Science and Technology of Food Industry

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Volume 44 Issue 16
Aug.  2023
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Abstract
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SHEN Ying, ZHAN Xiuxing, HUANG Chunhong, et al. Rapid Determination of Visible/Near-infrared Snapshot Multispectral Imaging Astaxanthin Content of Haematococcus pluvialis[J]. Science and Technology of Food Industry, 2023, 44(16): 313−320. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022100108.
Citation: SHEN Ying, ZHAN Xiuxing, HUANG Chunhong, et al. Rapid Determination of Visible/Near-infrared Snapshot Multispectral Imaging Astaxanthin Content of Haematococcus pluvialis[J]. Science and Technology of Food Industry, 2023, 44(16): 313−320. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022100108.
shu

Rapid Determination of Visible/Near-infrared Snapshot Multispectral Imaging Astaxanthin Content of Haematococcus pluvialis

  • 1.

    College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

  • 2.

    College of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, China

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  • Received Date: October 13, 2022
  • Available Online: June 10, 2023
    Graphical Abstract
    • Abstract
  • In order to achieve rapid and non-destructive detection of the astaxanthin content in Haematococcus pluvialis, a snapshot multispectral imaging method was proposed in this paper. An imaging system was built by using two snapshot multispectral cameras with visible spectral ranges of 480~635 nm and near-infrared spectral ranges of 665~950 nm, respectively. The spectral data of H. pluvialis samples in different growth periods was collected. To optimize the model prediction, a great variety of methods were compared, including different spectral ranges, three preprocessing methods, two characteristic wavelength selection methods and two modeling methods. The results indicated that the combination of both visible and near-infrared spectroscopy achieved the optimial prediction performance with the pretreatment of first derivation (FD), and the characteristic band selection method of competitive adaptive reweighting sampling (CARS) and modeling method of back propagation (BP) neural network, the prediction set correlation coefficient (Rp) of 0.9622, the root mean square error (RMSEP) of 0.5126 and the residual prediction error (RPD) of 3.6726, which was superior to the visible alone (Rp of 0.9467, RMSEP of 0.6065 and RPD of 3.1042). These indicated that it was feasible to detect the content of astaxanthin in H. pluvialis by the snapshot multispectral imaging technique, and the combination of both visible and near-infrared spectroscopy could be more effective.
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    • References (33)
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