Band width selection data from Near Infra-red Spectral (NIRS) quantitative modelling of energy storage components (protein, lipid, glycogen) for single and multi-bivalve species models

Jill K. Bartlett, William A. Maher, Matthew B.J. Purss

Research output: Contribution to journalArticle

Abstract

Data presented in this article are related to the research article entitled “Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets” [1]. Band width selections were determined using a data-driven approach to modelling Near Infra-red Spectra (NIRS) of protein, lipid and glycogen content in bivalves. Models were produced for single species and combined species of Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia. Band width selection was undertaken using Fourier wavelet transformation coupled with a genetic algorithm (GA) to aggregate adjacent wavelet bands to select the minimum number of IR bands that were consistently identified in the majority of individual spectra.

Original languageEnglish
Pages (from-to)1509-1512
Number of pages4
JournalData in Brief
Volume18
DOIs
Publication statusPublished - Jun 2018

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bivalve
near infrared
lipid
protein
wavelet
modeling
infrared spectroscopy
genetic algorithm
calibration
energy storage

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title = "Band width selection data from Near Infra-red Spectral (NIRS) quantitative modelling of energy storage components (protein, lipid, glycogen) for single and multi-bivalve species models",
abstract = "Data presented in this article are related to the research article entitled “Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets” [1]. Band width selections were determined using a data-driven approach to modelling Near Infra-red Spectra (NIRS) of protein, lipid and glycogen content in bivalves. Models were produced for single species and combined species of Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia. Band width selection was undertaken using Fourier wavelet transformation coupled with a genetic algorithm (GA) to aggregate adjacent wavelet bands to select the minimum number of IR bands that were consistently identified in the majority of individual spectra.",
author = "Bartlett, {Jill K.} and Maher, {William A.} and Purss, {Matthew B.J.}",
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AU - Maher, William A.

AU - Purss, Matthew B.J.

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N2 - Data presented in this article are related to the research article entitled “Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets” [1]. Band width selections were determined using a data-driven approach to modelling Near Infra-red Spectra (NIRS) of protein, lipid and glycogen content in bivalves. Models were produced for single species and combined species of Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia. Band width selection was undertaken using Fourier wavelet transformation coupled with a genetic algorithm (GA) to aggregate adjacent wavelet bands to select the minimum number of IR bands that were consistently identified in the majority of individual spectra.

AB - Data presented in this article are related to the research article entitled “Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets” [1]. Band width selections were determined using a data-driven approach to modelling Near Infra-red Spectra (NIRS) of protein, lipid and glycogen content in bivalves. Models were produced for single species and combined species of Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia. Band width selection was undertaken using Fourier wavelet transformation coupled with a genetic algorithm (GA) to aggregate adjacent wavelet bands to select the minimum number of IR bands that were consistently identified in the majority of individual spectra.

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