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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were...

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Autores principales: Ghidini, Sergio, Chiesa, Luca Maria, Panseri, Sara, Varrà, Maria Olga, Ianieri, Adriana, Pessina, Davide, Zanardi, Emanuela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074186/
https://www.ncbi.nlm.nih.gov/pubmed/33919551
http://dx.doi.org/10.3390/foods10040885
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author Ghidini, Sergio
Chiesa, Luca Maria
Panseri, Sara
Varrà, Maria Olga
Ianieri, Adriana
Pessina, Davide
Zanardi, Emanuela
author_facet Ghidini, Sergio
Chiesa, Luca Maria
Panseri, Sara
Varrà, Maria Olga
Ianieri, Adriana
Pessina, Davide
Zanardi, Emanuela
author_sort Ghidini, Sergio
collection PubMed
description The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg(−1) using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r(2)) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg(−1) and root mean square of cross-validation (RMSECV) ≤ 6 mg kg(−1) were achieved. Both models were optimal also in the validation stage, showing r(2) values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg(−1) and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.
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spelling pubmed-80741862021-04-27 Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy Ghidini, Sergio Chiesa, Luca Maria Panseri, Sara Varrà, Maria Olga Ianieri, Adriana Pessina, Davide Zanardi, Emanuela Foods Article The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg(−1) using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r(2)) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg(−1) and root mean square of cross-validation (RMSECV) ≤ 6 mg kg(−1) were achieved. Both models were optimal also in the validation stage, showing r(2) values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg(−1) and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts. MDPI 2021-04-18 /pmc/articles/PMC8074186/ /pubmed/33919551 http://dx.doi.org/10.3390/foods10040885 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ghidini, Sergio
Chiesa, Luca Maria
Panseri, Sara
Varrà, Maria Olga
Ianieri, Adriana
Pessina, Davide
Zanardi, Emanuela
Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title_full Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title_fullStr Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title_full_unstemmed Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title_short Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy
title_sort histamine control in raw and processed tuna: a rapid tool based on nir spectroscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074186/
https://www.ncbi.nlm.nih.gov/pubmed/33919551
http://dx.doi.org/10.3390/foods10040885
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