Carotenoid profile determination of bee pollen by advanced digital image analysis

https://doi.org/10.1016/j.compag.2020.105601Get rights and content

Highlights

  • Digital image analysis is an effective technique to predict composition in bee pollen.

  • Mathematical models to predict carotenoid content from Digital Image Analysis were established.

  • New carotenoids were identified in Colombian bee pollen.

  • It was possible to predict carotenoid content from Digital Image Analysis in bee pollen samples.

Abstract

Bee pollen is a natural matrix widely studied in its nutritional and bioactive compounds, including carotenoids. That composition is usually identified by Rapid Resolution Liquid Chromatography (RRLC) coupled to UV–Vis spectrophotometry, an expensive method that requires complex sample preparation and long analysis time. In this work, a correlation between colorimetric coordinates and carotenoid composition was evaluated. Through Digital Image Analysis (DIA) by DigiEye, the color characteristics were determined, and carotenoids profile was done by RRLC. The correlations were made by multiple linear regression (MLR). From 12 carotenoids found in the samples, six had a coefficient R2 > 0.75 between reference and predict value. Heterogeneous mixtures of bee pollen samples were analyzed, and the suitability of the mathematical models could be corroborated because the relative error for most of the compounds was less than 20%. It has been demonstrated that union of Tristimulus Colorimetry and Image Analysis represent an effective tool to estimate the chemical composition in food industry.

Introduction

Bee pollen is a natural product made by worker bees when they collect nectar and pollen. Floral pollen is mixed with salivary secretions and nectar. Afterward, it is recollected by beekeepers through traps in the hive and since that moment it can be named bee pollen (Fuenmayor et al., 2014, Kieliszek et al., 2018). It is composed of two protective layers, intine and exine, which protect the interior of the oxidation grain, radiation damage and chemical degradation (Atkin et al., 2011). Exine is composed of various organic and inorganic substances, among which sporopollenin is a very complex polymer that gives chemical resistance to pollen (Kovacik et al., 2009). The structure of sporopolenin has been extensively studied, finding that it presents a variety of substances, such as some types of carotenoids, tocopherols, provitamin A and vitamin D (Domínguez-Valhondo et al., 2011).

Among carotenoids, β-carotene, cryptoxanthin, zeaxanthin and lutein were identified in bee pollen (Domínguez-Valhondo et al., 2011, Schulte et al., 2009). In a previous study of Colombian bee pollen, traces of lutein, zeaxanthin, β-carotene and phytoene were identified (Gardana et al., 2018). Carotenoids are important because they are related to different positive effects in cancer, cardiovascular diseases, diabetes, cataracts and others (Kong et al., 2010, Saini et al., 2015). These compounds are also responsible for color (Saini et al., 2015, Sant’Anna et al., 2013).

Some studies characterized bee pollen color and carotenoid content, and looked for a relation between both parameters (Domínguez-Valhondo et al., 2011, Machado De-Melo et al., 2016, Schulte et al., 2009, Xu et al., 2013). However, the heterogeneity of the pollen samples makes it difficult to measure color by conventional colorimetric techniques. In these cases, Digital Image Analysis (DIA) allows to evaluate the color of each point within a given area, which makes this technique ideal for the analysis of bee pollen (Salazar-González et al., 2018). This technology also allows to develop Chemical Imaging, where it is possible to indicate the presence or concentration in pseudo-color scales of a chemical compound in an image (Rodríguez-Pulido et al., 2017).

Several studies have been made in the last years correlating data obtained by DIA with bioactive compounds in several foods: grapes or tomatoes (Rodríguez-Pulido et al., 2012, Stinco et al., 2013). In a study on tomato products (fresh and processed), the authors could correlate Digital Image Analysis with lycopene isomers content (LYC). When tridimensional character of color is considered and a multivariate analysis is needed, the correlation coefficient increased up to 0.77 (Stinco et al., 2013).

Previous studies showed that it is possible to predict chemical composition by Digital Image Analysis in food products. Then, the aim of this work was to evaluate the possibility to predict carotenoid content from image analysis parameters in Colombian bee pollen. These correlations would help beekeeping chain to value its products with one simple image.

Section snippets

Samples

Bee pollen was collected monthly through 2016 by random sampling. The experimental unit was an apiary in the geographic region of the Colombian high Andean forest (2.800 and 3.200 m above sea level) in Boyacá. Samples used in this work were classified into two sets:

  • Twelve groups: heterogeneous samples were manually classified to obtain pellets with uniform color and from this classification 12 groups were selected (A-M, letter “I” was omitted to avoid mistakes in the analysis). The homogeneity

Results and discussion

Groups A-M were selected to create models that correlate colorimetric characteristics and chemical composition, thus it was necessary that they were homogeneous. This was probed by pollen, color and carotenoid profile analysis, but only the relation between the chemistry of the groups and their optical properties were used to generate the models.

Then, heterogeneous samples (the way that there find in nature) were used to probe the feasibility of the models obtained previously. To accomplish

Conclusions

A methodology was developed for the estimation of individual carotenoids from digital images in bee pollen samples. For α-tocopherol, both lutein isomers, anteraxanthin isomer 1, zeaxanthin and β-cryptoxanthin, high correlations were achieved between the estimated values and those measured by reference methods. This methodology is a great advance for the rapid identification of carotenoids in bee pollen samples; however, other optical techniques, such as infrared spectra and harvest regions,

CRediT authorship contribution statement

Claudia Y. Salazar-González: Investigation, Writing - original draft. Francisco J. Rodríguez-Pulido: Software, Investigation. Carla M. Stinco: Investigation, Writing - original draft. Anass Terrab: Writing - review & editing. Consuelo Díaz-Moreno: Conceptualization, Supervision. Carlos Fuenmayor: Investigation. Francisco J. Heredia: Conceptualization, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank the School of Engineering of Universidad Nacional de Colombia for its support to international mobility. To the Asociación Universitaria Iberoamericana de Posgrado -AUIP- and its program “Becas de Movilidad entre Universidades Andaluzas e Iberoamericanas Convocatoria 2017”. Finally, we are thankful to the staff of Biology Service (SGI, Universidad de Sevilla) for the technical assistance.

This work received funding from El Patrimonio Autónomo Fondo Nacional de Financiamiento para la

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