Encapsulation of bioactive compounds extracted from Cucurbita moschata pumpkin waste: the multi-objective optimisation study

Abstract

Aim

Artificial neural network (ANN) development to find optimal carriers (pea protein-P, maltodextrin-M, and inulin-I) mixture for encapsulation of pumpkin waste bioactive (β-carotene and phenolics).

Methods

Freeze-drying encapsulation and encapsulates characterisation in terms of bioactive contents and encapsulation efficiencies, water activity, hygroscopicity, densities, flowability, cohesiveness, particle size (laser diffraction), solubility, colour (CIELab), morphological (SEM), stability and release properties.

Results

Optimal encapsulates, OE-T (with highest total bioactive contents; P, M, and I of 53.9, 46.1, and 0%w/w) and OE-EE (with highest bioactive encapsulation efficiencies; P, M, and I of 45.5, 32.0, and 22.5%w/w) had particle diameters of 94.561 ± 1.341 µm and 90.206 ± 0.571 µm, the span of 1.777 ± 0.094 and 1.588 ± 0.089, highest release at pH 7.4 of phenolics of 71.03%w/w after 72 h and 66.22%w/w after 48 h, and β-carotene of 43.67%w/w after 8 h and 48.62%w/w after 6 h, respectively.

Conclusion

ANN model for prediction of encapsulates’ preparation, showed good anticipation properties (with gained determination coefficients of 1.000).

Keywords:

• Pumpkin waste
• bioactive compounds
• encapsulation
• optimisation
• artificial neural network

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This work was financially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Project No. 451–03-68/2022–14/200134).