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

冯愈钦; 吴龙国; 何建国; 王松磊; 贺晓光; 丁佳兴

doi:10.3788/fgxb20163708.1014

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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

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 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
- Detection Method of Soluble Solid of Jujube at Different Preservative Temperature Based on Hyper-spectral Imaging Technology
- 发光学报 2016年37卷第8期页码：1014-1022
- 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
  中图分类号： S123;O657.3
- 纸质出版日期：2016-8-5，
  
  收稿日期：2016-3-13，
  
  修回日期：2016-4-17，
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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

高光谱成像可溶性固形物连续投影法偏最小二乘法

Keywords

hyper-spectral imagingsoluble solidsuccessive projections algorithmpartial least squares

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