Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy

Kaixu Zhang, Hongzhe Jiang, Haicheng Zhang, Zequn Zhao, Yingjie Yang, Shudan Guo and Wei Wang
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Kaixu Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Hongzhe Jiang: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Haicheng Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Zequn Zhao: College of Engineering, China Agricultural University, Beijing 100083, China
Yingjie Yang: College of Engineering, China Agricultural University, Beijing 100083, China
Shudan Guo: College of Engineering, China Agricultural University, Beijing 100083, China
Wei Wang: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2022, vol. 12, issue 4, 1-17

Abstract: Apple moldy core disease is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease.

Keywords: NIRS; apple; moldy core; online detection; fruit orientation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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