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Protein content determination in Brassica oleracea species using FT‐NIR technique and PLS regression
Author(s) -
Szigedi Tamás,
Lénárt József,
Dernovics Mihály,
Turza Sándor,
Fodor Marietta
Publication year - 2012
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/j.1365-2621.2011.02848.x
Subject(s) - partial least squares regression , kjeldahl method , brassica oleracea , calibration , mathematics , coefficient of determination , near infrared spectroscopy , analytical chemistry (journal) , cultivar , mean squared error , root mean square , chemistry , botany , chromatography , statistics , biology , physics , organic chemistry , neuroscience , nitrogen , quantum mechanics
Summary Six fresh and one frozen vegetable cultivar groups possessing remarkably different morphology from the same Brassica oleracea species, including broccoli, Brussels sprouts, curly cabbage, white cabbage, red cabbage, cauliflower and white kohlrabi, were chosen to set up a Fourier transform near‐infrared spectroscopy (FT‐NIR)‐based method for the quantification of protein content. Sample preparation was based on lyophilisation and homogenisation. Calibration was set up with the help of the Kjeldahl method for the quantification of protein content in the range of 12.9–32.5 m/m%. Calibration model was developed using the spectral regions 1136–1334 and 1639–1836 nm, with partial least squares regression. This model was checked by cross‐validation. The performance of the final FT‐NIR estimation model was evaluated by root mean square of cross‐validation, root‐mean‐square error of estimation and the determination coefficient ( R 2 ). The final estimation function for the protein determination was characterised with the predictive error of 0.76 m/m% and R 2 value of 98.81.