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Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas)

  • Lipin Chen
  • Zhaojie Li
  • Fanqianhui Yu
  • Xu Zhang
  • Yong Xue
  • Changhu Xue
Article
  • 100 Downloads

Abstract

Total volatile basic nitrogen (TVB-N) content is used to evaluate Pacific oyster (Crassostrea gigas) freshness. In this work, hyperspectral imaging (HSI; 400–1000 nm) was used to measure the TVB-N content in Pacific oysters. Accordingly, Pacific oyster samples stored in 15 °C water were assessed at intervals after 1, 3, 5, 7, or 9 days. Minimum noise separation processing of the hyperspectral images was performed before determining the region of interest for data dimension reduction. The effects of multiplicative scatter correction (MSC) on the obtained data were then investigated. To simplify the calibration model, 12 characteristic wavelengths were selected from the Pacific oyster hypercube using the correlation coefficient method. Finally, multiple linear regression (MLR) and back-propagation artificial neural network (BP-ANN) models were built from the selected wavelengths. The experimental results showed that the correlation coefficients between the corrected, predicted, and cross-validated datasets were lower in the MLR model than in the BP-ANN. However, the MLR model outperformed the BP-ANN in terms of the root-mean-square errors of correction, prediction, and interaction verification. Overall, both the MLR and BP-ANN models demonstrated that the combination of HSI with chemometric methods can be used to detect and accurately predict Pacific Oyster freshness during storage.

Keywords

Total volatile basic nitrogen Pacific oysters (Crassostrea gigasHyperspectral imaging Multiplicative scatter correction Multiple linear regression Back-propagation artificial neural network 

Notes

Funding Information

This work was financially supported by the earmarked fund for Modern Agro-industry Technology Research System (CARS-49).

Compliance with Ethical Standards

Conflict of Interest

Lipin Chen declares that she has no conflict of interest. Zhaojie Li declares that he has no conflict of interest. Fanqianhui Yu declares that she has no conflict of interest. Xu Zhang declares that she has no conflict of interest Yong Xue declares that he has no conflict of interest. Changhu Xue declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Lipin Chen
    • 1
  • Zhaojie Li
    • 1
  • Fanqianhui Yu
    • 1
  • Xu Zhang
    • 1
  • Yong Xue
    • 1
  • Changhu Xue
    • 1
  1. 1.College of Food Science and EngineeringOcean University of ChinaQingdaoChina

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