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Updated: May 16, 2025

Measurement of Chitinase Activity in Biological Samples

Measurement of Chitinase Activity in Biological Samples

Published on: August 22, 2019

Chitin estimation in agricultural soils.

Alex Gobbi, Roberto Lava, Giorgia Vianello

EFSA Journal. European Food Safety Authority

|April 4, 2025

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This study estimated soil chitin levels, a precursor to chitosan, to assess its natural environmental availability. Fungi are the primary source, with soil chitin ranging from 27-280 kg/ha (0-5 cm) and 99-901 kg/ha (0-20 cm).

Area of Science:

Environmental Science
Soil Biology
Biochemistry

Background:

Chitosan and chitosan hydrochloride are considered for plant protection uses as basic substances under Regulation (EC) No 1107/2009.
Estimating the natural environmental availability of chitosan requires understanding the baseline levels of its precursor, chitin, in agricultural soils.

Purpose of the Study:

To estimate the amount of chitin present in agricultural soil.
To establish a baseline for the natural availability of chitin in soil.
To inform the estimation of chitosan potentially available in the environment.

Main Methods:

Literature review to gather data on chitin content in agricultural soils.
Estimation of chitin concentration in soil layers (0-5 cm and 0-20 cm).

Keywords:

agricultural soil basic substance chitin chitosan natural occurrence plant protection

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Development of a polynomial function to predict chitin content based on available data.

Main Results:

Estimated soil chitin content ranges from 27 to 280 kg/ha in the top 0-5 cm layer and 99 to 901 kg/ha in the 0-20 cm layer.
Fungi were identified as the primary producers of chitin in soil, followed by insects and nematodes.
A polynomial function was developed to estimate chitin amounts and identify key predictors in similar biomes.

Conclusions:

The study provides an initial estimate of soil chitin levels, crucial for assessing chitosan's environmental presence.
Further validation through field measurements across diverse soil types and conditions is necessary for a generalized model.
Identified data gaps and uncertainties require further investigation for robust environmental risk assessments.