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Non-destructive detection and classification of in-shell insect-infested almonds based on multispectral imaging technology

Published online by Cambridge University Press:  11 February 2019

J. Yu ,
S. Ren ,
C. Liu ,
B. Wei ,
L. Zhang ,
S. Younas  and
L. Zheng
J. Yu
Affiliation:
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
S. Ren
Affiliation:
Three Squirrels Co., Ltd., Wuhu, 241000, China
C. Liu*
Affiliation:
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
B. Wei
Affiliation:
Three Squirrels Co., Ltd., Wuhu, 241000, China
L. Zhang
Affiliation:
Three Squirrels Co., Ltd., Wuhu, 241000, China
S. Younas
Affiliation:
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
L. Zheng*
Affiliation:
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
*
Author for correspondence: C. Liu, E-mail: [email protected]; L. Zheng, E-mail: [email protected]; [email protected]
Author for correspondence: C. Liu, E-mail: [email protected]; L. Zheng, E-mail: [email protected]; [email protected]
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Abstract

The feasibility of non-destructive detection and classification of in-shell insect-infested almonds was examined by using multispectral imaging (MSI) technology combined with chemometrics. Differentiation of reflectance spectral data between intact and insect-infested almonds was attempted by using analytical approaches based on principal component analysis and support vector machines, classification accuracy rates as high as 99.1% in the calibration set and 97.5% in the prediction set were achieved. Meanwhile, the in-shell almonds were categorized into three classes (intact, slightly infested and severely infested) based on the degree of damage caused by insect infestation and were characterized quantitatively by the analysis of shell/kernel weight ratio. A three-class model for the identification of intact, slightly infested and severely infested almonds yielded acceptable classification performance (95.6% accuracy in the calibration set and 93.3% in the prediction set). These results revealed that MSI technology combined with chemometrics may be a promising approach for the non-destructive detection of hidden insect damage in almonds and could be used for industrial applications.

Keywords

Chemometricsinsect infestationin-shell almondsmultispectral imaging
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 9 , November 2018 , pp. 1103 - 1110
Copyright
Copyright © Cambridge University Press 2019 

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