基于近红外的柚子品种判别和糖度检测通用模型

李雄; 刘燕德; 欧阳爱国; 孙旭东; 胡军; 姜小刚; 欧阳玉平

doi:10.3788/fgxb20194006.0808

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基于近红外的柚子品种判别和糖度检测通用模型

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 基于近红外的柚子品种判别和糖度检测通用模型
- A General Model for Judging and Brix Detection of Grapefruit Variety Based on Near Infrared
- 发光学报 2019年40卷第6期页码：808-814
- 作者机构：
  
  华东交通大学机电与车辆工程学院, 江西南昌 330013
- 作者简介：
- 基金信息：
  
  国家自然科学基金（61640417）();南方山地果园智能化管理技术与装备协同创新中心（赣教高字[2014]60号）();江西省优势科技创新团队（20153BCB240
- DOI：10.3788/fgxb20194006.0808
  中图分类号： O657.33
- 纸质出版日期：2019-6-5，
  
  网络出版日期：2018-11-5，
  
  收稿日期：2018-7-25，
  
  修回日期：2018-10-18，
扫描看全文
李雄, 刘燕德, 欧阳爱国等. 基于近红外的柚子品种判别和糖度检测通用模型[J]. 发光学报, 2019,40(6): 808-814

LI Xiong, LIU Yan-de, OUYANG Ai-guo etc. A General Model for Judging and Brix Detection of Grapefruit Variety Based on Near Infrared[J]. Chinese Journal of Luminescence, 2019,40(6): 808-814
李雄, 刘燕德, 欧阳爱国等. 基于近红外的柚子品种判别和糖度检测通用模型[J]. 发光学报, 2019,40(6): 808-814 DOI： 10.3788/fgxb20194006.0808.

LI Xiong, LIU Yan-de, OUYANG Ai-guo etc. A General Model for Judging and Brix Detection of Grapefruit Variety Based on Near Infrared[J]. Chinese Journal of Luminescence, 2019,40(6): 808-814 DOI： 10.3788/fgxb20194006.0808.

摘要

针对厚皮果透光性差、不同柚子品种糖度在线检测要单独建模等问题，本文以柚子为研究对象，采集有效光谱325条，对比分析不同柚子品种在710 nm和800 nm附近的两个吸收峰光谱响应特性。550~970 nm全波段范围内的光谱采用SPA无信息消除后建立柚子偏最小二乘判别模型误判率为1.25%；偏最小二乘法在550~970 nm全波段范围和去差异化后750~930 nm波段范围的预测相关系数分别为0.58和0.86，预测均方根误差RMSEP分别为0.84和0.55。实验结果表明，连续投影法结合偏最小二乘判别模型可以实现不同柚子品种的定性判别，变异系数法对光谱去差异化后结合最小二乘模型对厚皮果柚子可溶性固形物的定量检测效果最佳，该研究为不同品种的厚皮果在线分选技术提供了参考和理论依据。

Abstract

In view of the poor light transmission of thick skin fruit and the independent modeling of the sugar content of different grapefruit varieties

the grapefruit was the research object

and 325 effective spectra were collected to compare and analyze the spectral responses of two absorption peaks of different grapefruit varieties near 710 nm and 800 nm. In the 550-970 nm full-band spectrum

the false positive rate of the grapefruit partial least squares discriminant model is 1.25% after SPA no information elimination; the partial least squares method is in the 550-970 nm full-band range and the de-differential 750-930 nm band. The prediction correlation coefficients of the range are 0.58 and 0.86

respectively

and the predicted root mean square error RMSEP is 0.84 and 0.55

respectively. Experiments show that the continuous projection method combined with the partial least squares discriminant model can realize the qualitative discrimination of different grapefruit varieties. The coefficient of variation method is the best to quantify the soluble solids of the thick fruit grapefruit with the least squares model. This study provides a reference and theoretical basis for the online sorting technology of different varieties of thick fruit.

关键词

近红外光谱在线检测模型可溶性固形物品种判别

Keywords

near-infrared spectroscopyon-line detectionuniversal modelsoluble solidsvariety discrimination

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