Near infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets

J.K. Bartlett, W.A. Maher, M.B.J. Purss

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    3 Citations (Scopus)

    Abstract

    Near infrared spectroscopy (NIRS) quantitative modelling was used to measure the protein, lipid and glycogen composition of five marine bivalve species (Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia) from multiple locations and seasons. Predictive models were produced for each component using individual species and aggregated sample populations for the three oyster species (S. glomerata, O. angasi and C. gigas) and for all five bivalve species. Whole animal tissues were freeze dried, ground to > 20 μm and scanned by NIRS. Protein, lipid and glycogen composition were determined by traditional chemical analyses and calibration models developed to allow rapid NIRS-measurement of these components in the five bivalve species. Calibration modelling was performed using wavelet selection, genetic algorithms and partial least squares analysis. Model quality was assessed using RPIQ and RMESP. For protein composition, single species model results had RPIQ values between 2.4 and 3.5 and RMSEP between 8.6 and 18%, the three oyster model had an RPIQ of 2.6 and an RMSEP of 10.8% and the five bivalve species had an RPIQ of 3.6 and RMSEP of 8.7% respectively. For lipid composition, single species models achieved RPIQ values between 2.9 and 5.3 with RMSEP between 9.1 and 11.2%, the oyster model had an RPIQ of 3.6 and RMSEP of 6.8 and the five bivalve model had an RPIQ of 5.2 and RMSEP of 6.8% respectively. For glycogen composition, the single species models had RPIQs between 3.8 and 18.9 with RMSEP between 3.5 and 9.2%, the oyster model had an RPIQ of 5.5 and RMSEP of 7.1% and the five bivalve model had an RPIQ of 4 and RMSEP of 7.6% respectively. Comparison between individual species models and aggregated models for three oyster species and five bivalve species for each component indicate that aggregating data from like species produces high quality models with robust and reliable quantitative application. The benefit of aggregated multi-species models include a greater range of bivalve composition, greater application to different bivalve species and reduced need to extensively sample individual species, that is required for obtain robust single species NIRS models.

    Original languageEnglish
    Pages (from-to)537-557
    Number of pages21
    JournalSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
    Volume193
    DOIs
    Publication statusPublished - 2018

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    glycogens
    Near infrared spectroscopy
    Glycogen
    Lipids
    lipids
    infrared spectroscopy
    Calibration
    proteins
    Proteins
    Chemical analysis

    Cite this

    @article{0634dc547fb64c5ab69c87f4f2e3805b,
    title = "Near infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets",
    abstract = "Near infrared spectroscopy (NIRS) quantitative modelling was used to measure the protein, lipid and glycogen composition of five marine bivalve species (Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia) from multiple locations and seasons. Predictive models were produced for each component using individual species and aggregated sample populations for the three oyster species (S. glomerata, O. angasi and C. gigas) and for all five bivalve species. Whole animal tissues were freeze dried, ground to > 20 μm and scanned by NIRS. Protein, lipid and glycogen composition were determined by traditional chemical analyses and calibration models developed to allow rapid NIRS-measurement of these components in the five bivalve species. Calibration modelling was performed using wavelet selection, genetic algorithms and partial least squares analysis. Model quality was assessed using RPIQ and RMESP. For protein composition, single species model results had RPIQ values between 2.4 and 3.5 and RMSEP between 8.6 and 18{\%}, the three oyster model had an RPIQ of 2.6 and an RMSEP of 10.8{\%} and the five bivalve species had an RPIQ of 3.6 and RMSEP of 8.7{\%} respectively. For lipid composition, single species models achieved RPIQ values between 2.9 and 5.3 with RMSEP between 9.1 and 11.2{\%}, the oyster model had an RPIQ of 3.6 and RMSEP of 6.8 and the five bivalve model had an RPIQ of 5.2 and RMSEP of 6.8{\%} respectively. For glycogen composition, the single species models had RPIQs between 3.8 and 18.9 with RMSEP between 3.5 and 9.2{\%}, the oyster model had an RPIQ of 5.5 and RMSEP of 7.1{\%} and the five bivalve model had an RPIQ of 4 and RMSEP of 7.6{\%} respectively. Comparison between individual species models and aggregated models for three oyster species and five bivalve species for each component indicate that aggregating data from like species produces high quality models with robust and reliable quantitative application. The benefit of aggregated multi-species models include a greater range of bivalve composition, greater application to different bivalve species and reduced need to extensively sample individual species, that is required for obtain robust single species NIRS models.",
    keywords = "Bivalve, Glycogen, Lipid, Near infrared spectroscopy, Protein",
    author = "J.K. Bartlett and W.A. Maher and M.B.J. Purss",
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    T1 - Near infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets

    AU - Bartlett, J.K.

