chemical composition-ssf

Published: 26 May 2022| Version 1 | DOI: 10.17632/y6rk2rj86x.1
adedoyin amos,


The raw data contained fermented and unfermented chemical analyses of selected agro-industial by-products. The products were fermented in a solid state fermentation using Aspergillus species. Improved nutritional profile were observed after fermentation.


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Chemical analysis of samples The chemical compositions of the samples were carried out before and after microbial fermentation. All measurements were done in quadruplicates. Proximate composition The samples were analyzed for proximate composition (crude protein, crude fibre, ether extract and ash) according to the Association of Analytical Chemists, (2002). Nitrogen free extract (NFE) was calculated by difference. The fibre compositions: Neutral detergent fibre (NDF), Acid Detergent Fibre (ADF) and Acid Detergent Lignin (ADL) were determined according to Van Soest et al. (1991). Cellulose and hemicellulose were calculated as differences between ADF and ADL, NDF and ADF respectively. The gross energy contents of the samples were determined according to standard procedures using the Adiatic Bomb Calorimeter (Model 1261; Parr Instrument Company, Moline, IL, USA). The digestible energy values of the samples were calculated using de Blas et al. (1992); (DE= GE x (0.867–0.0012ADF). Mineral Composition. The mineral contents of the samples were determined according to the standard protocols described by Sodamade et al. (2013). Anti-nutritional factors Anti-nutritional factors of the samples were determined as follows: Tannin content was determined according to the protocols of (Makkar et al., 1993), phytate and oxalate contents were determined according to the methods of (Haritha and Jayadev, 2017), total phenol content was determined using the protocols of (Kaur and Kapoor, 2002), trypsin inhibitor activity was carried out as described in International Organization for Standardization (ISO) 14902:2001. Medium pH The pH was maintained at 6.8 Statistical Analysis Data generated from this study were subjected to a one-way analysis of variance (ANOVA) at a 5% level of significance using SPSS Ver.20 for windows. Means were separated using Tukey's multiple comparison tests. Statistical model Yij = µ + Ti + Ɛij Where; Yij = the observed value of the dependent variable μ = population mean Ti = ith effect of the treatment Ɛijk= Residual error


Federal University of Agriculture Abeokuta


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