Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Abstract

Summary Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant’s resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( λ= 280-400 ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

https://researchopenworld.com/effect-of-propolis-nanoparticles-on-early-stage-wound-healing-in-a-diabetic-noncontractile-wound-model/#

“…The detection of a typical chemical profile associated with propolis from a specific production region or season may be used to obtain a specific pharmacological activity. The chemical fingerprint of the propolis in the present work is consistent with the same specific harvest season and region of production as other studies determined through NMR-based metabolomics, chemometrics, machine learning algorithms [13], and UV-Vis profiles [19]. Furthermore, chromatographic profiles of the propolis herein investigated and the source of Artepillin C, the plant species Baccharis dracunculifolia, were similar [20].…”

Comparison of Physicochemical Properties of Bee Pollen with Other Bee Products

“…The UV-Vis spectra of bee products were performed using different dilution levels: bee pollen samples were diluted 11 times, beebread 18 times, royal jelly 10 times, propolis 19 times and dilution of three times was used for honey solutions. Absorbance values were recorded with a UV-visible spectrophotometer Shimadzu UV-Vis 1280 (Kyoto, Japan) using 1.0 nm scan pitch, 200-1100 nm scan range in 60 s. For all absorbance measurements Quartz cells (1 cm) were used [25].…”

Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through near‐infrared spectroscopy and chemometrics

“…60 Machine learning techniques have become popular in recent years to support decision making, predicting events and analyzing data. 61,62 In using a spectral library of 43 000 soil samples via NIR spectroscopy combined with machine learning analysis and the use of the rf algorithm, Santana et al 17 measured the organic matter content of other soil samples and had accuracy rates of 90.6% for predictive models. Also using the rf algorithm and NIR, Nawar et al 21 obtained accuracy rates of 60-75% for the prediction of the organic carbon content of 347 soil samples from three locations in the UK.…”