<span style="font-size:11.0pt;font-family: "Times New Roman";mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Mangal;mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language: HI" lang="EN-GB">Selection of nutrients for polygalacturonase production by <i style="mso-bidi-font-style:normal">Aspergillus awamori</i> MTCC 9166 using Plackett-Burman design</span>

Abstract

Selection of the best nutrients is one of the most critical stage in media optimization for polygalacturonase production. Plackett-Burman design was used to screen various pectin substrates, nitrogen sources and mineral nutrients for polygalacturonase production by <i style="mso-bidi-font-style:normal">Aspergillus awamori</i> MTCC 9166. Fifteen different pectin sources like crude pectin, polygalacturonic acid, orange peel, citrus peel, jackfruit peel, etc. were selected for polygalacturonase production using 16 experimental design of Plackett-Burman. Similarly, eleven nitrogen sources like yeast extract, tryptone, casein hydrolysate, sodium nitrate, ammonium chloride, etc. and eleven mineral nutrients like NaCl, MgSO₄, KH₂PO₄, CaCl₂, etc. were screened for polygalacturonase production using 12 experimental design of Plackett-Burman. The enzyme production was studied for 5 d, where the maximum production was observed on 3^rd d and so this data was analyzed using Indostat software to obtain regression coefficients and t-values. Based on these values significant nutrients like seven pectin sources (orange peel, jack fruit rind, apple peel, pine apple peel, mango peel, banana peel & tomato pulp), four nitrogen sources (urea, yeast extract, casein hydrolysate & potassium nitrate) and four mineral nutrients (NaCl, KH₂PO₄, CaCl₂ & KH₂PO₄) were selected for second level screening of efficient nutrients for polygalacturonase production using 16 experimental design of Plackett-Burman. Orange peel as pectin source, casein hydrolysate as nitrogen source and NaCl showed maximum enzyme production and so were selected for further quantitative optimization.