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

Abstract

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.

Effects of ultrasonication, agitation and stirring extraction techniques on the physicochemical properties, health‐promoting phytochemicals and structure of cold‐brewed coffee

“…R 2 and Q 2 difference must be within 0.2–0.3. Relatively high values of RMS and RMSE and low R 2 would indicate a poor fit of the model (Nunes et al ., 2017). The results show that predicted and measured values were close to each other.…”

Effectiveness of different methods for the extraction of principle actives and phytochemicals content in medicinal herbals

“…Therefore, the source of variation was statistically significant for the test set evaluated. Overall, the result of the statistic ANOVA analysis could also be used to significant improvement of the extraction procedure of active principles found in diverse medicinal plants, considerably reducing the time and number of experiments performed to optimize these processes routinely used in the field of natural products (Gutt et al, 2016;Nunes et al, 2016;Pan et al, 2017;Francisco et al, 2018;Guizellini et al, 2018), and hence have an enormous benefice for diverse medicinal purposes.…”

Quality control of fresh strawberries by a random forest model

“…It is impracticable to monitor the sensory response of consumers, as sensory tests demand a great number of consumers and require a large amount of time and resources 7 . However, as the sensory properties of the food are affected by its chemical compounds it is possible to correlate the chemical profile of the strawberry with its sensory profile, and with the general acceptance of the fruit 2 .…”

“…Recently, several studies demonstrated that the physical and physical–chemical data can be correlated with the sensory response of consumers 6–12 . These studies used prediction models to comprehend the food system through regression analysis, correlating the physical and physical–chemical properties of food with their sensory responses.…”

“…The contributions of the current study are: (i) to show that it is possible to predict the consumer's satisfaction and willingness to pay more for strawberries using only physical and physical–chemical data; and (ii) to propose a robust methodology for developing regression and classification models for the sensory sciences and sensometrics communities. The latter is very important as several recent works have still employed less effective methodologies based on linear equations, 2 such as multiple linear regression 6,7 or partial least squares 20,21 …”