A simple method to quantify azo dyes in spices based on flow injection chromatography combined with chemometric tools.

Para Red (PR) and Sudan dyes have been illegally used as colorants to adulterate certain foods by enhancing their red/orange colour. In addition, they are toxic and carcinogenic. This work presents the development of a simple flow injection chromatographic method combined with chemometric tools to perform the determination of PR, Sudan I (SI) and Sudan II (SII) in food samples. The flow chromatographic system consisted of a low-pressure manifold coupled to a reverse phase monolithic column. A Partial Least Square (PLS) model was applied to resolve overlapped absorption spectra registered for each dye at the corresponding retention time. The relative errors of calibration (RMSECV, %) were 0.49, 0.85 and 0.23, and the relative errors of prediction (RMSEP, %) were 1.12, 0.75 and 0.33 for PR, SI and SII, respectively. The residual predictive deviation (RPD) values obtained were higher than 3.00 for all analytes. The method was successfully applied to quantify the dyes in six different commercial spices samples. The results were compared with the HPLC reference method concluding that there were no significant differences at the studied confidence level (α = 0.05). The proposed method can be used to rapidly determine the analytes in a simple, reliable, low-cost and environmentally-friendly manner. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-021-05299-8.

Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye.

Sudan I is a synthetic-azo dye commonly used to adulterate foods to increase sensory appearance. However, it is banned due to its carcinogenic, mutagenic and genotoxic properties, which represent a serious risk to human health. Thus, this paper proposes a feasibility study to identify and quantify Sudan I dye in ketchup samples using colour histograms (obtained from digital images) and multivariate analysis. The successive projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all samples, while the partial least squares coupled with SPA for interval selection (iSPA-PLS) quantified adequately the adulterant, attaining values of RMSEP of 11.64 mg kg-1, R2 of 0.96, RPD of 5.28, REP of 13.63% and LOD of 39.45 mg kg-1. Therefore, the proposed methodology provides a simple, fast, inexpensive, promising analytical tool for the screening of both the quality and safety of ketchup samples. As a consequence, it can help to protect the consumer's health.

NIR-based Sudan I to IV and Para-Red food adulterants screening.

Spices are added in order to enhance the organoleptic characteristics of food and culinary dishes, making them more attractive for consumers. The use of illicit cheap colourants might be profitable along the food supply chain, posing undue risks to human health. This work evaluates the feasibility of NIR spectroscopy with chemometrics as a rapid, simple, non-destructive and affordable screening tool to determine the presence of Sudan I, II, III, IV and Para-red dyes in paprika. The dataset comprised unadulterated and adulterated samples with the five studied dyes at different concentration levels. Several multivariate classification models were built with Linear Discriminant Analysis (LDA) and different machine learning techniques. Preliminary results show that a classifier based on only six wavenumbers is able to determine the presence of some of these dyes in food samples in levels that may represent risk to human health. Sensitivities and specificities above 90% were obtained in almost all cases. These results show the feasibility of inexpensive and portable devices that can be useful for screening out adulterated stock along the food chain supply.

Standardization of UV-visible data in a food adulteration classification problem.

This study evaluates the performance of multivariate calibration transfer methods in a classification context. The spectral variation caused by some experimental conditions can worsen the performance of the initial multivariate classification model but this situation can be solved by implementing standardization methods such as Piecewise Direct Standardization (PDS). This study looks at the adulteration of culinary spices with banned dyes such as Sudan I, II, III and IV. The samples are characterised by their UV-visible spectra and Partial Least Squares-Discriminant Analysis (PLS-DA) is used to discriminate between unadulterated samples and samples adulterated with any of the four Sudan dyes. Two different datasets that need to be standardised are presented. The standardization process yields positive classification results comparable to those obtained from the initial PLS-DA model, in which high classification performance was achieved.

Surface Enhanced Raman Spectroscopy (SERS) and multivariate analysis as a screening tool for detecting Sudan I dye in culinary spices.

Raman spectroscopy combined with multivariate analysis was evaluated as a tool for detecting Sudan I dye in culinary spices. Three Raman modalities were studied: normal Raman, FT-Raman and SERS. The results show that SERS is the most appropriate modality capable of providing a proper Raman signal when a complex matrix is analyzed. To get rid of the spectral noise and background, Savitzky-Golay smoothing with polynomial baseline correction and wavelet transform were applied. Finally, to check whether unadulterated samples can be differentiated from samples adulterated with Sudan I dye, an exploratory analysis such as principal component analysis (PCA) was applied to raw data and data processed with the two mentioned strategies. The results obtained by PCA show that Raman spectra need to be properly treated if useful information is to be obtained and both spectra treatments are appropriate for processing the Raman signal. The proposed methodology shows that SERS combined with appropriate spectra treatment can be used as a practical screening tool to distinguish samples suspicious to be adulterated with Sudan I dye.

1H NMR variable selection approaches for classification. A case study: the determination of adulterated foodstuffs.

Whenever dealing with large amount of data as is the case of a NMR spectrum, carrying out a variable selection before applying a multivariate technique is necessary. This work applies various variable selection techniques to extract relevant information from (1)H NMR spectral data. Three approaches have been chosen, because each is based on very different foundations. The first method, called Xdiff, is based on calculating the normalized differences between the mean spectrum of a class considered to be the reference and the spectra of each sample. The second approach is the interval Partial Least Squares method (iPLS), which investigates the influential zones of the spectra that contains the most discriminating predictors calculating local PLS-DA models on narrow intervals. The last one is Genetic Algorithms (GAs) which finds the optimal variables from a random initial subset of variables by means of an iterative process. The performance of each variable selection strategy is determined by the classification results obtained when multiclass Partial Least Squares-Discriminant Analysis is applied. This study has been applied to NMR spectra of culinary spices that might be adulterated with banned dyes such as Sudan dyes (I-IV). The three techniques give neither the same number nor the same selected variables, but they do select a common zone from the spectra containing the most discriminating variables. All three techniques give satisfactory classification and prediction results, being higher than 95% with iPLS and GA and around 89% with Xdiff, therefore the three variable selection techniques are suitable to be used with NMR data in the determination of food adulteration with Sudan dyes as well as the specific type of adulterant used (I-IV).

1H NMR and UV-visible data fusion for determining Sudan dyes in culinary spices.

Two data fusion strategies (variable and decision level) combined with a multivariate classification approach (Partial Least Squares-Discriminant Analysis, PLS-DA) have been applied to get benefits from the synergistic effect of the information obtained from two spectroscopic techniques: UV-visible and (1)H NMR. Variable level data fusion consists of merging the spectra obtained from each spectroscopic technique in what is called "meta-spectrum" and then applying the classification technique. Decision level data fusion combines the results of individually applying the classification technique in each spectroscopic technique. Among the possible ways of combinations, we have used the fuzzy aggregation connective operators. This procedure has been applied to determine banned dyes (Sudan III and IV) in culinary spices. The results show that data fusion is an effective strategy since the classification results are better than the individual ones: between 80 and 100% for the individual techniques and between 97 and 100% with the two fusion strategies.

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques.

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L(-1). Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV. Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.