A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

Gil-Sánchez, Luís; Soto, Juán; Martínez‐Máñez, Ramón; García‐Breijo, Eduardo; Ibáñez, Javier; Llobet, Eduard

doi:10.1016/j.sna.2011.08.006

Cited by 73 publications

(36 citation statements)

References 24 publications

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“…By fusion of E-nose or E-tongue responses, the discrimination of meat adulteration could be improved by giving the overall sensory evaluation of meat, closer to human judgment. e combination of E-nose and E-tongue had been reported in the researches in geographical origin identification of potato creams [18] and virgin olive oil [19][20][21]; varieties and grade level discrimination of Chinese green tea [22], black tea [23], red wine [24], and fruit juice [25,26]; cultivars discrimination and characterisation of Perilla frutescens [27] and species differentiation of coffee [28]; freshness evaluation of wine [29] and milk [30]; quality differentiation [31]; and authenticity assessment [32]. ese studies provided references for the in-depth study of the food quality inspection of E-nose and E-tongue.…”

Section: Introductionmentioning

confidence: 99%

Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

Xiao

Wang

et al. 2019

Journal of Food Quality

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An E-panel, comprising an electronic nose (E-nose) and an electronic tongue (E-tongue), was used to distinguish the organoleptic characteristics of minced mutton adulterated with different proportions of pork. Meanwhile, the normalization, stepwise linear discriminant analysis (step-LDA), and principle component analysis were employed to merge the data matrix of E-nose and E-tongue. The discrimination results were evaluated and compared by canonical discriminant analysis (CDA) and Bayesian discriminant analysis (BAD). It was shown that the capability of discrimination of the combined system (classification error 0%∼1.67%) was superior or equable to that obtained with the two instruments separately, and E-tongue system (classification error for E-tongue 0∼2.5%) obtained higher accuracy than E-nose (classification error 0.83%∼10.83% for E-nose). For the combined system, the combination of extracted data of 6 PCs of E-nose and 5 PCs of E-tongue was proved to be the most effective method. In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. Good correlations were found between the signals obtained from E-tongue, E-nose, and fusion data of E-nose and E-tongue and proportions of pork in minced mutton with correlation coefficients higher than 0.90 in the calibration and validation data sets. And BPNN was proved to be the most effective method for the prediction of pork proportions with R2 higher than 0.97 both for the calibration and validation data set. These results indicated that integration of E-nose and E-tongue could be a useful tool for the detection of mutton adulteration.

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Section: Introductionmentioning

confidence: 99%

Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

Xiao

Wang

et al. 2019

Journal of Food Quality

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“…In addition, different varietals of wine from different vintages were studied and discriminated using the electronic tongue (Riul et al ., ). A combination of the e‐tongue and e‐nose has also been used in the study of wine spoilage (Gil‐Sánchez et al ., ) and the prediction of the sensory parameters of wines (Cetó et al ., ). Other authors have also used the electronic tongue to predict chemical composition of wines (Rudnitskaya et al ., ).…”

Section: Resultsmentioning

confidence: 99%

Determination of 4‐ethylcatechol in a Merlot wine using sensory evaluation and the electronic tongue

Diako

Vixie

Weller

et al. 2017

Int J of Food Sci Tech

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The objective of this study was to determine the influence of 4-ethylcatechol (4-EC) on the sensory profile of Brettanomyces-contaminated Merlot wine and evaluate electronic tongue discrimination. Using sensory evaluation panels, the consumer detection threshold (DT) and the consumer rejection threshold (CRT) of 4-EC were determined in Merlot containing 493, 714, 1035 and 1500 lg L À1 4-EC. The DT of 4-EC in Merlot was 823 lg L À1 , while the CRT was 1323 lg L À1 . The electronic tongue discriminated index (DI = 82%) among the samples, with hierarchical clustering showing a clear distinction between the control sample and the spiked samples. The lowest concentration distinguished by the electronic tongue was 493 lg L À1 , a lower value than the sensory threshold determined. These findings suggest that for the detection of 4-EC in Merlot, the e-tongue may be more sensitive than some consumers and the e-tongue may be a suitable methodology for detection of subthreshold concentrations of chemical compounds in wine.International Journal of Food Science and Technology 2017 4-EC in Merlot: sensory and e-tongue detection C. Diako et al. Figure 4 Cluster dendrogram for Merlot wines (n = 5) with varying levels of 4-ethylcatechol (expressed as lg L À1 ) based on electronic tongue signal intensities for sweet, sour, bitter, salty, umami, metallic and spicy sensors and Euclidean distances with complete linkage.

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“…For instance, an e-tongue composed of “all-solid-state” potentiometric sensors was able to monitor the levels of acetic acid in white wines (Verrelli et al, 2007b). An e-nose combined with an array of potentiometric sensors has been used to monitor the spoilage of wines that was also followed by the titrable (total) acidity (Gil-Sánchez et al, 2011). …”

Section: E-noses and E-tongues In The Wine Industrymentioning

confidence: 99%

Electronic Noses and Tongues in Wine Industry

Rodrı́guez-Méndez

Saja

Gonzalez-Anton

et al. 2016

Front. Bioeng. Biotechnol.

100

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The quality of wines is usually evaluated by a sensory panel formed of trained experts or traditional chemical analysis. Over the last few decades, electronic noses (e-noses) and electronic tongues have been developed to determine the quality of foods and beverages. They consist of arrays of sensors with cross-sensitivity, combined with pattern recognition software, which provide a fingerprint of the samples that can be used to discriminate or classify the samples. This holistic approach is inspired by the method used in mammals to recognize food through their senses. They have been widely applied to the analysis of wines, including quality control, aging control, or the detection of fraudulence, among others. In this paper, the current status of research and development in the field of e-noses and tongues applied to the analysis of wines is reviewed. Their potential applications in the wine industry are described. The review ends with a final comment about expected future developments.

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A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

Cited by 73 publications

References 24 publications

Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork

Determination of 4‐ethylcatechol in a Merlot wine using sensory evaluation and the electronic tongue

Electronic Noses and Tongues in Wine Industry

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