Quantification of dry matter, pH, amylose, swelling power and solubility of Fufu flour using ASD Quality Spek portable NIRS

This scientific report concerns data analysis of four matrices of measured data on Fufu flour physico-chemical data as well as the spectral data. The data were collected on Fufu flour in NRCRI, Nigeria. The analyses concern 56 cassava genotypes harvested from Preliminary Yield Trial of NEXTGEN CASSAVA BREEDING PROGRAMME in 2021 at Umudike, Nigeria and processed into Fufu flour. For this study 56 Fufu flour samples were analysed; Three different quartz cups with the Fufu (of about 8 – 10g) were sampled. The three cups were chosen to serve as replicates for the samples during spectra collection. One spectra was captured on each of the three cups, the total number of spectra captured is equal to 168.
Fufu Four Production: The protocol used in the Fufu flour production was as described by Thierry TRAN, (2019).
Dry matter (DM) quantification was achieved in NRCRI food profiling Trait laboratory in Biotechnology programme of NRCRI, Umudike using an oven method of analysis. Near infrared spectra were scanned in NIR laboratory of NRCRI in Biotechnology Programme. The protocol measurement follows the SOP protocol described for wet Fufu mash: https://doi.org/10.18167/agritrop/00677
Amylose quantification was achieved in NRCRI food profiling Trait laboratory in Biotechnology programme of NRCRI, Umudike. Near infrared spectra were scanned in NIR laboratory of NRCRI in Biotechnology Programme. The protocol measurement follows the SOP protocol described for amylose content using DSC https://doi.org/10.18167/DVN1/MBB1CT
The pH was determined according to Mathew et al. (1995), swelling power and solubility determination were done according to method 10.1016/j.proenv.2015.01.025 in NRCRI food profiling Trait laboratory in Biotechnology programme of NRCRI, Umudike.
The dry matter, pH, amylose, swelling power and solubility contents, expressed as % of total weight, vary between 8.60% and 25.40% with an average value of 17.60%, 3.6% and 5.85% with average value of 3.83%, 11.90% and 25.31% with an average value of 18.57%, 3.88% and 8.12% with an average value of 5.96% and between 1.00% and 4.00% with an average value of 1.66% respectively on dry weight basis of the 56 samples with 2 data sets (40 for calibration and 16 for validation).
The traits DM, pH, amylose, swelling power and solubility contents were calibrated using Modified Partial Least Squares Regression, for the spectral range: 400 nm - 2500nm, that is to say Visible and NIR regions. Calibrations were done on non-pretreated spectra and pretreated spectra using different pre-treatments. The best model was obtained with SNVD 2,5,5,1 pre-treatment applied to the spectra, the R²cal were 0.79 (DM), 0.55 (pH), 0.96 (amylose), 0.56 (swelling power) and 0.75 (solubility) with an SECV equal to 0.4%, 0.47%, 0.2%, 0.2% and 0.7% respectively. The performance of the calibration model was evaluated using the coefficient of determination of calibration (R2c), the coefficient of determination of cross-validation (R2cv) and SECV. Generally, the optimum models
were based on the higher R2c values. The R2Cal, R2cv, values indicate how well the equations performed under the same conditions (Junjie et al, 2018).