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ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder

In a preliminary study, commercial insect powders were successfully identified using infrared spectroscopy combined with multivariate analysis. Nonetheless, it is necessary to check if this technology is capable of discriminating, predicting, and quantifying insect species once they are used as an i...

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Autores principales: García-Gutiérrez, Nerea, Mellado-Carretero, Jorge, Bengoa, Christophe, Salvador, Ana, Sanz, Teresa, Wang, Junjing, Ferrando, Montse, Güell, Carme, de Lamo-Castellví, Sílvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394341/
https://www.ncbi.nlm.nih.gov/pubmed/34441584
http://dx.doi.org/10.3390/foods10081806
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author García-Gutiérrez, Nerea
Mellado-Carretero, Jorge
Bengoa, Christophe
Salvador, Ana
Sanz, Teresa
Wang, Junjing
Ferrando, Montse
Güell, Carme
de Lamo-Castellví, Sílvia
author_facet García-Gutiérrez, Nerea
Mellado-Carretero, Jorge
Bengoa, Christophe
Salvador, Ana
Sanz, Teresa
Wang, Junjing
Ferrando, Montse
Güell, Carme
de Lamo-Castellví, Sílvia
author_sort García-Gutiérrez, Nerea
collection PubMed
description In a preliminary study, commercial insect powders were successfully identified using infrared spectroscopy combined with multivariate analysis. Nonetheless, it is necessary to check if this technology is capable of discriminating, predicting, and quantifying insect species once they are used as an ingredient in food products. The objective of this research was to study the potential of using attenuated total reflection Fourier transform mid-infrared spectroscopy (ATR-FTMIR) combined with multivariate analysis to discriminate doughs and 3D-printed baked snacks, enriched with Alphitobius diaperinus and Locusta migratoria powders. Several doughs were made with a variable amount of insect powder (0–13.9%) replacing the same amount of chickpea flour (46–32%). The spectral data were analyzed using soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) algorithms. SIMCA models successfully discriminated the insect species used to prepare the doughs and snacks. Discrimination was mainly associated with lipids, proteins, and chitin. PLSR models predicted the percentage of insect powder added to the dough and the snacks, with determination coefficients of 0.972, 0.979, and 0.994 and a standard error of prediction of 1.24, 1.08, and 1.90%, respectively. ATR-FTMIR combined with multivariate analysis has a high potential as a new tool in insect product authentication.
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spelling pubmed-83943412021-08-28 ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder García-Gutiérrez, Nerea Mellado-Carretero, Jorge Bengoa, Christophe Salvador, Ana Sanz, Teresa Wang, Junjing Ferrando, Montse Güell, Carme de Lamo-Castellví, Sílvia Foods Article In a preliminary study, commercial insect powders were successfully identified using infrared spectroscopy combined with multivariate analysis. Nonetheless, it is necessary to check if this technology is capable of discriminating, predicting, and quantifying insect species once they are used as an ingredient in food products. The objective of this research was to study the potential of using attenuated total reflection Fourier transform mid-infrared spectroscopy (ATR-FTMIR) combined with multivariate analysis to discriminate doughs and 3D-printed baked snacks, enriched with Alphitobius diaperinus and Locusta migratoria powders. Several doughs were made with a variable amount of insect powder (0–13.9%) replacing the same amount of chickpea flour (46–32%). The spectral data were analyzed using soft independent modeling of class analogy (SIMCA) and partial least squares regression (PLSR) algorithms. SIMCA models successfully discriminated the insect species used to prepare the doughs and snacks. Discrimination was mainly associated with lipids, proteins, and chitin. PLSR models predicted the percentage of insect powder added to the dough and the snacks, with determination coefficients of 0.972, 0.979, and 0.994 and a standard error of prediction of 1.24, 1.08, and 1.90%, respectively. ATR-FTMIR combined with multivariate analysis has a high potential as a new tool in insect product authentication. MDPI 2021-08-05 /pmc/articles/PMC8394341/ /pubmed/34441584 http://dx.doi.org/10.3390/foods10081806 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
García-Gutiérrez, Nerea
Mellado-Carretero, Jorge
Bengoa, Christophe
Salvador, Ana
Sanz, Teresa
Wang, Junjing
Ferrando, Montse
Güell, Carme
de Lamo-Castellví, Sílvia
ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title_full ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title_fullStr ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title_full_unstemmed ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title_short ATR-FTIR Spectroscopy Combined with Multivariate Analysis Successfully Discriminates Raw Doughs and Baked 3D-Printed Snacks Enriched with Edible Insect Powder
title_sort atr-ftir spectroscopy combined with multivariate analysis successfully discriminates raw doughs and baked 3d-printed snacks enriched with edible insect powder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394341/
https://www.ncbi.nlm.nih.gov/pubmed/34441584
http://dx.doi.org/10.3390/foods10081806
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