Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction

Liulei Pan; Hao Li; Juan Zhao

doi:10.1016/j.saa.2023.123075

Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction

Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 5:302:123075. doi: 10.1016/j.saa.2023.123075. Epub 2023 Jul 5.

Authors

Liulei Pan¹, Hao Li², Juan Zhao³

Affiliations

¹ Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China. Electronic address: panliulei@nwafu.edu.cn.
² Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China. Electronic address: 2841284875@qq.com.
³ Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China. Electronic address: zhaojuan@nwsuaf.edu.cn.

PMID: 37423101
DOI: 10.1016/j.saa.2023.123075

Abstract

Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.

Keywords: Apple; Data fusion; Model correction; Ripeness assessment; Spectroscopy.