The role of statistical methods in optimizing and enhancing fungal chitosan commercial production

This review portrays the role of fermentation technology in the production of fungal chitosan (FCH) under optimized conditions using statistical  methods. It is noteworthy to mention that FCH is superior than crustacean chitosan (CCH) due to its low molecular weight (LMW) (≈ 20–30 kDa), polymer homogeneity, high degree of deacetylation (DDA) (74–92%), along with thermal stability, solubility at wide physiological pH and greener extraction process. Employment of suitable submerged fermentation conditions improves the quality (high DDA and LMW) of FCH for varied applications. Literature survey depicted the crucial role of recent advancements of statistical tools and software in FCH fermentation technology. A close look at the literature over the past three decades showed ≈ 64% of FCH production from Absidia coerulea, Rhizopus oryzae, R. japonicus, Aspergillus niger, A. terreus, A. favus, Cunninghamella elegans (≈ 16% each) followed by Mucor rouxii (≈ 11%). Other fungi namely, Benjaminiella poitrasii, Penicillium chrysogenum and Trametes versicolor, Gongronella butleri and Ganoderma lucidum (≈ 5% each) have been reported. The Design of Experiments (DOE), like response surface methodology (RSM) including Plackett–Burman Design (PBD), Central composite design (CCD), Box Behnken design (BBD) and Taguchi have improved biomass and FCH yield meaningfully. Among diferent approaches, One-factor-at-a-time (OFAT) approach was the foremost choice (≈ 29%) followed by CCD (≈ 12%) and OFAT combined with CCD (≈11%) were employed by researchers to optimize FCH production from potent strains. Around 6% of the reports suggest that BBD, Taguchi, FC-BBD, CCD, 2>2 factorials have been employed at an individual level to achieve a high yield of FCH. Those methods can be employed either individually or in combination. This article comprehensively presents the basic information, performances of the statistical   methods/tools of DOE and software employed for successful scaling-up of FCH while highlighting their merits, limitations, and challenges.