Qin Yingdong, Jia Wenshen, Sun Xu, Lv Haolin
Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
College of Computer and Information Engineering, Beijing University of Agriculture, Beijing, China.
Front Nutr. 2023 Jul 31;10:1222988. doi: 10.3389/fnut.2023.1222988. eCollection 2023.
A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.
设计了一种自行研制的便携式电子鼻及其分类模型，用于检测和区分草莓的轻微机械损伤。该电子鼻利用四个金属氧化物传感器和四个专门为草莓检测校准的电化学传感器。选用的草莓使用H2Q - C空气浴振荡器以不同速度进行模拟损伤，然后储存在4°C下，以模拟实际生活中的机械损伤情况。提出了多种特征提取方法，并与主成分分析（PCA）降维相结合进行比较建模。在使用支持向量机（SVM）、K近邻算法（KNN）、线性判别分析（LDA）、朴素贝叶斯和子空间集成等各种模型进行验证后，网格搜索优化的支持向量机（GS - SVM）方法在评估草莓损伤程度时达到了最高分类准确率0.84。此外，特征提取集成分类器在确定草莓损伤时间间隔方面达到了最高分类准确率（0.89）。该实验证明了自行研制的电子鼻用于检测草莓轻微机械损伤的可行性。
