Texture analysis of raw and cooked tubers of short‐duration lines of cassava by multivariate and fractional conversion techniques

Sajeev, M. S.; Sreekumar, J.; Moorthy, S. N.; Suja, G.; Shanavas, S.

doi:10.1002/jsfa.3055

Cited by 8 publications

(14 citation statements)

References 37 publications

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“…It is worth mentioning that for the same variety, the values of A and B are same, but among the varieties the values were different, registering a minimum value of 102.8 for 'H152' and maximum of 111.7 for 'Venjaramoodan'. Similar results were also reported for carrot (A=114.9, B=115.1) and radish (A=116.3, B=116.0) by Ramesh et al (1997) and for cassava tubers of short duration varieties (A=105.7-127.5, B=105.7-127.5) by Sajeev et al (2008). The validity of such models was ascertained by estimating the modulus values as explained by Ramesh et al (1997) and is given in Table 2.…”

Section: Resultssupporting

confidence: 86%

“…However, as explained earlier, the varietal dependence on the constants necessitated the development of a generalized model without giving weightage to individual varieties i.e., by considering these data as a whole for cassava tubers. The linear regression of the total data gave the value of A as 104.5 and B as 93.7, which were found to be lower than that of the short duration lines of cassava for which Sajeev et al (2008) got A=113.0 and B=110.8. When this general model was compared with the individual models, the modulus values were all below 5%, also showing extremely good fit to explain the variation of relative firmness with degree of cooking.…”

Section: Resultsmentioning

confidence: 74%

“…Among the various unit operations involved in cassava processing, mechanization is mainly needed in peeling, slicing and crushing. The design of such machineries depends on the engineering and mechanical properties of the tubers, for which several studies have been conducted (Odigboh 1983;Ohwovoriole et al 1988;Raja et al 1990;Nanda and Mathew 1996;Sajeev et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Studies have been conducted to analyze the textural properties of boiled cassava tubers by compression test, shear tests and texture profile analysis (Beleia et al 2004a, b;Sajeev et al 2008). Besides maturity of the tubers, growth environment, physico-chemical constituents and starch structural properties may also affect the cooking quality (Safo and Owusu 1992;Eggleston and Asiedu 1994;Ngeve 2003;Padonou et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Besides maturity of the tubers, growth environment, physico-chemical constituents and starch structural properties may also affect the cooking quality (Safo and Owusu 1992;Eggleston and Asiedu 1994;Ngeve 2003;Padonou et al 2005). The thermal softening behavior of roots and tubers can be explained by either a first order or a dual mechanism first order kinetic model (Paulus and Saguy 1980;Huang and Bourne 1983;Harada et al 1985;Bourne 1987;Rizvi and Tong 1997;Sajeev et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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Kinetics of thermal softening of cassava tubers and rheological modeling of the starch

et al. 2010

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Cassava or tapioca (Manihot esculenta Crantz) tubers having high amount of carbohydrate are utilized after boiling or processing into starch and flour. Textural properties of raw and cooked tubers depend on variety, maturity, growing environment, physico-chemical and starch properties. Starch is used in food preparations as gelling and thickening agent, stabilizer and texture modifier. This study aims at analyzing and modeling the textural, dynamic rheological and gelatinization properties of selected cassava varieties. The thermal softening behavior was analyzed by linear regression and fractional conversion techniques, rheological properties of the gelated starch by Maxwell and power law models. The varieties were classified based on their physico-chemical, texture profile, rheological and gelatinization properties by multivariate analysis. The textural, rheological and gelatinization properties were significantly affected by the varieties (p<0.05). Thermal softening of tubers was modeled by dual mechanism first order kinetic model with rate constant values ranging from 0.081 to 0.105 min −1 . Linear regression models with extremely good fit were obtained to explain the relationship between the degree of cooking and relative firmness. The dynamic spectra of the gelated starch showed the characteristics of concentrated biopolymer dispersion and described using Maxwell and power law model. The results showed that textural, rheological and gelatinization properties varied considerably among the varieties and besides the physico-chemical properties, interaction between them and structural make up of the tuber parenchyma had a great influence on cooking quality and rheological properties.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kinetics of thermal softening of cassava tubers and rheological modeling of the starch

et al. 2010

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Short‐duration cassava genotypes for crop diversification in the humid tropics: growth dynamics, biomass, yield and quality

et al. 2009

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Cell wall polysaccharides determine cooking quality in cassava roots

Sathitnaitham,

Ceballos,

Wonnapinij

et al. 2024

Plants People Planet

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Social Impact StatementCassava is a vital food source for millions worldwide, crucial for food security and economic stability. This study analyzed cell wall polysaccharides in cassava roots to understand their impact on cooking properties. We found these polysaccharides influence the textural attributes of cassava roots, essential for both cooking and consumption. The research highlights the need to further identify and analyze cassava cell wall components. By improving our understanding of these components, we can improve food security, affordability, and resilience in diverse regions, ultimately contributing to global food security and better aligning with consumer preferences.Summary The textural attributes of cassava roots significantly influence preferences in cooking and consumption as a food source, yet the specific components dictating these properties remain unclear. We aimed to identify the factors shaping the cooking properties of cassava roots. We conducted a compositional analysis of 22 traits in a diverse F1 biparental population derived from soft‐ and hard‐boiling progenitors. The traits encompassed cooking qualities, starch properties, and cell wall composition. Specific cell wall components including cellulose, xylan, un‐esterified, and methyl‐esterified homogalacturonan (HG) demonstrated a correlation with cooking quality attributes: sensory assessments related to texture (SAT) and water absorption during boiling (WAB). Correlation and regression analyses revealed that these wall components collectively contribute to 20% of SAT variability and 14% of WAB variability. SAT appeared to be influenced by methyl‐esterified and un‐esterified HGs, xylan, and cellulose, impacting the tensile strength of the cell wall. Conversely, WAB appeared to be associated with methyl‐esterified HG, potentially altering water absorption properties. Although genome‐wide association studies (GWAS) were unable to identify significant SNPs for SAT and WAB, notable associations emerged for cellulose, xylan, and un‐esterified‐ and methyl‐esterified HG. Candidate genes associated with these SNPs point towards diverse cell wall‐related proteins, transcription factors, sugar metabolism‐related genes, and glycosyl hydrolases. This study provides insights into the relationship between cassava root compositions and cooking characteristics and the role of wall components in determining the cooking quality of edible cassava. This information represents a substantial contribution towards the development of protocols for selecting varieties with texture preferences.

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Texture analysis of raw and cooked tubers of short‐duration lines of cassava by multivariate and fractional conversion techniques

Cited by 8 publications

References 37 publications

Kinetics of thermal softening of cassava tubers and rheological modeling of the starch

Kinetics of thermal softening of cassava tubers and rheological modeling of the starch

Short‐duration cassava genotypes for crop diversification in the humid tropics: growth dynamics, biomass, yield and quality

Cell wall polysaccharides determine cooking quality in cassava roots

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