Analysis of chemical compounds in beverages - Guidance for establishing a compositional analysis

Determination of the stability of sodium cyclamate during deep-frying using HPLC

The oil used to fry food is often used multiple times to reduce costs. However, when foods containing sweeteners are processed in this way, the sweeteners may produce substances harmful to the body as a result of repeated frying at high temperatures. This article investigated the stability of sodium cyclamate during deep-frying by HPLC using a pre-column derivatization method. The results showed that cyclohexylamine was a decomposition product of a standard sample of sodium cyclamate when deep-fried at 200°C for 25 min. A pre-column derivatization/HPLC method was established to determine cyclohexylamine, a decomposition product of sodium cyclamate, under these conditions. Dansyl chloride was used as the derivatization reagent, the derivatization temperature was 60°C, the derivatization time was 20 min, the pH of sodium bicarbonate buffer solution was 11, and the concentration of dansyl chloride was 2.0 mg/mL. Detection was carried out by using an Agilent 1260 high-performance liquid chromatograph coupled with an ultraviolet detector. The ultraviolet detection wavelength was 254 nm, and the mobile phase was acetonitrile-1.0 g/L potassium dihydrogen phosphate solution at a flow rate of 1.0 mL/min. Gradient elution was adopted, the peak of the cyclohexylamine derivative appeared at a retention time of 17.75 min, and the peak area response value was the largest. The methodological validation analysis showed that the detection limit of cyclohexylamine was 0.5 mg/kg, the quantification limit was 2.0 mg/kg, and the spiked recoveries were in the range of 99.37-110.16%. The relative standard deviations (RSDs) were in the range of 0.17-1.26%. Four samples were tested and analyzed by the established method, and cyclohexylamine was not detected.

Advancing forensic research: An examination of compositional data analysis with an application on petrol fraud detection

In recent years, numerous studies have examined the chemical compounds of petrol and petrol data for forensic research. Standard quantitative methods often assume that the variables or compounds do not have compositional constraints or are not part of a constrained whole, operating within an Euclidean vector space. However, chemical compounds are typically part of a whole, and the appropriate vector space for their analysis is the simplex. Biased and arbitrary results result when statistical analysis are applied on such data without proper pre-processing of such data. Compositional analysis of data has not yet been considered in forensic science. Therefore, we compare classical statistical analysis as applied in forensic research and the new proposed paradigm of compositional data analysis (CoDa). It is demonstrated how such analysis improves the analysis in petrol and forensic science. Our study shows how principal component analysis (PCA) and classification results are affected by the preprocessing steps performed on the raw data. Our results indicate that results from a log ratio analysis provides a better separation between subgroups of the data and leads to an easier interpretation of the results. In addition, with a compositional analysis a higher classification accuracy is obtained. Even a non-linear classification method - in our case a random forest - was shown to perform poorly when applied without using compositional methods. Moreover, normalization of samples due to laboratory/unit-of-measurement effects is no longer necessary, since the composition of an observation is in compositional thinking equivalent to a multiple of it, because the used (log) ratios on raw and log ratio transformed data are equal. Petrol data from different petrol stations in Brazil are used for the demonstration. This data is highly susceptible to counterfeit petrol. Forensic analysis of its chemical elements requires non-biased statistical analysis designed for compositional data to detect fraud. Based on these results, we recommend the use of compositional data methods for gasoline and petrol chemical element analysis and gasoline product characterization, authentication and fraud detection in forensic sciences.

Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.