A robust fluorescence-based sensing strategy was designed considering
relevance of analyzing chemical additives in industrialized food. In
this study, a sensing approach was developed using fluorescent carbon
quantum dots (CQDs) as a chemometric tool. CQDs were synthesized by a
simple one-step hydrothermal mute using the American natural seed
Caelsalpinia pulcherrima, and further characterized regarding their
chemical structure. Five food additives were identified, citric acid,
lactic acid, ascorbic acid, sodium benzoate and potassium sorbate, which
showed a highly sensitive response with a limit of detection (LOD) as
low as 252 ng mL(-1). The sensing platform was designed using the
supervised method for recognizing patterns of linear discriminant
analysis (LDA), where we could identify different concentrations of
additives, after optimization of experimental parameters. Furthermore,
the sensing strategy successfully identified all tested additives in a
pickled olives sample with 95 % of confidence, where 100 % of
combinations were correctly identified based on classification matrix.
Overall, the obtained results evidence the accuracy and potential of
CQDs-based fluorescence sensing in the identification of food additives.
%0 Journal Article
%1 WOS:000633038000004
%A V, S Carneiro
%A Holanda, M H B
%A Cunha, H O
%A Oliveira, J J P
%A Pontes, S M A
%A Cruz, A A C
%A Fechine, L M U D
%A Moura, T A
%A Paschoal, A R
%A Zambelli, R A
%A Freire, R M
%A Fechine, P B A
%C PO BOX 564, 1001 LAUSANNE, SWITZERLAND
%D 2021
%I ELSEVIER SCIENCE SA
%J JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY A-CHEMISTRY
%K Fluorescence; Food Linear Pickled Sensing additives; analysis; discriminant dots; olives} platform; quantum {Carbon
%R 10.1016/j.jphotochem.2021.113198
%T Highly sensitive sensing of food additives based on fluorescent carbon
quantum dots
%V 411
%X A robust fluorescence-based sensing strategy was designed considering
relevance of analyzing chemical additives in industrialized food. In
this study, a sensing approach was developed using fluorescent carbon
quantum dots (CQDs) as a chemometric tool. CQDs were synthesized by a
simple one-step hydrothermal mute using the American natural seed
Caelsalpinia pulcherrima, and further characterized regarding their
chemical structure. Five food additives were identified, citric acid,
lactic acid, ascorbic acid, sodium benzoate and potassium sorbate, which
showed a highly sensitive response with a limit of detection (LOD) as
low as 252 ng mL(-1). The sensing platform was designed using the
supervised method for recognizing patterns of linear discriminant
analysis (LDA), where we could identify different concentrations of
additives, after optimization of experimental parameters. Furthermore,
the sensing strategy successfully identified all tested additives in a
pickled olives sample with 95 % of confidence, where 100 % of
combinations were correctly identified based on classification matrix.
Overall, the obtained results evidence the accuracy and potential of
CQDs-based fluorescence sensing in the identification of food additives.
@article{WOS:000633038000004,
abstract = {A robust fluorescence-based sensing strategy was designed considering
relevance of analyzing chemical additives in industrialized food. In
this study, a sensing approach was developed using fluorescent carbon
quantum dots (CQDs) as a chemometric tool. CQDs were synthesized by a
simple one-step hydrothermal mute using the American natural seed
Caelsalpinia pulcherrima, and further characterized regarding their
chemical structure. Five food additives were identified, citric acid,
lactic acid, ascorbic acid, sodium benzoate and potassium sorbate, which
showed a highly sensitive response with a limit of detection (LOD) as
low as 252 ng mL(-1). The sensing platform was designed using the
supervised method for recognizing patterns of linear discriminant
analysis (LDA), where we could identify different concentrations of
additives, after optimization of experimental parameters. Furthermore,
the sensing strategy successfully identified all tested additives in a
pickled olives sample with 95 % of confidence, where 100 % of
combinations were correctly identified based on classification matrix.
Overall, the obtained results evidence the accuracy and potential of
CQDs-based fluorescence sensing in the identification of food additives.},
added-at = {2022-05-23T20:00:14.000+0200},
address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
author = {V, S Carneiro and Holanda, M H B and Cunha, H O and Oliveira, J J P and Pontes, S M A and Cruz, A A C and Fechine, L M U D and Moura, T A and Paschoal, A R and Zambelli, R A and Freire, R M and Fechine, P B A},
biburl = {https://www.bibsonomy.org/bibtex/2eb8b1ad54e42d06cf0c36abd28994bd0/ppgfis_ufc_br},
doi = {10.1016/j.jphotochem.2021.113198},
interhash = {5bc9f718a193966d211a81d4b50ea91c},
intrahash = {eb8b1ad54e42d06cf0c36abd28994bd0},
issn = {1010-6030},
journal = {JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY A-CHEMISTRY},
keywords = {Fluorescence; Food Linear Pickled Sensing additives; analysis; discriminant dots; olives} platform; quantum {Carbon},
publisher = {ELSEVIER SCIENCE SA},
pubstate = {published},
timestamp = {2022-05-23T20:00:14.000+0200},
title = {Highly sensitive sensing of food additives based on fluorescent carbon
quantum dots},
tppubtype = {article},
volume = 411,
year = 2021
}