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J Sci Food Agric. 2019 Mar 15;99(4):1709-1718. doi: 10.1002/jsfa.9360. Epub 2018 Oct 30.

Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging.

Pan X1, Sun L1, Li Y2,3, Che W1, Ji Y1, Li J1, Li J1, Xie X1, Xu Y1.

Author information

1
Key Laboratory of Electronics Engineering, College of Heilongjiang Province, Heilongjiang University, Harbin, China.
2
Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing, China.
3
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.

Abstract

BACKGROUND:

Bruising time of apple is one of the most important factors for internal quality assessment. The present study aimed to establish a non-destructive method for the classification of apple bruising time using visible and near-infrared (VNIR) hyperspectral imaging. In this study, VNIR hyperspectral images were obtained and analyzed at seven bruising periods. Moreover, regions of interest (ROIs) were chosen to construct the bruised region classification model, and spectra of bruised regions were collected and resampled based on four different methods. Subsequently, machine learning algorithms were employed and used for dealing with the time classification model of apples. In order to reduce data redundancy and improve the accuracy of the classification model, a tree-based assembling learning model was used to select feature wavelengths, and linear discriminant analysis (LDA) was used to improve the discernibility of data.

RESULTS:

The results revealed that the random forest (RF) model can precisely locate bruised regions, while the gradient boosting decision tree (GBDT) model can validly classify apple bruising times with 70.59% accuracy. Data of 128 wavebands were compressed to 13 wavebands, providing a high accuracy of 92.86%.

CONCLUSION:

The results prove that the hyperspectral technique can be used for predicting apple bruising time, which will help to assess the internal quality and safety of apples. © 2018 Society of Chemical Industry.

KEYWORDS:

bruised region extraction; bruising time; feature wavelength selection; gradient boosting decision tree; hyperspectral imaging; spectra pretreatment

PMID:
30221355
DOI:
10.1002/jsfa.9360
[Indexed for MEDLINE]

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