Estimating sensory texture of cooked rice using full and optimized predictive regression models

Lee, Youngseung; Kwak, Han Sub; Lenjo, Marura; Meullenet, Jean François

doi:10.9755/ejfa.2015-09-793

Cited by 4 publications

(1 citation statement)

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“…The use of logistic regression to adjust models with ordinal response variables is an important research tool for statisticians, biomedical practitioners, and epidemiologists according to a recent study (Vaitheeswaran, Subbiah, Ramakrishnan, & Kannan, ). For some reason, it is not a practice in research of sensory texture of rice although statistical and mathematical methodology is applied to estimation of sensory texture attributes and quality preselection of rice (Castro et al., ; Lee, Kwak, Lenjo, & Meullenet, ; Sesmat & Meullenet, ). However, the response investigated on those studies are nominal and in a different context of the current study.…”

Section: Introductionmentioning

confidence: 99%

Prediction models of rice cooking quality

et al. 2018

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Background and objectives: Rice quality can be primarily assessed by evaluating its texture after cooking. The classical sensory evaluation is an expensive and time-consuming method as it requires training, capability, and availability of people. Therefore, this study investigated the possibility of replacing sensory evaluation by analyzing the relationship between sensory and instrumental texture and viscosity measurements. Findings: Models predicting the sensory evaluation were developed by applying statistical methods such as principal component analysis and polytomous logistic regression. The level of prediction efficiency of these models was obtained by estimating the apparent misclassification error rate and also using the ROC curve graph. The results indicated that the instrumental texture measurements were consistently related to sensory analysis. Similarly, viscosity measurements enabled the prediction of results obtained by sensory texture evaluation. Conclusions: Principal component analysis together with polytomous logistic regression is an efficient method to predict sensorial stickiness of rice using viscosity measures of texture as predictors. Significance and novelty: The current study was able to correctly predict sensory stickiness in about 86% of cases using just one principal component formed by a combination of measures of apparent amylose content, gelatinization temperature, and RVA parameters. K E Y W O R D S polytomous logistic regression, principal components, rice cooking quality, RVA, sensory evaluation, texture analyzer

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Section: Introductionmentioning

confidence: 99%