Ayetigbo O, Latif S, Abass A, Müller J. Dataset on influence of drying variables on properties of cassava foam produced from white- and yellow-fleshed cassava varieties.
Data Brief 2021;
37:107192. [PMID:
34150963 PMCID:
PMC8193113 DOI:
10.1016/j.dib.2021.107192]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/26/2021] [Accepted: 05/31/2021] [Indexed: 11/26/2022] Open
Abstract
Freshly harvested cassava has a tendency to deteriorate rapidly in its physiological properties after harvest. Therefore, cassava is often processed using a number of unit operations in order to derive a stable, storable product of acceptable eating quality. Among the unit operations employed, drying is considered as one of the oldest and most important process in arresting deterioration of cassava. In recent times, more researchers are considering foam mat drying as a drying technique for tuber or root crops, although the technique is used, ideally, for fruit juices and dairy. Cassava foam production from white and yellow cassava varieties has been optimized in our previous work [1]. Our data were procured from experimentally measuring mass of cassava foams of white and yellow cassava varieties dried at different temperatures (50, 65, 80 °C) and foam thicknesses (6, 8, 10 mm) over regular drying intervals until no considerable mass change was observed. The mass measurements are the primary datasets used in determination of secondary datasets presented here as moisture removal ratio (MR), effective moisture diffusivity (Deff), and drying rate (DR). The MR data were fitted to four thin-layer drying models (Henderson-Pabis, Page, Newton, Two-term), and Page model described the experimental drying data best. The Page model coefficients were analyzed by multiple linear regression (MLR) analysis to show how they are influenced by the drying variables. Drying rate was also fitted by Rational model to fit the DR data and to reflect the two falling rates found. Statistical accuracy and significance were calculated as coefficient of determination (R2), root mean square error (RMSE) and Chi square (χ2) and an analysis of variance (ANOVA). Data obtained here are useful as primary data in process and dryer designs and processing of cassava in the cassava industry.
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