Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis

Abstract

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

The Development and Application of a HPTLC-Derived Database for the Identification of Phenolics in Honey

“…MPB, with a slightly higher polarity, was chosen to ensure that more polar phenolics were also adequately separated and detected. In a similar way, VSA was employed as a derivatisation reagent as it had been used in numerous, previous HPTLC-based honey analyses [ 36 , 37 , 66 ], allowing for cross-referencing, whereas NP-PEG was selected as a versatile derivatisation reagent particularly suitable for the identification of phenolic compounds [ 67 ], which also allowed us to broadly differentiate between flavonoids and other phenolics based on colour development [ 39 , 67 ].…”

A Comprehensive HPTLC-Based Analysis of the Impacts of Temperature on the Chemical Properties and Antioxidant Activity of Honey

“…Islam et al (2020) developed a fully validated analysis method for sugars in honey using HPTLC, which can detect and quantify its major sugars (e.g., fructose, glucose, sucrose and maltose) with high levels of precision and accuracy, as well as low limits of detection (LOD) and quantification (LOQ) [ 43 ]. The method can also be employed for the detection of post-harvest adulterations of honey with sugar syrups [ 44 , 45 ]. Locher et al (2017 and 2018) developed a HPTLC-based fingerprinting method for organic honey extracts [ 46 , 47 ], which can be used for the authentication of a honey’s floral origin as well as tracking changes in its organic extract profile over time post-exposure to different elevated temperatures.…”