Trebar Mira, Žalik Anamarie, Vidrih Rajko
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.
Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia.
Foods. 2024 Aug 14;13(16):2530. doi: 10.3390/foods13162530.
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.
消费者在超市选购苹果时，常常面临苹果质量信息匮乏的问题。诸如颜色、机械损伤或微生物侵害等外观因素，会影响消费者决定购买还是拒绝这些苹果。近来，一种名为电子鼻的设备，能基于水果释放的挥发性有机化合物（VOCs），对苹果的成熟阶段进行简便且无损的评估。在本研究中，于2022年和2023年对在室温下储存和监测的“金冠”苹果进行了分析，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。在四个实验中，基于传感器测量的各种数据集，使用主成分分析（PCA）和K均值聚类方法，确定了三个成熟阶段（欠熟、成熟和过熟）。应用K近邻（KNN）模型后，结果表明针对特定数据集的苹果分类成功，准确率高于75%。对于包含所有实验测量值的数据集，在特定测试集以及来自全新、完全独立实验的评估集上，实现了令人印象深刻的100%准确率。此外，相关性和PCA分析表明，选择两到三个传感器也能获得同样成功的结果。总体而言，电子鼻的结果凸显了在苹果收获后较长一段时间内，对多个实验数据进行分析的重要性。在秋季或春季分析的成熟度较低或较高的苹果中，所研究的VOC参数（乙烯、酯类、醇类和醛类）存在异同，这有助于提高春季所研究苹果成熟阶段的判定，且预测成功率更高。
