Prediction of Enzymatic Activity of Aspergillus Species Using Visible-Near Infrared Machine Vision System

Document Type : Original Research

Authors

1 Mechanical Engineering of Biosystems Department, Ilam University, Ilam, Iran

2 Plant Protection Department, Ilam University, Ilam, Iran

10.22103/bbr.2026.26809.1146

Abstract

Various species of the filamentous fungus Aspergillus are among the most common fungi found in air, soil, plants, and indoor environments. Many species of this genus are of significant importance in economic, biotechnological, and medical contexts, particularly in the production of enzymes, organic acids, antibiotics, and other bioactive metabolites. However, some species are opportunistic pathogens capable of causing diseases in both plants and humans. Hyperspectral imaging is a useful tool for identifying fungal traits, and when combined with machine learning, it enables more accurate and automated detection. In this study, the effect of time on the fungal growth and production of chitinase enzyme in Aspergillus endophytic isolates was investigated and a comparison was made between the isolates. The results showed that with time, the enzymatic activity of the fungal isolates increased. Additionally, significant differences were observed between the fungal isolates. The fungal growth increased with increasing enzyme activity duration too.

Keywords

References

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Biomechanism and Bioenergy Research
Volume 5, Issue 1
March 2026
Pages 70-81

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History

  • Receive Date: 10 February 2026
  • Accept Date: 06 March 2026

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