2014
DOI: 10.1016/j.lwt.2014.07.042
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Prediction of the sensory acceptance of fruits by physical and physical–chemical parameters using multivariate models

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Cited by 16 publications
(21 citation statements)
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“…Cruz et al () used multiple linear regression (MLR), partial least squares regression (PLS), and principal component regression (PCR) to model the overall liking of the yoghurts as a function of the scores attributed to the sensory attributes and they observed R 2 varied from.85 to.87 for calibration. Correa et al () also noted similar values to found in our study for prediction of fruits acceptance.…”
Section: Resultssupporting
confidence: 91%
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“…Cruz et al () used multiple linear regression (MLR), partial least squares regression (PLS), and principal component regression (PCR) to model the overall liking of the yoghurts as a function of the scores attributed to the sensory attributes and they observed R 2 varied from.85 to.87 for calibration. Correa et al () also noted similar values to found in our study for prediction of fruits acceptance.…”
Section: Resultssupporting
confidence: 91%
“…In relation to the product acceptance, Cadena et al () studied the sensory descriptive and physico‐chemical data of mango nectar correlated with an acceptance test by Partial least square (PLS) regression and Piombino et al () used the same methodology to correlate volatiles, sensory descriptors and physico‐chemical parameters and liking of tomatoes. Multivariate models were also used to Prediction of the sensory acceptance of fruits (Correa et al, ). However, there are few studies regarding quantitative correlations between physical/chemical parameters to consumer acceptance of processed products, which have a large variation in the production process.…”
Section: Introductionmentioning
confidence: 99%
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“…Today, many instrumental techniques are multivariate and based on indirect measurements of the chemical and physical properties of the sample [24,25]. Therefore, modern analytical tools can be defined as systems that are integrated, by the combination of instrumental techniques and the chemometric method with the sample that allows a better understanding of the compositional changes occurring within the process that affect the quality of the sample being analysed [14,[21][22][23].…”
Section: Introductionmentioning
confidence: 99%
“…In food science and technology, applied statistics is widely used for numerous purposes: design of experiments, modeling of response variables using response surface methodology, and recently, the application of multivariate statistical methods to unravel technological problems and to better understand complex experimental data (Farris & Piergiovanni, 2009;Nunes et al, 2014;Ortea & Gallardo et al, 2015). In this sense, undoubtedly, descriptive analysis (i.e., calculation of means, median, correlation, linear regression, standard deviation, among others) followed by inferential statistics (i.e., analysis of variances and multiple comparison of means) are the most frequently used methods (Kumar, Bansal, Sarma, & Rawal, 2014).…”
Section: Statistical Methods Applied In Grape Juice Studiesmentioning
confidence: 99%