    AU - Maher, W.A.

    AU - Purss, M.B.J.

    N1 - cited By 0

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    N2 - Near infrared spectroscopy (NIRS) quantitative modelling was used to measure the protein, lipid and glycogen composition of five marine bivalve species (Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia) from multiple locations and seasons. Predictive models were produced for each component using individual species and aggregated sample populations for the three oyster species (S. glomerata, O. angasi and C. gigas) and for all five bivalve species. Whole animal tissues were freeze dried, ground to > 20 μm and scanned by NIRS. Protein, lipid and glycogen composition were determined by traditional chemical analyses and calibration models developed to allow rapid NIRS-measurement of these components in the five bivalve species. Calibration modelling was performed using wavelet selection, genetic algorithms and partial least squares analysis. Model quality was assessed using RPIQ and RMESP. For protein composition, single species model results had RPIQ values between 2.4 and 3.5 and RMSEP between 8.6 and 18%, the three oyster model had an RPIQ of 2.6 and an RMSEP of 10.8% and the five bivalve species had an RPIQ of 3.6 and RMSEP of 8.7% respectively. For lipid composition, single species models achieved RPIQ values between 2.9 and 5.3 with RMSEP between 9.1 and 11.2%, the oyster model had an RPIQ of 3.6 and RMSEP of 6.8 and the five bivalve model had an RPIQ of 5.2 and RMSEP of 6.8% respectively. For glycogen composition, the single species models had RPIQs between 3.8 and 18.9 with RMSEP between 3.5 and 9.2%, the oyster model had an RPIQ of 5.5 and RMSEP of 7.1% and the five bivalve model had an RPIQ of 4 and RMSEP of 7.6% respectively. Comparison between individual species models and aggregated models for three oyster species and five bivalve species for each component indicate that aggregating data from like species produces high quality models with robust and reliable quantitative application. The benefit of aggregated multi-species models include a greater range of bivalve composition, greater application to different bivalve species and reduced need to extensively sample individual species, that is required for obtain robust single species NIRS models.

    AB - Near infrared spectroscopy (NIRS) quantitative modelling was used to measure the protein, lipid and glycogen composition of five marine bivalve species (Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia) from multiple locations and seasons. Predictive models were produced for each component using individual species and aggregated sample populations for the three oyster species (S. glomerata, O. angasi and C. gigas) and for all five bivalve species. Whole animal tissues were freeze dried, ground to > 20 μm and scanned by NIRS. Protein, lipid and glycogen composition were determined by traditional chemical analyses and calibration models developed to allow rapid NIRS-measurement of these components in the five bivalve species. Calibration modelling was performed using wavelet selection, genetic algorithms and partial least squares analysis. Model quality was assessed using RPIQ and RMESP. For protein composition, single species model results had RPIQ values between 2.4 and 3.5 and RMSEP between 8.6 and 18%, the three oyster model had an RPIQ of 2.6 and an RMSEP of 10.8% and the five bivalve species had an RPIQ of 3.6 and RMSEP of 8.7% respectively. For lipid composition, single species models achieved RPIQ values between 2.9 and 5.3 with RMSEP between 9.1 and 11.2%, the oyster model had an RPIQ of 3.6 and RMSEP of 6.8 and the five bivalve model had an RPIQ of 5.2 and RMSEP of 6.8% respectively. For glycogen composition, the single species models had RPIQs between 3.8 and 18.9 with RMSEP between 3.5 and 9.2%, the oyster model had an RPIQ of 5.5 and RMSEP of 7.1% and the five bivalve model had an RPIQ of 4 and RMSEP of 7.6% respectively. Comparison between individual species models and aggregated models for three oyster species and five bivalve species for each component indicate that aggregating data from like species produces high quality models with robust and reliable quantitative application. The benefit of aggregated multi-species models include a greater range of bivalve composition, greater application to different bivalve species and reduced need to extensively sample individual species, that is required for obtain robust single species NIRS models.

    KW - Bivalve

    KW - Glycogen

    KW - Lipid

    KW - Near infrared spectroscopy

    KW - Protein

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    UR - http://www.mendeley.com/research/near-infrared-spectroscopy-quantitative-modelling-bivalve-protein-lipid-glycogen-composition-using-s

    U2 - 10.1016/j.saa.2017.12.046

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    M3 - Article

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    JO - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

    JF - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

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