Qualitative and quantitative multivariate strategies for determining paprika adulteration with SUDAN I and II dyes

Márquez, Cristina D.; Ruisánchez, Itziar; Callao, M. Pilar

doi:10.1016/j.microc.2018.11.034

Cited by 22 publications

(5 citation statements)

References 20 publications

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“…PLS was considered as the finest method for finding the quantitative model of adulteration levels in tomato paste and showed the best simplification ability. This is due to that PLS can pick up in high‐dimensional characteristic space with less training samples (Márquez, Ruisánchez, & Callao, ). By addingthe different levels of adulteration in tomato paste, physical parameters of the tomato paste, reduced by reducing adhesion (Ghasemi‐Varnamkhasti et al, ) so physical parameters can be checked in future studies.…”

Section: Resultsmentioning

confidence: 99%

Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste

Mohammad‐Razdari

Ghasemi‐Varnamkhasti

Yoosefian

et al. 2019

J Food Process Engineering

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In this study, adulteration in tomato paste was investigated using a gas sensors array. For this purpose, olfactory machine system was evaluated based on five gas sensors to investigate pumpkin, potato, and starch adulteration (0, 5, 10, 15, and 20%). Principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and partial least square (PLS) methods were used for classification and analysis of sensors response. The use of PCA method in the classification of pumpkin, potato, and starch adulteration has explained 99% of the variance between the data. Also, TGS2610, MQ3, TGS2620, and TGS2600 sensors are widely used for classification. LDA method precision for pumpkin, potato, and starch adulteration classification was 79.07, 83.87, and 92.22%, respectively. In SVM method, C-SVM function as polynomial function and Nu-SVM as radial base function were used for pumpkin classification with 98.84 and 88.14% precision, respectively. In the classification of potato adulteration, the polynomial function had 90.32 and 83.87% precision in C-SVM and 96.77 and 86.02% precision in Nu-SVM. Finally, the two TGS2610 and MQ3 sensors had the best results for classification by PLS. Among the methods of classification, PCA method for categorizing pumpkin adulteration and PLS method in the classification of potato and starch adulteration had the best results. Practical applicationsIn this study, an electronic nose system was examined based on five metal oxide semiconductor sensors to recognize pumpkin, potato, and starch adulteration in tomato paste in combination with pattern recognition methods including PCA, LDA, SVM, and PLS.According to results, it is recommended to use TGS2610 and MQ-3 sensors combined with PCA and SVM method in the design and development of the electronic nose system. Also, potential of the electronic nose system uses for food adulteration monitoring.

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Section: Resultsmentioning

confidence: 99%

Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste

Mohammad‐Razdari

Ghasemi‐Varnamkhasti

Yoosefian

et al. 2019

J Food Process Engineering

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“…Marquez et al. (2019) conducted a study to classify paprika samples adulterated with Sudan dyes by using UV‐visible spectral reflectance. Lohumi et al.…”

Section: Resultsmentioning

confidence: 99%

Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study

Keskin

Arslan

Soysal

et al. 2021

Food Processing Preservation

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“…Standard error of prediction (SEP), RPD, and range error ratio (RER) as the standard deviation ratio of the reference values and the range of reference values to RMSEP, respectively, were also determined. The LOD were calculated according to Allegrini and Olivieri ( 2014 ) and Márquez et al ( 2019 ) at the 95% confidence level.…”

Section: Methodsmentioning

confidence: 99%

Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours

Xie

Tan

2022

J Consum Prot Food Saf

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This article describes a novel front-face synchronous fluorescence spectroscopy (FFSFS) method for the fast and non-invasive authentication of ground roasted Arabica coffee adulterated with roasted maize and soybean flours. The detection was based on the different composition of fluorescent Maillard reaction products and caffeine in roasted coffee and cereal flours. For each roasted maize or soybean adulterant flour (5–40 wt%), principal component analysis coupled with linear discriminant analysis (PCA–LDA) was used for qualitative discrimination. Quantitative prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square regression (PLSR), followed by fivefold cross-validation and external validation. The PLSR models produced suitable results, with the determination coefficient of prediction ( R p 2 ) > 0.9, root mean square error of prediction (RMSEP) < 5%, relative error of prediction (REP) < 25% and residual predictive deviation (RPD) > 3. The limits of detection (LOD) were both 10% for roasted maize and soybean flours. Most relative errors for the prediction of simulated blind samples were between -30% and + 30%. The benefits of this strategy are simplicity, rapidity, and non-destructive detection. However, owing to the high similarity between roasted coffee and roasted cereal flours and the influence of the roasting degree on fluorescent Maillard reaction products, its application is limited to the preliminary screening of roasted coffee with the same roasting degree, adulterated with relatively large amounts of roasted cereal flours which are roasted to analogous color to the coffee. Supplementary Information The online version contains supplementary material available at 10.1007/s00003-022-01396-8.

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Qualitative and quantitative multivariate strategies for determining paprika adulteration with SUDAN I and II dyes

Cited by 22 publications

References 20 publications

Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste

Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste

Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study

Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours

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