Due to the technogenic impact on the biosphere and its components, a significant amount of heavy metals and radionuclides ends up in the environment. One of the main directions for improving the ecological components of environmental safety is the biotransformation of bottom sediments of reservoirs containing heavy metals, with the help of vermiculture, into biologically safe organic fertilizer. Assessment of the concentration of heavy metals in bottom sediments is an urgent task, the solution of which will allow preserving the natural environment, improving the condition of soils and, as a result, human health. The problem of using bottom deposits in this case is the accuracy of determining the content of various heavy metals in them, which affect the vital activity of earthworms. The gross and mobile forms of heavy metals in experimental substrates can be most accurately determined by atomic absorption spectral analysis. Atomic absorption analysis is a method of analytical chemistry based on the selective absorption of electromagnetic radiation of a certain wavelength by neutral atoms of the element being determined free of all molecular bonds. In the process of absorption, an electron moves from the main energy level to a higher one as a result of photon excitation. In this case, the intensity of the exciting light of a given frequency decreases. Accurate quantification is often hampered by significant matrix interference and non-uniform analyte distribution. To achieve the accuracy and reliability of the method required for vermicultivation, this work proposes a modification of the analysis method by applying fuzzy modeling of the experimental results. From a mathematical point of view, the process of constructing a calibration graph can be implemented using the procedure for constructing a fuzzy scale in the method for decoding the weight of proteins during electrophoresis. An algorithm is described for determining the fuzzy concentration of a metal from the atomic absorption signal data, followed by defuzzification of the obtained fuzzy concentration for analysis and practical use. Keywords: fuzzy modeling, spectral analysis, heavy metals.