Influence of Adulteration Agents on Physico-Chemical and Spectral Profile of Different Honey Types

Detection of honey adulteration using machine learning

Honey adulteration is a growing concern due to its health benefits and high nutritional content. Traditional methods like Melissopalynology are ineffective in detecting adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on hyper-spectrum images and improves the accuracy of existing models using hyperparameter tuning. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples. The study found that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy. The results showed that between 5% and 10% of adulterated honey samples are misclassified, with C1 Clover honey being the most frequently misclassified. This study aims to develop an efficient and accurate honey counterfeit detection technology using machine learning technologies such as Artificial Neural Networks (ANN), Support-vector machines (SVM), K Nearest Neighbors, Random Forests, and Decision trees. The proposed model relies on hyper-spectrum images and overcomes generalization to unknown honey types of problems. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples from seven different brands with 12 different botanical origin labels. Feature reduction techniques, such as feature ranking-based feature selection, and autoencoder techniques are employed to classify the botanical origins of honey. The model parameters are enhanced or tuned by the training process, and hyperparameters are adjusted by running the whole training data. The researchers used Python, and well-known algorithms like ANN, SVM, KNN, random forests, and decision trees. The results show that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy.

Differentiation of High-Fructose Corn Syrup Adulterated Kelulut Honey Using Physicochemical, Rheological, and Antibacterial Parameters

Kelulut (stingless bee) honey (KH) possesses a wide range of benefits for human consumption and can exhibit medical effects. Due to its high value, this premium honey is often adulterated with different types of cheaper sugars, causing low nutrients and potential food safety threats in the final product. This study aims to determine the physicochemical, rheological, and antibacterial properties of sugar-based adulterated KH from the stingless bee species Heterotrigona itama. Adulterated samples were prepared using pure honey mixed with different concentrations of high fructose corn syrup (HFCS), i.e., 10%, 20%, 30%, 40%, and 50%. Water activity, colour, total soluble solids, pH, turbidity, viscosity, and antimicrobial activity of KH were determined. In addition, the primary sugar composition (fructose, glucose, and trehalulose) was determined by high-performance liquid chromatography with evaporative light-scattering detection (HPLC-ELSD). This study shows that the increasing percentage of HFCS addition in the KH samples significantly increases (p < 0.05) the total soluble solids, colour, pH, turbidity, viscosity, glucose, and fructose content; meanwhile, the water activity and trehalulose were reduced significantly (p < 0.05). Antimicrobial activity against S. aureus was reduced significantly (p = 0.006) by an increased percentage of HFCS compared to Control. Antimicrobial activity against P. aeruginosa was also found to be reduced significantly but showed non-significant effect from an increased percentage of HFCS in honey (p = 0.413). The bacterium S. aureus was more vulnerable to treatment with honey from both Control and adulterated groups compared to P. aeruginosa. In conclusion, HFCS-adulterated KH and authentic KH can be differentiated using all the parameters investigated. These data are vital for the governing bodies to ensure that KH sold in the markets is free from HFCS adulteration.

Deep learning assisted distinguishing of honey seasonal changes using quadruple voltammetric electrodes

The work presents innovative quadruple disk iridium, platinum, and iridium-platinum voltammetric electrodes with a special design, dedicated to the testing of samples with a complex organic composition. Noble metal wires are tightened in one silver rod, and therefore each of them acts as a single sensor. It was demonstrated that the signals of the iridium-platinum sensor combine the electrode responses constructed from one metal, which increases the possibilities and range of applications of this sensor, and it can be used as an electronic tongue. These single and combined noble metal electrodes were successfully verified to profile the seasonal variability of honey collected from an apiary in Małopolska (voivodeship in Poland). Data obtained by the differential pulse voltammetry, according to the principles of green chemistry, without using any reagents, were interpreted by principal component analysis, preceded by the optimized variable selection procedure. The best results in distinguishing 12 honeys were obtained using a multimetallic electrode. The classification model calculated using deep convolutional neural networks indicated the proper belonging of honeys to the groups with 100% accuracy for the training and validation set. The proposed solution proved that noble metals quadruple disk electrodes are a promising tool supporting voltammetric profiling of samples and this strategy, considering deep learning, can be developed to a large extent.

Monitoring the quality of honey: South African case study

The popularity of honey as a high-valued commodity is growing and consequently, honey adulteration is on the rise affecting the honey quality. The quality of the honey on the South African market was evaluated using the Agricultural Product Standards Act, 1990 as assessment tool. Various physico-chemical characteristics were tested which indicated compliance of >80% for all honey samples. A canonical variate analysis using 95% confidence regions indicates significant differences between the quality of local and imported honey with total acid, sucrose and ash as the parameters mostly distinguishing between the groups. Honey produced from agricultural crops differed significantly from all other forage types. The parameters that mostly distinguished between forage types were Lund, hydroxy-methyl-furfural and ash content. Even though honey sold on the South African market is generally in accordance with national and international standards, an overall declining trend in quality was observed and it should be continuously monitored.