Optimization of fungal chitosan production from Cunninghamella echinulata using statistical designs.

Fungal chitosan (FCH) is superior to crustacean chitosan (CH) sources and is of immense interest to the scientific community while having a high demand at the global market. Industrial scale fermentation technologies of FCH production are associated with considerable challenges that frequently restrict their economic production and feasibility. The production of high quality FCH using an underexplored fungal strain Cunninghamella echinulata NCIM 691 that is hoped to mitigate potential future large-scale production was investigated. The one-factor-at-a-time (OFAT) method was implemented to examine the effect of the medium components (i.e. carbon and nitrogen) on the FCH yield. Among these variables, the optimal condition for increased FCH yield was carbon (glucose) and nitrogen (yeast extract) source. A total of 11 factors affected FCH yield among which, the best factors were screened by Plackett-Burman design (PBD). The optimization process was carried out using the response surface methodology (RSM) via Box-Behnken design (BBD). The three-level Box- Behnken factorial design facilitated optimum values for 3 parameters-glucose (2% w/v), yeast extract (1.5% w/v) and magnesium sulphate (0.1% w/v) at 30˚C and pH of 4.5. The optimization resulted in a 2.2-fold higher FCH yield. The produced FCH was confirmed using XRD, 1H NMR, TGA and DSC techniques. The degree of deacetylation (DDA) of the extracted FCH was 88.3%. This optimization process provided a significant improvement of FCH yields and product quality for future potential scale-up processes. This research represents the first report on achieving high FCH yield using a reasonably unfamiliar fungus C. echinulata NCIM 691 through optimised submerged fermentation conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03919-6.

Some developments on seasonal INAR processes with application to influenza data.

Influenza epidemic data are seasonal in nature. Zero-inflation, zero-deflation, overdispersion, and underdispersion are frequently seen in such number of cases of disease (count) data. To explain these counts' features, this paper introduces a flexible model for nonnegative integer-valued time series with a seasonal autoregressive structure. Some probabilistic properties of the model are discussed for general seasonal INAR(p) model and three estimation methods are used to estimate the model parameters for its special case seasonal INAR(1) model. The performance of the estimation procedures has been studied using simulation. The proposed model is applied to analyze weekly influenza data from the Breisgau- Hochschwarzwald county of Baden-Württemberg state, Germany. The empirical findings show that the suggested model performs better than existing models.