Prediction of the sensory acceptance of fruits by physical and physical–chemical parameters using multivariate models

Corrêa, Síntia Carla; Pinheiro, Ana Carla Marques; Siqueira, Heloísa Elias; Carvalho, Elisângela Elena Nunes; Nunes, Cleiton Antônio; Boas, Eduardo Valério de Barros Vilas

doi:10.1016/j.lwt.2014.07.042

Cited by 15 publications

(19 citation statements)

References 19 publications

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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%

“…Multivariate models were also used to Prediction of the sensory acceptance of fruits (Correa et al, 2014). 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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Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling

Nunes

Souza

Rodrigues

et al. 2017

Journal of Food Processing and Preservation

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Consumer acceptances for French bread, fish bread, and roasted coffees were calibrated against physical measurements of those products using Multiple Linear Regression. The models obtained were then validated and tested using the widespread used methods of cross‐validation, y‐randomization and external validation. In all cases, multivariate models presented R2 for calibration greater than.9, which was superior to those univariate ones. For the French bread analysis, the multivariate model performed well and the length of the cut on bread surface is the parameter that most strongly influenced this model; on the other hand, a large width of the cut on bread surface would greatly contribute to a lower acceptance. The model for predicting the acceptance of the fish bread also showed a good performance; the bulkier fish breads received a better acceptance. An efficient model was also obtained for the data set of roasted coffee; redder coffees were more accepted. Practical applications A multivariate regression was used in order to predict the consumer acceptance from measurements commonly performed for characterization of food products. Consumer acceptance can be predicted by easy and rapid physical (and/or chemical) measurements using regression models. Once built and validated, the models can be used to predict the consumer acceptance by rapid physical measurements on the products. This approach can be a useful method to be included as a quality control parameter on food industry.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling

Nunes

Souza

Rodrigues

et al. 2017

Journal of Food Processing and Preservation

View full text Add to dashboard Cite

show abstract

“…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%

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

et al. 2017

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Abstract:The production of several agricultural products and foods are linked with fermentation. Traditional methods used to control and monitor the quality of the products and processes are based on the use of simple chemical analysis. However, these methods are time-consuming and do not provide sufficient relevant information to guarantee the chemical changes during the process. Commonly used methods applied in the agriculture and food industries to monitor fermentation are those based on simple or single-point sensors, where only one parameter is measured (e.g., temperature or density). These sensors are used several times per day and are often the only source of data available from which the conditions and rate of fermentation are monitored. In the modern food industry, an ideal method to control and monitor the fermentation process should enable a direct, rapid, precise, and accurate determination of several target compounds, with minimal to no sample preparation or reagent consumption. Here, state-of-the-art advancements in both the application of sensors and analytical tools to monitor beverage and food fermentation processes will be discussed.

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“…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%

Reflectance of botanical, production and geographical origin on the unique compositional traits of purple grape juices

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Prediction of the sensory acceptance of fruits by physical and physical–chemical parameters using multivariate models

Cited by 15 publications

References 19 publications

Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling

Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling

The Application of State-of-the-Art Analytic Tools (Biosensors and Spectroscopy) in Beverage and Food Fermentation Process Monitoring

Reflectance of botanical, production and geographical origin on the unique compositional traits of purple grape juices

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