Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology

FENG Yu-qin; WU Long-guo; HE Jian-guo; WANG Song-lei; HE Xiao-guang; DING Jia-xing

doi:10.3788/fgxb20163708.1014

您当前的位置：

首页 >

文章列表页 >

Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology

更新时间：2020-08-12

- Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology
- Chinese Journal of Luminescence Vol. 37, Issue 8, Pages: 1014-1022(2016)
- 作者机构：
  
  1. 宁夏大学农学院,宁夏银川,750021
  2. 宁夏大学土木水利工程学院,宁夏银川,750021
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20163708.1014
  CLC： S123;O657.3
- Received：13 March 2016，
  
  Revised：17 April 2016，
  
  Published：05 August 2016
- 稿件说明：
移动端阅览
冯愈钦, 吴龙国, 何建国等. 基于高光谱成像技术的长枣不同保藏温度的可溶性固形物含量检测方法[J]. 发光学报, 2016,37(8): 1014-1022

FENG Yu-qin, WU Long-guo, HE Jian-guo etc. Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology[J]. Chinese Journal of Luminescence, 2016,37(8): 1014-1022
冯愈钦, 吴龙国, 何建国等. 基于高光谱成像技术的长枣不同保藏温度的可溶性固形物含量检测方法[J]. 发光学报, 2016,37(8): 1014-1022 DOI： 10.3788/fgxb20163708.1014.

FENG Yu-qin, WU Long-guo, HE Jian-guo etc. Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology[J]. Chinese Journal of Luminescence, 2016,37(8): 1014-1022 DOI： 10.3788/fgxb20163708.1014.

摘要

应用高光谱成像技术对不同保藏温度的灵武长枣的可溶性固形物含量进行预测模型建立。提取图像中感兴趣区域的平均光谱数据，经过不同光谱预处理后，利用连续投影法（SPA）选择特征波长，对4℃冷藏光谱提取13个特征波段（421，426，512，598，641，670，675，723，814，906，944，978，982 nm），对常温保藏光谱提取12个特征波段（425，507，555，598，673，680，685，718，809，910，954，978 nm）。对于MSC处理、MSC+SPA处理、Savitzky-Golay平滑处理和SNV 4种预处理方法，筛选出的最优预处理方法是冷藏采用MSC处理、常温采用MSC+SPA处理。对应这两种最优预处理方法，分别建立偏最小二乘法（PLSR）、支持向量机（SVM）、主成分回归（PCR）3种预测模型。在以上获得的6个预测模型中，得出冷藏、常温保藏的最优模型分别为MSC-PLSR模型（

：0.852，RMSEC：0.940；

P：0.857，RMSEP：0.894）和MSC+SPA-PLSR模型（

：0.872，RMSEC：0.866；

P：0.787，RMSEP：1.007）。结果表明：利用高光谱成像技术，结合多种预测模型建立，能够测定不同保藏温度下的灵武长枣可溶性固形物含量，实现对灵武长枣准确快速的无损检测。

Abstract

The hyper-spectral imaging technology was applied to build a prediction model for soluble solid content of Lingwu jujube at different preservative temperature. The average spectra data were extracted from the area-of-interest of the image. After pre-treatment of different spectrum

the succession projection analysis (SPA) was used to select characteristic wavelength. 13 characteristic wavebands under 4℃ temperature condition (421

426

512

598

641

670

675

723

814

906

944

978

982 nm) and 12 characteristic wavebands under normal temperature condition (425

507

555

598

673

680

685

718

809

910

954

978 nm) were extracted. By adoption of MSC treatment

MSC+SPA treatment

Savitzky-Golay smooth treatment and SNV treatment

both MSC treatment and MSC+SPA treatment out of 4 above were screened out as the optimum pre-treatment method afterwards. In corresponding to these 2 optimum pre-treatment methods

