RT info:eu-repo/semantics/article
T1 Detection of cold chain breaks using partial least squares-class modelling based on biogenic amine profiles in tuna
A1 Reguera Alonso, Celia
A1 Sanllorente Méndez, Silvia
A1 Herrero Gutiérrez, Ana
A1 Sarabia Peinador, Luis Antonio
A1 Ortiz Fernández, Mª Cruz
K1 Biogenic amines
K1 Cold chain
K1 Partial least squares - class modelling
K1 HPLC-FLD
K1 Parallel factor analysis
K1 Spoilage of tuna
K1 Química analítica
K1 Chemistry, Analytic
AB The maintenance of the cold chain is essential to ensure foodstuff conformity and safety. However, gaps in the cold chain may be expected so designing analytical methods capable to detect cold chain breaks is a worthwhile issue. In this paper, the possibility of using the amount of nine biogenic amines (BAs) determined in Thunnus albacares by HPLC-FLD for detecting cold chain breaks is approached. Tuna is stored at 3 different temperature conditions for 8 storage periods. The evolution of the content of BAs is analyzed through parallel factor analysis (PARAFAC), in such a way that storage temperature, BAs and storage time profiles are estimated. PARAFAC has made it possible to observe two spoilage routes with different relative evolution of BAs. In addition, it has enabled to estimate the storage time, by considering the three storage temperatures, with errors of 0.5 and 1.0 days in fitting and in prediction, respectively. Furthermore, a class-modelling technique based on partial least squares is sequentially applied to decide, from the amount of BAs, if there has been a cold chain break. Firstly, samples stored at 25 °C are statistically discriminated from those kept at 4 °C and −18 °C; next, frozen samples are distinguished from those refrigerated. In the first case, the probabilities of false non-compliance and false compliance are almost zero, whereas in the second one, both probabilities are 10%. Globally, the results of this work have pointed out the feasibility of using the amount of BAs together with PLS-CM to decide if the cold chain has been maintained or not.
PB Elsevier
SN 0039-9140
YR 2019
FD 2019-09
LK http://hdl.handle.net/10259/5092
UL http://hdl.handle.net/10259/5092
LA eng
NO Agencia Estatal de Investigación of Spanish Ministerio de Economía, Industria y Competitividad, Gobierno de España [project CTQ2017-88894-R] and Consejería de Educación de la Junta de Castilla y León [project BU012P17] both co-financed with European Regional Development Fund
DS Repositorio Institucional de la Universidad de Burgos
RD 21-nov-2024