Apple fresh odor recognition system based on SFLA optimized BP neural network

LIU Yungang; WANG Wei

doi:10.13873/J.1000-9787(2020)08-0096-04

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Apple fresh odor recognition system based on SFLA optimized BP neural network

更新时间：2025-09-11

- Apple fresh odor recognition system based on SFLA optimized BP neural network
- Issue 8, Pages: 96-99+106(2020)
- 作者机构：
  
  中北大学信息与通信工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)08-0096-04
  CLC： TP183;TP212;TS255.7
- Published：2020
- 稿件说明：
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LIU Yungang, WANG Wei. Apple fresh odor recognition system based on SFLA optimized BP neural network[J]. 2020, (8): 96-99+106.
DOI：

LIU Yungang, WANG Wei. Apple fresh odor recognition system based on SFLA optimized BP neural network[J]. 2020, (8): 96-99+106. DOI： 10.13873/J.1000-9787(2020)08-0096-04.

摘要

提出了一种苹果鲜度检测的气味识别系统

用于苹果品质的快速评价。系统由连接到数据采集系统的气体传感器阵列和带有模式识别软件的计算机组成。通过采集和检测苹果散发出的香气

使用稳定的系统响应值来建立聚类分析和分类模型。采用连续投影算法(SPA)对传感器阵列进行了优化

优化后的传感器阵列可用于区分苹果的新鲜度。建立了反向传播(BP)神经网络分类模型。基于优化的传感器阵列

测试准确度达到98.67%

能够准确区分不同新鲜度的苹果

可作为苹果品质的快速评价方法。

Abstract

An odor recognition system for apple freshness detection is proposed for rapid evaluation of apple quality. The system consists of a gas sensor array connected to a data acquisition system and a computer with pattern recognition software. By collecting and detecting the aromas emitted by apples

stable system response values are used to set up cluster analysis and classification models. The successive projections algorithm（ SPA） is employed to optimize the sensors array

and the results proved that the optimized sensor array is feasible to distinguish freshness of apple. Back propagation（ BP） neural network classification model is built. Accuracy of testing reach 98. 67% based on the optimized sensor array

and this study can accurately distinguish apples with different freshness. It can be used as fast evaluation method for apple quality.

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