面向采后赤霞珠葡萄质量分类的传感阵列设计和有效性分析

刘静; 窦全胜; 陈炳希; 宁宇宸; 魏广芬

doi:10.11959/j.issn.2096-109x.2026001

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面向采后赤霞珠葡萄质量分类的传感阵列设计和有效性分析

学术论文 | 更新时间：2026-05-26

- 面向采后赤霞珠葡萄质量分类的传感阵列设计和有效性分析
- Design and effectiveness analysis of a sensor array for post-harvest quality classification of Cabernet Sauvignon grapes
- 网络与信息安全学报 2026年12卷第1期页码：117-127
- 作者机构：
  
  1.喀什大学，新疆喀什 844008
  2.山东工商学院，山东烟台 264005
- 作者简介：
  
  刘静（1981- ），女，新疆焉耆人，喀什大学副教授，主要研究方向为深度学习与模式识别等。
  窦全胜（1971- ），男，黑龙江大庆人，博士，山东工商学院教授，主要研究方向为机器学习、数据挖掘、知识工程与知识处理、智能理论与方法等。
  陈炳希（1998- ），男，山东枣庄人，山东工商学院硕士生，主要研究方向为计算机视觉、目标检测、机器学习和视觉语言模型等。
  宁宇宸（2002- ），男，山东泰安人，山东工商学院硕士生，主要研究方向为人工智能、深度学习、知识图谱和大语言模型等。
  魏广芬（1978- ），女，山东诸城人，博士，山东工商学院教授，主要研究方向为电子鼻系统等。
- 基金信息：
  
  国家自然科学基金(61976125);新疆维吾尔自治区自然科学基金(2022D01A237);新疆维吾尔自治区本科教育教学研究和改革项目(XJGXJGPTA-2024011)
- DOI：10.11959/j.issn.2096-109x.2026001
  中图分类号： TP393
- 收稿：2025-08-18，
  
  修回：2025-11-05，
  
  纸质出版：2026-02-25
- 稿件说明：
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刘静, 窦全胜, 陈炳希, 等. 面向采后赤霞珠葡萄质量分类的传感阵列设计和有效性分析[J]. 网络与信息安全学报, 2026, 12(1): 117-127.

Liu Jing, Dou Quansheng, Chen Bingxi, et al. Design and effectiveness analysis of a sensor array for post-harvest quality classification of Cabernet Sauvignon grapes[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 117-127.
刘静, 窦全胜, 陈炳希, 等. 面向采后赤霞珠葡萄质量分类的传感阵列设计和有效性分析[J]. 网络与信息安全学报, 2026, 12(1): 117-127. DOI： 10.11959/j.issn.2096-109x.2026001.

Liu Jing, Dou Quansheng, Chen Bingxi, et al. Design and effectiveness analysis of a sensor array for post-harvest quality classification of Cabernet Sauvignon grapes[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 117-127. DOI： 10.11959/j.issn.2096-109x.2026001.

摘要

赤霞珠是全球种植最广泛的酿酒葡萄品种之一，利用电子鼻系统对采后赤霞珠葡萄质量进行有效监测具有重要的现实意义。传感阵列是电子鼻系统的核心，决定了电子鼻系统的气味识别能力。为此，设计了一款由8个传感器组成的传感器阵列，用于采集赤霞珠葡萄的挥发性有机化合物（VOC）数据；同时，提出了CDI法则和基于多粒度聚类的核心子集发现算法（ESDA-MC），用于验证传感阵列的有效性。实验结果表明，在不同聚类粒度下，ESDA-MC提取的核心子集对原始时序数据的覆盖率达73.1%~97.1%，轮廓系数大于0.5，验证了ESDA-MC输出的核心子集具有良好的时序连续性与簇间可区分性。特别地，当聚类数量

为3~5时，核心子集间的轮廓系数达0.74以上、综合指数超过0.84，输出模式与感官分类一致，满足CDI法则，验证了所设计的传感阵列能够有效识别赤霞珠葡萄的VOC模式变化，为采后赤霞珠葡萄的质量监测提供了技术手段，也为同类应用中传感阵列的设计和有效性分析提供了一个可以借鉴的解决方案。

Abstract

Cabernet Sauvignon is one of the most widely planted wine grape varieties globally

and the effective monitoring of post-harvest Cabernet Sauvignon grape quality using an electronic nose system holds significant practical importance. The sensor array is the core of the electronic nose system

determining the olfactory recognition capability of the electronic nose system. A sensor array composed of eight sensors was designed for collecting data on VOC released during the quality evolution process of Cabernet Sauvignon grapes. A CDI criterion and a essential subset discovery algorithm based on multi-granularity clustering (ESDA-MC) were proposed to validate the effectiveness of the sensor array. Experimental results demonstrated that under different clustering granularities

coverage rates of 73.1% to 97.1% were achieved by the essential subsets extracted by ESDA-MC for the original time-series data

with all silhouette coefficients exceeding 0.5

indicating satisfactory temporal continuity and inter-cluster separability. Particularly when the number of clusters was set between 3 and 5

silhouette coefficients were maintained above 0.74

the comprehensive index exceeded 0.84

and the output patterns were found to be consistent with sensory classification

fulfilling the CDI criterion. This verifies that the designed sensor array can effectively identify changes in the VOC patterns of Cabernet Sauvignon grapes

providing a technical means for post-harvest quality monitoring and offering a referable solution for the design and effectiveness validation of sensor arrays in similar applications.

关键词

Keywords

references

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