Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds.

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

SUBMITTER: Huang J 

PROVIDER: S-EPMC11765290 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature