[16] ENGEL J, GERRETZEN J, SZYMAŃSKA E, et al.Breaking with trends in pre-processing?[J].TrAC Trends in Analytical Chemistry, 2013, 50:96-106.
[17] MORAIS C L M, SANTOS M C D, LIMA K M G, et al.Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach[J].Bioinformatics, 2019, 35(24):5 257-5 263.
[18] QIU G Y, TAO D, XIAO Q, et al.Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis[J].Journal of the Science of Food and Agriculture, 2021, 101(4):1 323-1 330.
[19] SOARES S F C, GOMES A A, ARAUJO M C U, et al.The successive projections algorithm[J].TrAC Trends in Analytical Chemistry, 2013, 42:84-98.
[20] VARATHARAJAN R, MANOGARAN G, PRIYAN M K.A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing[J].Multimedia Tools and Applications, 2018, 77(8):10 195-10 215.
[21] MO L N, CHEN H Z, CHEN W H, et al.Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy[J].Infrared Physics & Technology, 2020, 108:103366.
[22] WALSH K B, BLASCO J, ZUDE-SASSE M, et al.Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment:The science behind three decades of commercial use[J].Postharvest Biology and Technology, 2020, 168:111246.
[23] 陈敬谊. 苹果优质丰产栽培实用技术[M].北京:化学工业出版社, 2016:34-35.CHEN J Y.Practical Cultivation Technology of High Quality and High Yield of Apple[M].Beijing:Chemical Industry Press, 2016:34-35.
[1] 苗小雨, 柴春祥, 鲁晓翔. 无损检测技术在食品微生物检测中的应用与展望[J]. 食品与发酵工业, 2022, 48(8): 311-319.
[2] 郭阳, 史勇, 郭俊先, 李雪莲, 黄华. 近红外光谱技术结合反向区间偏最小二乘算法-连续投影算法预测哈密瓜可溶性固形物含量[J]. 食品与发酵工业, 2022, 48(2): 248-253.
[3] 张建超, 张鹏, 薛友林, 贾晓昱, 李江阔. 基于电子鼻表征霉心病苹果特征气味及无损检测模型建立[J]. 食品与发酵工业, 2022, 48(2): 267-273.
[4] 朱金艳, 朱玉杰, 冯国红, 曾明飞, 刘思岐. 基于近红外光谱技术联合极限学习机的蓝莓贮藏品质定量模型建立[J]. 食品与发酵工业, 2022, 48(16): 270-276.
[5] 李子财, 李林, 李光洲, 王建清, 辛维岗, 张棋麟, 王峰, 林连兵. 基于灌流浓缩培养的罗伊氏乳杆菌高密度发酵研究[J]. 食品与发酵工业, 2022, 48(15): 18-23.
[6] 孟庆龙, 冯树南, 尚静, 黄人帅, 张艳, 曹森. 基于可见/近红外光谱预测枇杷糖度及模型优化[J]. 食品与发酵工业, 2022, 48(12): 249-254.
[7] 孙媛媛, 崔树茂, 唐鑫, 毛丙永, 赵建新, 陈卫. 发酵乳杆菌的生长限制性因素分析及高密度培养工艺优化[J]. 食品与发酵工业, 2021, 47(6): 1-10.
[8] 吕奎, 贾禄强, 戴京京, 丁健. 通用型毕赤酵母高密度培养策略的网络共享技术[J]. 食品与发酵工业, 2021, 47(5): 92-98.
[9] 李怡菲, 覃小丽, 阚建全, 刘雄, 钟金锋. 环木菠萝烯醇阿魏酸酯分子结构性质的密度泛函理论研究[J]. 食品与发酵工业, 2021, 47(2): 51-56.
[10] 高欣伟, 崔树茂, 唐鑫, 毛丙永, 赵建新, 陈卫. 长双歧杆菌的最适底物解析和高密度发酵工艺优化[J]. 食品与发酵工业, 2021, 47(19): 12-20.
