A comparative analysis of hybrid SVM and LS‐SVM classification algorithms to identify dried wolfberry fruits quality based on hyperspectral imaging technology

Abstract

A very common preservation method used in food processing is the drying method. Hence it is necessary to properly examine and ascertain how the fruit's quality is affected after drying. Hyperspectral imaging (HSI) technology is one of the advanced technologies adopted for assessing food quality. This study explored the impact of standard normalization variate (SNV) and Savitsky–Golay (SG) data preprocessing and optimization methods on spectral data of dried wolfberry fruits’ quality taken in a range of 400.680–1001.612 nm wavelength. Thus, this research demonstrated that using HSI technology and integrating SG-SVN preprocessing methods to the least square-support vector machine (LS-SVM) model could accurately predict dried wolfberry fruit quality. The prediction accuracy of the LS-SVM algorithm coupled with SG-SVN achieved 96.66%, which was the highest classification accuracy. The study results demonstrated that HSI technology combined with the LS-SVM model is feasible for dried wolfberry fruit quality classification. Novelty impact statement Conventional methods of classifying wolfberry fruit quality rely mainly on appearance and human sensory, which is time consuming and lacks accuracy. In this study, hyperspectral imaging technology with the LS-SVM algorithm is used for precise rapid nondestructive classification of wolfberry fruit dried under different temperatures and time frames, respectively.