Rapid detection of moisture content of pineapple based on near infrared spectroscopy and SSA-RELM

Abstract

Objective: A method for a fast and non-destructive detection of pineapple moisture content was established. Methods: A novel detection model of pineapple moisture content was proposed based on continuous projection feature wavelength selection and Sparrow search algorithm. Firstly, according to the characteristic of pineapple NIR data with high dimension and redundant information, the results of feature wavelength selection such as successive projections algorithm, principal component analysis and full-band were compared, the selection method of characteristic wavelength of pineapple near infrared spectrum was determined. Secondly, considering that the performance of RELM model was affected by the selection of input layer weight and hidden layer bias, the sparrow search algorithm was used to optimize the input layer weight and hidden layer bias of RELM model, a novel pineapple moisture content detection model based on RELM model improved by sparrow search algorithm was proposed. Results: compared with GA-RELM, PSO-RELM and RELM, the detection model based on SSA-RELM had the highest detection accuracy. Conclusion: RELM model is optimized by sparrow search algorithm can effectively improve the detection accuracy of RELM model .

Keywords

• near infrared spectroscopy
• pineapple
• maltose content
• regularized extreme learning machine
• sparrow search algorithm
• characteristic wavelength
• successive projections algorithm