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.

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.

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.

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.

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.

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.