02248naa a2200289 a 450000100080000000500110000800800410001902400280006010000200008824500700010826000090017852014440018765000200163165000090165165000230166065000270168365000100171065000170172065300350173765300250177265300210179770000220181870000160184070000190185670000200187577300630189520860722018-05-02       2018    bl uuuu     u00u1 u    #d7 a10.1002/cche.100172DOI1 aVON BORRIES, G.  aPrediction models of rice cooking quality.h[electronic resource]  c2018  aBackground 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.  aCooking quality  aRice  aSensory evaluation  aAnálise organoleptica  aArroz  aOryza sativa  aPolytomous logistic regression  aPrincipal components  aTexture analyzer1 aBASSINELLO, P. Z.1 aRIOS, E. S.1 aKOAKUZU, S. N.1 aCARVALHO, R. N.  tCereal Chemistrygv. 95, n. 1, p. 158-166, Jan./Feb. 2018.