# Modelling the Combined Effects of Oxalic Acid, Water Activity, and pH on the Growth and Mycotoxin Production of Aspergillus spp. in a Dried Fig System

## TL;DR

This study models how water activity, pH, and oxalic acid affect the growth and mycotoxin production of Aspergillus species in dried figs.

## Contribution

The study introduces a predictive model combining water activity, pH, and oxalic acid effects on fungal growth and mycotoxin production in a dried fig system.

## Key findings

- Water activity (aw) was the most influential factor affecting fungal growth and mycotoxin production.
- High water activity (0.99) was necessary for significant mycotoxin accumulation.
- Oxalic acid interactions with aw and pH delayed fungal growth but did not strongly inhibit mycotoxins.

## Abstract

This study aimed to model the effects of aw, pH, and OA, a compound commonly used as a plant elicitor, on the growth and mycotoxin production of Aspergillus welwitschiae and Aspergillus flavus on a fig-based model substrate. Using RSM with a BBD, the combined impact of aw (0.92–0.99), pH (5.6–6.3), and OA (1–2 mM) on growth and mycotoxin production was evaluated under fixed temperature cycle simulating field conditions. HPLC-FLD quantified OTA and AFs. The results revealed that aw was the most influential factor governing fungal behaviour. The driest aw (0.92) significantly delayed growth and completely inhibited the production of OTA and AFB1. Conversely, high aw (0.99) was a prerequisite for significant mycotoxin accumulation. While OA at the tested elicitor concentrations did not prove to be a potent independent inhibitor of mycotoxins, its interactions with aw and pH did significantly delay fungal growth. The high R2 values (>96%) for growth models indicated a strong goodness-of-fit for comparing the relative impact of the factors. The models for mycotoxins had more moderate R2 values, a common finding reflecting the complexity of secondary metabolism. Consequently, these models should be regarded as semi-quantitative tools for identifying high-risk trends rather than for precise prediction. Following internal validation, all developed models proved to be valuable semi-quantitative tools for identifying high-risk conditions, including those with more modest R2 values like the OTA model (R2 = 56.5%, validation R > 0.945).