Near infrared sensors for the precise characterization of salt content in canned tuna fish

Abstract

Non-invasive technologies could help to guarantee quality standards of canned tuna fish. The aim of this study was to investigate the ability of bench-top (FT-NIR) and low-cost (LC-NIR) near infrared spectrometers to determine salt content and texture in canned tuna. Salt content distribution was also investigated using hyperspectral imaging (HSI) and computed tomography. Spectra were acquired on canned tuna and reference analysis performed. Partial least squares regression and discriminant analysis were used to develop salt content predictive and texture classification models. Salt content predictive errors were 0.10%, 0.22% and 0.22% for FT-NIR, LC-NIR and HSI, respectively. Salt content was not always homogeneously distributed in the can which was attributed to the salt content differences between internal and external parts of the tuna fish. Low-cost sensors could be a suitable solution to standardise the production and enable precise nutritional labelling, but more sophisticated algorithms are needed to identify textural defects.This work was supported by R&D Director (L. Caillaud) and R&D Manager (I. Lopez-Salgueiro) of Bolton Food, CCLabel project (RTI-2018-096883-R-C41), consolidated Research Group (2021 SGR 00461) and CERCA programme from Generalitat de Catalunya. Acknowledgements are extended Ministerio de Ciencia e Innovación for financing the doctorate studies of Mar Giró-Candanedo (Spanish Government, PRE2019-091224).info:eu-repo/semantics/acceptedVersio