{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang J"],"funding":["Jing Huang","Yanqun Xu"],"pagination":["169"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11765290"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(2)"],"pubmed_abstract":["Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from"],"journal":["Foods (Basel, Switzerland)"],"pubmed_title":["Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds."],"pmcid":["PMC11765290"],"funding_grant_id":["XY2022034","2023QL037","2022YFD2100100","LR24C200004","32272776"],"pubmed_authors":["Yang H","Li Z","Wang Q","Huang J","Ding S","Luo Z","Zhang X","Xue Z","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds.","description":"Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2025-04-19T18:20:56.839Z","creation":"2025-04-19T18:20:56.839Z"},"accession":"S-EPMC11765290","cross_references":{"pubmed":["39856837"],"doi":["10.3390/foods14020169"]}}