Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging

JIE Deng-fei; LI Ze-hai; ZHAO Jun-wei; LIAN Yu-xiang; WEI Xuan

doi:10.3788/fgxb20173805.0685

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Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging

更新时间：2020-08-12

- Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging
- Chinese Journal of Luminescence Vol. 38, Issue 5, Pages: 685-691(2017)
- 作者机构：
  
  1. 华中农业大学工学院,湖北武汉,430070
  2. 福建农林大学机电工程学院,福建福州,350002
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20173805.0685
  CLC： S666;TP751
- Received：05 November 2016，
  
  Revised：24 January 2017，
  
  Published：05 May 2017
- 稿件说明：
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介邓飞, 李泽海, 赵竣威等. 基于高光谱漫透射成像可视化检测脐橙可溶性固形物[J]. 发光学报, 2017,38(5): 685-691

JIE Deng-fei, LI Ze-hai, ZHAO Jun-wei etc. Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging[J]. Chinese Journal of Luminescence, 2017,38(5): 685-691
介邓飞, 李泽海, 赵竣威等. 基于高光谱漫透射成像可视化检测脐橙可溶性固形物[J]. 发光学报, 2017,38(5): 685-691 DOI： 10.3788/fgxb20173805.0685.

JIE Deng-fei, LI Ze-hai, ZHAO Jun-wei etc. Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging[J]. Chinese Journal of Luminescence, 2017,38(5): 685-691 DOI： 10.3788/fgxb20173805.0685.

摘要

厚皮类瓜果内部品质的无损检测是目前水果产业的检测技术瓶颈。本文采用高光谱漫透射技术对脐橙可溶性固形物（SSC）含量进行可视化分析研究。通过基线校正（Baseline）预处理结合连续投影算法（SPA）优选9个特征波长，建立SSC偏最小二乘回归（PLSR）模型，校正集相关系数

cal

为0.891，校正集均方根误差RSMEC为0.612 Brix，预测集相关系数

pre

为0.889，预测集均方根误差RMSEP为0.630 Brix。最后，计算各个像素点的SSC值结合图像处理技术得出SSC的可视化分布图，直观判断脐橙SSC含量高低。

Abstract

Compared with the fruit with thin skin

it is more difficult to acquire the internal quality information of fruits with thick skin. In this study

the hyperspectral diffuse transmission technique was used to visually analyze the soluble solids content (SSC) of navel orange. By comparison of the results

the model using the spectra pretreated by baseline correction as the input was the best one. Based on the baseline corrected spectra

successive projections algorithm (SPA) was applied to select feature wavelengths and finally 9 bands were remained. The results of the partial least squares regression (PLSR) model for SSC prediction indicate that the correlation coefficient of calibration (

cal

) is 0.891

the root mean square error of calibration (RSMEC) is 0.612

the correlation coefficient of prediction (

pre

) is 0.889

and the root mean square error of prediction (RMSEP) is 0.630

respectively. Using the spectra of feature wavelengths as the input

the multiple linear regression (MLR) models for SSC prediction were calibrated. Based on the MLR model

each pixel value of the images was calculated. Combined with the image processing

the distribution maps of SSC in navel orange were drawn. So

the SSC of navel orange can be intuitive judged.

关键词

Keywords

references

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