Flour Pasting Properties Define Consumer Selection Decisions for Boiled and Flour-Based Cassava Food Products in Uganda

Nuwamanya, Ephraim; Kanaabi, Michael; Wembabazi, Enoch; Muhumuza, Nicholas; Ozimati, Alfred; Esuma, Williams; Iragaba, Paula; Angudubo, Steven; Nanyonjo, AnnRitah; Tufan, Hale; Kawuki, R.

doi:10.20944/preprints202103.0567.v1

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…In Uganda, 25 cassava genotypes were grown at NaCRRI (Namulongue) and harvested at 10 months after planting (MAP). Ten roots per genotype were collected, peeled, washed and cut into cylinders (6 cm long, 5–5.5 cm diameter), then boiled for 55 min in banana leaf wraps 20 . The boiled samples were characterized by sensory QDA and instrumental hardness using a penetrometer.…”

Section: Methodsmentioning

confidence: 99%

Definition of sensory and instrumental thresholds of acceptability for selection of cassava genotypes with improved boiling properties

Iragaba,

Adinsi,

Delgado

et al. 2024

J Sci Food Agric

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BackgroundConsumers of boiled cassava in Africa, Latin America and Asia use specific preference criteria to evaluate its cooking quality, in terms of texture, color and taste. To improve adoption rates of improved cassava varieties intended for consumption after boiling, these preference criteria need to be determined, quantified and integrated as post‐harvest quality traits in the target product profile of boiled cassava, so that breeding programs may screen candidate varieties based on both agronomic traits and consumer preference traits.ResultsSurveys of various end‐user groups identified seven priority quality attributes of boiled cassava covering root preparation, visual aspect, taste and texture. Three populations of contrasted cassava genotypes, from good‐cooking to bad‐cooking, in three countries (Uganda, Benin, Colombia) were then characterized according to these quality attributes by sensory quantitative descriptive analysis (QDA) and by standard instrumental methods. Consumers preferences of the texture attributes mealiness and hardness were also determined. By analysis of correlations, the consumers preferences scores were translated to thresholds of acceptability in terms of QDA scores, then in terms of instrumental measurements (water absorption during boiling and texture analysis). The thresholds of acceptability were used to identify among the Colombian and Benin populations promising genotypes for boiled cassava quality.ConclusionThis work demonstrates the steps of determining priority quality attributes for boiled cassava and establishing their corresponding quantitative thresholds of acceptability. The information can then be included in boiled cassava target product profiles used by cassava breeders, for better selection and adoption rates of new varieties.This article is protected by copyright. All rights reserved.

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Section: Methodsmentioning

confidence: 99%

Definition of sensory and instrumental thresholds of acceptability for selection of cassava genotypes with improved boiling properties

Iragaba,

Adinsi,

Delgado

et al. 2024

J Sci Food Agric

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“…In this study, the final viscosity ranged from 28 to 38 mPa•s, which was a small difference between RPC and YPI. The peak time for protein materials used ranged from 4.2 to 7.5 min, which describes the time taken to achieve the peak viscosity while heating at 130 • C [55].…”

Section: Pasting Propertiesmentioning

confidence: 99%

Development and Characterization of Extrudates Based on Rapeseed and Pea Protein Blends Using High-Moisture Extrusion Cooking

Zahari

Ferawati

Purhagen

et al. 2021

Foods

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Rapeseed protein is not currently utilized for food applications, although it has excellent physicochemical, functional, and nutritional properties similar to soy protein. Thus, the goal of this study was to create new plant-based extrudates for application as high-moisture meat analogs from a 50:50 blend of rapeseed protein concentrate (RPC) and yellow pea isolate (YPI) using high-moisture-extrusion (HME) cooking with a twin-screw extruder to gain a better understanding of the properties of the protein powders and resulting extrudates. The effects of extrusion processing parameters such as moisture content (60%, 63%, 65%, 70%), screw speed (500, 700, and 900 rpm), and a barrel temperature profile of 40–80–130–150 °C on the extrudates’ characteristics were studied. When compared to the effect of varying screw speeds, targeted moisture content had a larger impact on textural characteristics. The extrudates had a greater hardness at the same moisture content when the screw speed was reduced. The specific mechanical energy (SME) increased as the screw speed increased, while increased moisture content resulted in a small reduction in SME. The lightness (L*) of most samples was found to increase as the target moisture content increased from 60% to 70%. The RPC:YPI blend was equivalent to proteins produced from other sources and comparable to the FAO/WHO standard requirements.

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“…The use of near-infrared spectroscopy (NIRS) to predict essential cassava root quality traits is gaining prominence. Thus, NIRS has been deployed to predict cassava root dry matter content and total carotenoids (Abincha et al, 2020(Abincha et al, , 2021Belalcazar et al, 2016;Sánchez et al, 2014), starch (Nkouaya Mbanjo et al, 2022, cassava boiled root cooking time (Namakula et al, 2023), and amylose (Nuwamanya, et al, 2022). This progress is hinged on the fact that NIRS offers fast, simultaneous, and accurate trait analyses with minimal sample preparation as compared to traditional laboratory wet chemistry methods (Alamu et al, 2020;Gaby et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava

Kanaabi,

Namakula,

Nuwamanya

et al. 2023

The Plant Genome

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This study focuses on meeting end‐users’ demand for cassava (Manihot esculenta Crantz) varieties with low cyanogenic potential (hydrogen cyanide potential [HCN]) by using near‐infrared spectrometry (NIRS). This technology provides a fast, accurate, and reliable way to determine sample constituents with minimal sample preparation. The study aims to evaluate the effectiveness of machine learning (ML) algorithms such as logistic regression (LR), support vector machine (SVM), and partial least squares discriminant analysis (PLS‐DA) in distinguishing between low and high HCN accessions. Low HCN accessions averagely scored 1–5.9, while high HCN accessions scored 6–9 on a 1–9 categorical scale. The researchers used 1164 root samples to test different NIRS prediction models and six spectral pretreatments. The wavelengths 961, 1165, 1403–1505, 1913–1981, and 2491 nm were influential in discrimination of low and high HCN accessions. Using selected wavelengths, LR achieved 100% classification accuracy and PLS‐DA achieved 99% classification accuracy. Using the full spectrum, the best model for discriminating low and high HCN accessions was the PLS‐DA combined with standard normal variate with second derivative, which produced an accuracy of 99.6%. The SVM and LR had moderate classification accuracies of 75% and 74%, respectively. This study demonstrates that NIRS coupled with ML algorithms can be used to identify low and high HCN accessions, which can help cassava breeding programs to select for low HCN accessions.

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Flour Pasting Properties Define Consumer Selection Decisions for Boiled and Flour-Based Cassava Food Products in Uganda

Cited by 3 publications

References 16 publications

Definition of sensory and instrumental thresholds of acceptability for selection of cassava genotypes with improved boiling properties

Definition of sensory and instrumental thresholds of acceptability for selection of cassava genotypes with improved boiling properties

Development and Characterization of Extrudates Based on Rapeseed and Pea Protein Blends Using High-Moisture Extrusion Cooking

Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava

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