3 prediction models like partial least squares regressions (PLSR)

support vector machine (SVM) and principal component regression (PCR) model were built

respectively. Among the aforesaid 6 prediction models

2 optimum modes such as PLSR model after treated by MSC (

:0.852

RMSEC:0.940;

:0.857

RMSEP:0.894) and PLSR model after treated by MSC+SPA (

:0.872

RMSEC:0.866;

:0.787

RMSEP:1.007) were acquired. The results show that the content of soluble solids of Lingwu jujube at different preservative temperature can be forecasted by utilization of hyper-spectral imaging technology in combination of building multiple prediction models

so that the nondestructive testing (NDT) can be achieved for Lingwu jujube in accurate and rapid manner.

关键词

Keywords

references

张令标.基于高光谱成像技术的红枣表面农药残留无损检测的研究[D].银川:宁夏大学, 2014. ZHANG L B. Non-destructive The Pesticide Residue of Red Jujube Surface by Hyperspectral Imaging Technology[D]. Yinchuan:Ningxia University, 2014. (in Chinese)

曹兵,侯晶东.对"灵武长枣栽培技术创新与长枣产业发展"的思考[J].农业科学研究,2011, 32(1):46-49. CAO B, HONG J D. Innovation of cultivating techniques and industry development of Zizyphus jujube Mill. cv. Lingwu Changzao[J]. J. Agric. Sci., 2011, 32(1):46-49. (in Chinese)

XIONG Z J, SUN D W, ZENG X A, et al.. Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats:a review[J]. J. Food Eng., 2014, 132:1-13.

VERD S, VSQUEZ F, GRAU R, et al.. Detection of adulterations with different grains in wheat products based on the hyperspectral image technique:the specific cases of flour and bread[J]. Food Control, 2016, 62:373-380.

CEN H Y, LU R F, ZHU Q B, et al.. Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification[J]. Postharv. Biol. Technol., 2016, 111:352-361.

VAKILI H, KOLAKOVIC R, GENINA N, et al.. Hyperspectral imaging in quality control of inkjet printed personalised dosage forms[J]. Int. J. Pharm., 2015, 483(1-2):244-249.

李金梦,叶旭君,王巧男,等.高光谱成像技术的柑橘植株叶片含氮量预测模型[J].光谱学与光谱分析, 2014, 34(1):212-216. LI J M, YE X J, WANG Q N, et al.. Development of prediction models for determining n content in citrus leaves based on hyperspectral imaging technology[J]. Spectrosc. Spect. Anal., 2014, 34(1):212-216. (in Chinese)

岳绒.受损猕猴桃贮藏期间近红外光谱与品质关系的研究[D].杨凌:西北农林科技大学, 2011. YUE R. Research on The Relationship Between Near-infrared Spectroscopy and Quality of Bruised Kiwifruit in Storage[D]. Yangling:Northwest A&F University, 2011. (in Chinese)

刘燕德,邓清.基于高光谱成像技术的脐橙叶片的叶绿素含量及其分布测量[J].发光学报,2015, 36(8):957-961. LIU Y D, DENG Q. Measurement of chlorophyll distribution in navel orange leaves based on hyper-spectral imaging technique[J]. Chin. J. Lumin., 2015, 36(8):957-961. (in Chinese)

薛建新,张淑娟,张晶晶.高光谱成像技术对鲜枣内外部品质检测的研究[J].光谱学与光谱分析, 2015, 35(8):2297-2302. XUE J X, ZHANG S J, ZHANG J J. Simultaneous detection of external and internal quality parameters of Huping jujube fruits using hyperspectral imaging technology[J]. Spectrosc. Spect. Anal., 2015, 35(8):2297-2302. (in Chinese)

GIOVENZANA V, BEGHI R, CIVELLI R, et al.. Optical techniques for rapid quality monitoring along minimally processed fruit and vegetable chain[J]. Trends Food Sci. Technol., 2015, 46(2):331-338.

