| Sumario: | Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its
preparation involves several unit operations, including fermentation, which is
essential in gari production. Fermentation brings about specific biochemical
changes in cassava starch due to the actions of lactic acid bacteria.
Consequently, it gives rise to organic acids and a significant reduction in the
pH. Consumer preferences for gari are influenced by these changes and impact
specific functional characteristics, which are often linked to cassava genotypes.
Measurement of these functional characteristics is time-consuming and
expensive. Therefore, this study aimed to develop high-throughput and less
expensive prediction models for water absorption capacity, swelling power,
bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy
(NIRS). Gari was produced from 63 cassava genotypes using the standard
method developed in the RTB foods project. The prediction model was
developed by dividing the gari samples into two sets of 48 samples for
calibration and 15 samples as the validation set. The gari samples were
transferred into a ring cell cup and scanned on the NIRS machine within the
Vis-NIR range of 400–2,498 nm wavelength, though only the NIR range of
800–2,400 nm was used to build the model. Calibration models were
developed using partial least regression algorithms after spectra preprocessing.
Also, the gari samples were analysed in the laboratory for their
functional properties to generate reference data. Results showed an excellent
coefficient of determination in calibrations (R2
Cal) of 0.99, 0.97, 0.97, and 0.89 for
bulk density, swelling power, dispersibility, and water absorption capacity,
respectively. Also, the performances of the prediction models were tested
using an independent set of 15 gari samples. A good prediction coefficient (R2
pred) and low standard error of prediction (SEP) was obtained as follows: Bulk
density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and
solubility index (0.62), respectively. Therefore, NIRS prediction models in this
study could provide a rapid screening tool for cassava breeding programs and
food scientists to determine the food quality of cassava granular products (Gari).
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