Data for: Spectra Data Classification with Kernel Extreme Learning

Published: 10 Aug 2019 | Version 1 | DOI: 10.17632/frrv2yd9rg.1

Description of this data

Datasets for Spectra Data Classification:

"FTIR_Spectra_instant_coffee.csv":
contains a collection of 56 mid infrared diffuse reflectance (MIR-DRIFT) spectra of lyophilized coffee produced from two species: arabica (29 samples) and canephora var. robusta (27 samples).
The data are described in full in the journal paper "Near- and Mid-Infrared Spectroscopies in Food Authentication: Coffee Varietal Identification" (Downey G. et al, J. Agric. Food Chem. 45 (11) 4357-4361 (1997)).

"MIRFreshMeats.csv":
Duplicate acquisitions from 60 independent samples. Raw data matrix size [448 x 120]. Obtained using Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) sampling. As described in "Mid-infrared spectroscopy and authenticity problems in selected meats: a feasibility study" Al-Jowder O, Kemsley E K, Wilson R. H.(1997) Food Chemistry 59 195-20.

"MIR_Fruit_Purees.csv":
contains a collection of 983 Mid-infrared spectra collected from different authenticated fruit purees in one of two classes: "Strawberry" (purees prepared from fresh whole fruits by the researchers) and "NON-Strawberry" (diverse collection of other purees, including: strawberry adulterated with other fruits and sugar solutions; raspberry; apple; blackcurrant; blackberry; plum; cherry; apricot; grape juice and mixtures of these.Spectra were acquired from each puree using attenuated total reflectance (ATR) sampling. The acquisition order was randomized with respect to sample type.
The data are described in more detail in the journal paper "Use of Fourier transform infrared spectroscopy and partial least squares regression for the detection of adulteration of strawberry purees" Holland JK, Kemsley EK, Wilson RH. (1998). Journal of the Science of Food and Agriculture, 76, 263-269

"FTIR_Spectra_olive_oils.csv":
contains a collection of 120 Mid-infrared spectra collected from 60 different authenticated extra virgin olive oils, supplied to the Institute of Food Research, UK, by the International Olive Oil Council.Spectra were acquired from each oil using attenuated total reflectance (ATR) sampling. The acquisition order was randomized with respect to the country of origin code. Once all the samples had been examined once, a second acquisition session commenced, to produce a second spectrum from each sample. Again the acquisition order was randomized with respect to country of origin. thus, duplicate spectra were collected from all samples.
The data are described in full in the journal paper "FTIR spectroscopy and multivariate analysis can distinguish the geographic origin of extra virgin olive oils" (Tapp H.S. et al, J. Agric. Food Chem. 51 (21) 6110-5 (2003)).

These data are free to analyse and redistribute for academic purpose; if you do so, please acknowledge the original sources (webpage and/or citation above).

Experiment data files

This data is associated with the following publication:

Spectra data classification with kernel extreme learning machine

Published in: Chemometrics and Intelligent Laboratory Systems

Latest version

  • Version 1

    2019-08-10

    Published: 2019-08-10

    DOI: 10.17632/frrv2yd9rg.1

    Cite this dataset

    Shu, Hongping; Tang, Hong; Zhang, Haiqing; Zheng, Wenbin (2019), “Data for: Spectra Data Classification with Kernel Extreme Learning ”, Mendeley Data, v1 http://dx.doi.org/10.17632/frrv2yd9rg.1

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Categories

Spectroscopy, Machine Learning, Chemometrics

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