Sensory Evaluation and Electronic Tongue for Sensing Flavored Mineral Water Taste Attributes

Abstract

In this article a trained sensory panel evaluated 6 flavored mineral water samples. The samples consisted of 3 different brands, each with 2 flavors (pear-lemon grass and josta berry). The applied sensory method was profile analysis. Our aim was to analyze the sensory profiles and to investigate the similarities between the sensitivity of the trained human panel and an electronic tongue device. Another objective was to demonstrate the possibilities for the prediction of sensory attributes from electronic tongue measurements using a multivariate statistical method (Partial Least Squares regression [PLS]). The results showed that the products manufactured under different brand name but with the same aromas had very similar sensory profiles. The panel performance evaluation showed that it is appropriate (discrimination ability, repeatability, and panel consensus) to compare the panel's results with the results of the electronic tongue. The samples can be discriminated by the electronic tongue and an accurate classification model can be built. Principal Component Analysis BiPlot diagrams showed that Brand A and B were similar because the manufacturers use the same aroma brands for their products. It can be concluded that Brand C was quite different compared to the other samples independently of the aroma content. Based on the electronic tongue results good prediction models can be obtained with high correlation coefficient (r(2) > 0.81) and low prediction error (RMSEP < 13.71 on the scale of the sensory evaluation from 0 to 100).

Sensory evaluation and electronic tongue for sensing grafted and non-grafted watermelon taste attributes

“…Several literature sources concluded that electronic tongues are capable of predicting, for example Italian red wine taste attributes (BURATTI et al, 2007), apple juice preference (BLEIBAUM et al, 2002), or to discriminate between mineral waters (SIPOS et al, 2012). Moreover, electronic tongue has been moved further in trend of getting prediction models based on the results of consumers (BLEIBAUM et Acta Alimentaria 47, 2018 and experts (SIPOS et al, 2013) for predicting different sensory attributes of different food products.…”

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

“…According to the score, the electronic tongue can predict most of the relatively acceptable sensory attributes except bitterness [27]. Until now, most of the existing researches are based on the comparison of artificial sensory evaluation to determine the accuracy of the intelligent bionic instrument and the grades of food samples, so that the intelligent bionic instrument can accurately and digitally express the information of samples [28][29][30][31][32]. These studies are important for achieving high-precision biomimetic prediction of samples.…”

A Predictive Model Based on Surface Electromyography to Assess the Easiness of Deglutition of Dysphagia Diets

“…The bitterness of dairy protein hydrolysates was corresponded to the degree of hydrolysis which is analyzed by sizeexclusive and reverse-phase chromatography elution patterns [2]. Additionally, PLS regression has shown that attributes of flavored mineral water are correlated with an electronic tongue device [3]. Thus, PLS analyses have been successful in providing datasets of instrumental measurement for variables that explain the sensations in the mouth during eating.…”