消费者经常面临缺乏有关超市中苹果质量的信息。一般外观因素,比如颜色,机械损伤,或者微生物攻击,影响消费者是否购买或拒绝苹果的决定。最近,被称为电子鼻的设备基于水果排放的挥发性有机化合物(VOC)提供了易于使用和非破坏性的成熟阶段评估。在这项研究中,\'金色美味\'苹果,在环境温度下存储和监控,在2022年和2023年进行了分析,以收集来自四个金属氧化物半导体(MOS)传感器(MQ3、MQ135、MQ136和MQ138)的数据。三个成熟阶段(不太成熟,成熟,和过熟)是使用主成分分析(PCA)和K均值聚类方法从四个实验中基于传感器测量的各种数据集中识别的。在应用K最近邻(KNN)模型后,结果显示苹果对特定数据集的成功分类,达到75%以上的精度。对于所有实验测量的数据集,在特定的测试集和新的评估集上实现了令人印象深刻的100%准确率,完全独立的实验。此外,相关性和PCA分析表明,选择两个或三个传感器可以提供同样成功的结果。总的来说,电子鼻结果强调了分析苹果收获后较长时间内进行的几个实验数据的重要性。在研究的VOC参数(乙烯,酯类,酒精,和醛)对于秋季或春季分析的较成熟或较成熟的苹果,这可以提高对成熟阶段的确定,对春季调查的苹果具有更高的预测成功率。
Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the \'Golden Delicious\' apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring.