Smartphone-Based Digital Image Analysis for Determination of Some Food Dyes in Commercial Products

An update on the sample preparation and analytical methods for synthetic food colorants in food products

Colorants, especially synthetic colorants, play a crucial role in enhancing the aesthetic qualities of food owing to their cost-effectiveness and stability against environmental factors. Ensuring the safe and regulated use of colorants is essential for maintaining consumer trust in food safety. Various preparation and analytical technologies, which are continuously undergoing improvement, are currently used to quantify of synthetic colorants in food products. This paper reviews recent developments in analytical techniques for synthetic food colorants, detection and compares the operational principles, advantages, and disadvantages of each technology. Additionally, it also explores advancements in these technologies, discussing several invaluable tools of analysis, such as high-performance liquid chromatography, liquid chromatography-tandem mass spectrometry, electrochemical sensors, digital image analysis, near-infrared spectroscopy, and surface-enhanced Raman spectroscopy. This comprehensive overview aims to provide valuable insights into current progress and research in the field of food colorant analysis.

Smartphone-based procedure to determine content of single synthetic dyes in food using the Arata-Possetto extraction method

The use of additives, including dyes, is common in the preparation of food products. The analytical control of artificial food dye content is relevant since some, such as azo dyes, may produce cancer and attention deficit disorders and hyperactivity in children. Consequently, the maximum permitted concentration of azo dyes in food is regulated by current legislation. Therefore, it is of interest to find simple and fast procedures for the control of these compounds. The aim of this study is to determine the concentration of azo dyes in food samples by the Arata-Possetto method - based on the extraction of azo dyes employing natural wool -, followed by the analysis of an image captured by a smartphone camera. After experimentally determining the optimal extraction conditions, the calibration curves for standard solutions of different food dyes and the color of the dyed wool were obtained. Results from dyed wool image processing were compared with the absorbance spectra of the solutions before extraction as measured by a diode array spectrophotometer. The spectrophotometric and the image processing procedures were employed to obtain the calibration curves for different food dyes, which were subsequently employed to analyze food samples. Statistical treatment shows that the results of both methods are comparable.

Using a Smartphone-Based Colorimetric Device with Molecularly Imprinted Polymer for the Quantification of Tartrazine in Soda Drinks

The present study reports the development and application of a rapid, low-cost in-situ method for the quantification of tartrazine in carbonated beverages using a smartphone-based colorimetric device with molecularly imprinted polymer (MIP). The MIP was synthesized using the free radical precipitation method with acrylamide (AC) as the functional monomer, N,N'-methylenebisacrylamide (NMBA) as the cross linker, and potassium persulfate (KPS) as radical initiator. The smartphone (RadesPhone)-operated rapid analysis device proposed in this study has dimensions of 10 × 10 × 15 cm and is illuminated internally by light emitting diode (LED) lights with intensity of 170 lux. The analytical methodology involved the use of a smartphone camera to capture images of MIP at various tartrazine concentrations, and the subsequent application of the Image-J software to calculate the red, green, blue (RGB) color values and hue, saturation, value (HSV) values from these images. A multivariate calibration analysis of tartrazine in the range of 0 to 30 mg/L was performed, and the optimum working range was determined to be 0 to 20 mg/L using five principal components and a limit of detection (LOD) of 1.2 mg/L was obtained. Repeatability analysis of tartrazine solutions with concentrations of 4, 8, and 15 mg/L (n = 10) showed a coefficient of variation (% RSD) of less than 6%. The proposed technique was applied to the analysis of five Peruvian soda drinks and the results were compared with the UHPLC reference method. The proposed technique showed a relative error between 6% and 16% and % RSD lower than 6.3%. The results of this study demonstrate that the smartphone-based device is a suitable analytical tool that offers an on-site, cost-effective, and rapid alternative for the quantification of tartrazine in soda drinks. This color analysis device can be used in other molecularly imprinted polymer systems and offers a wide range of possibilities for the detection and quantification of compounds in various industrial and environmental matrices that generate a color change in the MIP matrix.

Combination of colorimetry, inner filter effect-induced fluorometry and smartphone‑based digital image analysis: A versatile and reliable strategy for multi-mode visualization of food dyes

Herein, a multi-mode visualization platform was initiated for in-situ detection of food dyes (FDs) by combining colorimetry, fluorometry and smartphone‑based digital image analysis, in which water-dispersible quantum dots (QDs) were served as nanoprobes. Colorimetry was achieved by color comparison, while both fluorometry and fluorescence quantification were performed through inner filter effect (IFE)-induced fluorescence quenching, then color information (RGB & gray-scale values) of colorimetry and fluorometry was picked by a smartphone to reconstruct digitized alignments. Since IFE mechanism was concentration-dependent but did not rely on the interaction between fluorophore and quencher, the whole process of fluorescence response could be finished within 10 s, and both color gradients and fluorescence changes showed fine mappings to FDs concentrations in the range of 1.0 × 10^-3∼0.035 mg/mL for brilliant blue, and 1.0 × 10^-4∼0.1 mg/mL for Allura red and sunset yellow. As a proof-of-concept, the in-situ multi-mode visualization of these FDs in real beverages was experimentally proved to be highly feasible and reliable as compared with instrumental techniques like UV-vis/fluorescence spectrometry, along with HPLC. Finally, this strategy was extended to the multi-mode visualization of non-food dyes in three simulated wastewater samples with high credibility by contrast with the true additive amounts of model dyes.

Colorimetry Combined with Inner Filter Effect-Based Fluorometry: A Versatile and Robust Strategy for Multimode Visualization of Food Dyes

Herein, a strategy combining colorimetry and inner filter effect (IFE)-based fluorometry was developed for multimode visualization of food dyes (FDs) using CdTe quantum-dots-doped fluorescent indicator papers as a sample-to-answer device. Colorimetry was straightforwardly achieved by FDs extraction through electrostatic interaction and hydrophobic effect while fluorometry was implemented by IFE-induced fluorescence quenching. RGB/gray-scale values of colorimetry and fluorometry were furtherly picked by a smartphone application and applied to reconstruct color information-based digital image analysis for both direct alignments and linear regression analysis. The apparent color and fluorescence of FDs-bound indicator papers, together with their digitized color information, showed a good mapping to FDs concentrations in the range of 0-0.5 mg/mL for Sunset Yellow, 0-0.2 mg/mL for Allura Red, and 0-0.08 mg/mL for Brilliant Blue. As a proof of concept, the dosages of these FDs in real beverages and simulated dye effluents were deduced and cross-validated by different visualization modes, and finally double-checked by instrumental techniques such as spectrometric methods, high-performance liquid chromatography (HPLC), and mass spectroscopy (MS). The above findings concluded that (i) IFE mechanism is generally applicable to build fluorometric systems and (ii) cross validation of different visualization modes can markedly improve detection accuracy, which may provide references for design and fabrication of novel "lab-on-paper" devices for visualization applications with high reliability.