Earticle

Chemical sterilization using hydrogen peroxide (H₂O₂) and chlorine dioxide (ClO₂) was assessed as an alternative to traditional steam sterilization for preparing mushroom substrates. Both agents achieved a microbial reduction of ≥6 log, while maintaining biological efficiency comparable to autoclaving, even at low concentrations. These methods offer several advantages, including low energy consumption, simple setup, and cost-effectiveness, making them suitable for small-scale farms. However, there are concerns regarding species-specific sensitivity and potential residue risks. A life cycle assessment (LCA) indicated that chemical sterilization results in a lower environmental impact compared to thermal methods. Overall, H2O2 and ClO2 are promising options for decentralized and sustainable mushroom cultivation.

Due to the rapid senescence and short shelf-life, identifying fresh Agaricus bisporus requires careful attention. The representative indicator of quality status is the hardness of mushrooms, but there is a limitation that mushroom products are inevitably damaged when analyzing hardness values. In this study, the hardness of mushrooms was measured using the hyperspectral imaging (HSI) method, which is a non-destructive analytical tool, to overcome the limitations. In performance of HSI, hyperspectral wavelength bands were grouped according to A. bisporus hardness values which measured by texture profile analysis (TPA). The grouping was performed by dividing into two classes based on the median hardness value (over or under 15 N). We evaluated whether hyperspectral reflectance can stratify mushroom hardness into three groups by using principal component analysis (PCA) and random-forest (RF). As result, PCA scatter plots showed a partial visual tendency of separation near the hardness value of 14.93 N, and RF classification demonstrated moderate accuracy with 81%. In addition, Mean ± Std Dev spectra revealed clear differences between the two hardness group, particularly in the near-infrared region (800–900 nm), and ANOVA confirmed that multiple wavelength bands exhibited statistically significant differences (p<0.05). These results indicate that HSI can effectively capture variations in mushroom hardness across multiple spectral ranges. However, the limited number of samples and grouping approach constrained the robustness of classification, suggesting that larger datasets are required to generalize the findings. Based on improved experiments and expanded datasets, it is expected that mushroom hardness can ultimately be predicted through non-destructive HSI methods.