ALEXANDRINO G L, KHORASANI M R, AMIGO J M, et al.. Monitoring of multiple solid-state transformations at tablet surfaces using multi-series near-infrared hyperspectral imaging and multivariate curve resolution[J]. Eur. J. Pharm. Biopharm., 2015, 93:224-230.

邹小波,徐艺伟,陈武,等.高光谱图像技术检测枇杷叶片三萜酸含量及叶面分布[J].现代食品科技, 2015, 32(9):263-268. ZOU X B, XU Y W, CHEN W, et al.. Measurement of triterpenic acid content and its distribution in loquat leaves based on the hyperspectral imaging technique[J]. Mod. Food Sci. Technol., 2015, 32(9):263-268. (in Chinese)

LI J B, CHEN L P, HUANG W Q, et al.. Multispectral detection of skin defects of bi-colored peaches based on Vis-NIR hyperspectral imaging[J]. Postharv. Biol. Technol., 2016, 112:121-133.

鲍一丹,陈纳,何勇,等.近红外高光谱成像技术快速鉴别国产咖啡豆品种[J].光学精密工程,2015, 23(2):349-355. BAO Y D, CHEN N, HE Y, et al.. Rapid identification of coffee bean variety by near infrared hyperspectral imaging technology[J]. Opt. Precision Eng., 2015, 23(2):349-355. (in Chinese)

GALVIS L, BERTINETTO C G, HOLOPAINEN U, et al.. Structural and chemical analysis of native and malted barley kernels by polarized Raman spectroscopy (PRS)[J]. J. Cereal Sci., 2015, 62:73-80.

REINHOLDS I, BARTKEVICS V, SILVIS I C J, et al.. Analytical techniques combined with chemometrics for authentication and determination of contaminants in condiments:a review[J]. J. Food Compos. Anal., 2015, 44:56-72.

任晓东,雷武虎.基于光谱区分法的高光谱核异常检测算法[J].光子学报, 2016, 45(3):0330003-1-5. REN X D, LEI W H. Kernel anomaly detection method in hyperspectral imagery based on the spectral discrimination method[J]. Acta Photon. Sinica, 2016, 45(3):0330003-1-5. (in Chinese)

郭文川,董金磊.高光谱成像结合人工神经网络无损检测桃的硬度[J].光学精密工程, 2015, 23(6):1530-1537. GUO W C, DONG J L. Nondestructive detection on firmness of peaches based on hyperspectral imaging and artificial neural networks[J]. Opt. Precision Eng., 2015, 23(6):1530-1537. (in Chinese)

KANEKO H, FUNATSU K. Classification of drug tablets using hyperspectral imaging and wavelength selection with a GAWLS method modified for classification[J]. Int. J. Pharm., 2015, 491(1-2):130-135.

黄鸿,杨娅琼,罗甫林.基于半监督稀疏多流形嵌入的高光谱影像分类[J].光子学报,2016, 45(3):0330001. HUANG H, YANG Y Q, LUO F L. Classification of hyperspectral images based on semi-supervised sparse multi-manifold embedding[J]. Acta Photon. Sinica, 2016, 45(3):0330001. (in Chinese)

Views

613

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Oil Spills Identification Using Hyperspectral Imaging Based on Multi-pattern Method

Measurement of Chlorophyll Distribution in Navel Orange LeavesBased on Hyper-spectral Imaging Technique

Related Author

WAN Jian-hua

HAN Zhong-zhi

SONG Xin-xin

LIU Jie

LIU Yan-de

DENG Qing

Related Institution

School of Geosciences, China University of Petroleum

Information College, Qingdao Agricultural University

Qingdao Entry-exit Inspection and Quarantine Bureau

Institute of Optics-Mechanics-Electronics Technology and Application, School of Mechatronic Engineering, East China Jiaotong University, Nanchang 330013, China

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176862 Email：fgxbt@126.com
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