[11] 夏凯, 周志桥, 李海枝, 马喜山, 吴逸民, 苑鹏, 刘士伟, 段盛林. 海洋鱼骨胶原低聚肽及杜仲协同氨基葡萄糖-硫酸软骨素对去势大鼠骨密度及骨代谢的影响[J]. 食品与发酵工业, 2021, 47(19): 90-94.
[12] 李茹, 刘阳, 朱永康, 赵星辰, 王娜, 杜梓星, 刘书成, 孙钦秀, 魏帅, 夏秋瑜. 响应面优化高密度CO2诱导金鲳鱼鱼糜凝胶化的工艺[J]. 食品与发酵工业, 2021, 47(13): 198-204.
[13] 蒋文鑫, 崔树茂, 毛丙永, 唐鑫, 赵建新, 张灏, 陈卫. 短双歧杆菌冻干保护剂的优选及高密度冻干工艺优化[J]. 食品与发酵工业, 2020, 46(9): 31-36.
[14] 朱敏, 孙婷, 白直真, 罗惠波, 田建平, 黄丹. 基于可见光/近红外高光谱技术的窖泥总酸的分布[J]. 食品与发酵工业, 2020, 46(8): 111-117.
[15] 董迪, 潘嘹, 卢立新. 包装薄膜对生鲜牛肉可见光谱无损检测的干扰及处理方法研究[J]. 食品与发酵工业, 2020, 46(7): 234-238.
[1] Mou Yanying,Song Guoyong,Li Hong. Primary Study on Properties of Inulinase[J]. Food and Fermentation Industries, 2005, 31(11): 39 .
[2] . [J]. Food and Fermentation Industries, 2003, 29(3): 53 .
[3] Pan Hongyang,Wang Shuying,Mo Haizhen. Determination of Seleno-amino Acids in Enriched-selenium Dehydrated Brassica Chinensis by RHPLC[J]. Food and Fermentation Industries, 2008, 34(10): 141 .
[4] Chen Mo,Wang Zhiwei,Hu Changying,Wu Xiyang,Wang Pingli. Rapid Evaluating of Antimicrobial Activity of Vanillin with the Microplate Reader in 96-cell Plate[J]. Food and Fermentation Industries, 2009, 35(5): 63 .
[5] LI Jin-feng,YE Fa-yin,ZHAO Guo-hua. Synthesis and characterization of polysaccharide-phenolic acid conjugates[J]. Food and Fermentation Industries, 2017, 43(2): 245 .
[6] ZHAO Lu-yao,YANG Shu-ming,HOU Can,CHENG Yong-you,YOU Xin-yong,ZHANG Yan-hua. Progress of biomarkers research on monitoring drug abuse in animal production[J]. Food and Fermentation Industries, 2015, 41(9): 230 .
[7] YANG Hui,FAN Wen-lai,XU Yan. Characterization of non-volatile organic acids in Baijius (Chinese liquors) based on BSTFA derivatization coupled with GC-MS[J]. Food and Fermentation Industries, 2017, 43(5): 192 .
[8] CHEN An-te,ZHANG Wen-juan,ZHANG Xi,WU Qiu-hao,GAO Hong,JIA Li-rong. Effects of Saccharomyces cerevisiae on pickled radishes during fermentation[J]. Food and Fermentation Industries, 2017, 43(6): 129 .
[9] LI Ting-ting,CHEN Si,LI Huan,ZHAO Qiancheng,MA Kun,LI Jianrong. Analysis of spoilage ability of specific spoilage organism in refrigerated silver carp[J]. Food and Fermentation Industries, 2017, 43(6): 140 .
[10] Ruan Shili,Wang Xirui,Liu Debing,Wei Junya,MA Haijun. Research and Development Prospect of Cider Production Technology[J]. Food and Fermentation Industries, 2001, 27(4): 75 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《食品与发酵工业》编辑部
地址:北京朝阳区酒仙桥中路24号院6号楼111室
本系统由北京玛格泰克科技发展有限公司设计开发  技术支持:support@magtech.com.cn