Technique potential for classification of honey by electronic tongue

Wei, Zhenbo; Wang, Jun; Liao, Wenyan

doi:10.1016/j.jfoodeng.2009.03.016

Cited by 74 publications

(63 citation statements)

References 25 publications

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“…The use of the meta-heuristic SA algorithm for variable selection enabled the identification and selection of a minimum set of sensors required to fully discriminate monofloral honey samples according to their floral origin, after colour honey classification. Moreover, compared with previous reported applications of E-tongue for floral origin classification of honey, the performance of the proposed potentiometric E-tongue is similar [10,22] or quite superior [6,20,21,26].…”

Section: E-tongue Analysismentioning

confidence: 71%

“…In general, all these approaches showed good discrimination capabilities, precision, accuracy and reliability, but they are in general destructive, time-consuming and expensive, being unsuitable for in situ monitoring [10]. To overcome these drawbacks other more simple and userfriendly methodologies have been proposed, namely the use of potentiometric [6,10,21,22], voltammetric [23,24] or impedance [25] electronic tongues (E-tongues). The results reported in these studies clearly show that all these electrochemical devices can be used as effective and practical tools to discriminate honey according to their botanical origin, allowing distinguishing among different monofloral and/or polyfloral samples and in some cases among different geographical origins.…”

Section: Introductionmentioning

confidence: 99%

“…Wei et al [21] used a commercial potentiometric E-tongue with 7 sensors to classify 192 Chinese honey samples of different floral origin (Acacia, Astragali, Buckwheat, Coptis, Data, Motherwort, Radix Changll and Vitex) from a Chinese geographical area and 120 samples from five other Chinese geographical origins, harvested in 2008, using threepattern recognitions techniques (PCA; Cluster Analysis, CA; and Artificial Neural Networks, ANN), with correct classification rates greater than 90%. Zakaria et al [22] showed that an electronic nose used together with a chalcogenide-based potentiometric E-tongue containing ion-selective sensors, was the best solution for classifying honey of different floral origin.…”

Section: Introductionmentioning

confidence: 99%

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Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

Sousa

Dias

Veloso

et al. 2014

Talanta

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a b s t r a c tColour and floral origin are key parameters that may influence the honey market. Monofloral light honey are more demanded by consumers, mainly due to their flavour, being more valuable for producers due to their higher price when compared to darker honey. The latter usually have a high anti-oxidant content that increases their healthy potential. This work showed that it is possible to correctly classify monofloral honey with a high variability in floral origin with a potentiometric electronic tongue after making a preliminary selection of honey according their colours: white, amber and dark honey. The results showed that the device had a very satisfactory sensitivity towards floral origin (Castanea sp., Echium sp., Erica sp., Lavandula sp., Prunus sp. and Rubus sp.), allowing a leave-one-out cross validation correct classification of 100%. Therefore, the E-tongue shows potential to be used at analytical laboratory level for honey samples classification according to market and quality parameters, as a practical tool for ensuring monofloral honey authenticity.

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Section: E-tongue Analysismentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

Sousa

Dias

Veloso

et al. 2014

Talanta

View full text Add to dashboard Cite

show abstract

“…These studies clearly point out that e-tongues can be utilized as efficient and 453 practical tools to classify honeys according to their botanical (Dias et al, 2008;Wei and Wang, 2011;Wei et al, 2009) and geographical origin (Wei et al, 456 2009). of all the eight monofloral origins studied; additionally, the position of the samples on 468 the PCA score plots was related to the degree of ''sweetness".…”

Section: Adulteration 292mentioning

confidence: 97%

Electronic noses and tongues to assess food authenticity and adulteration

Peris

Escuder-Gilabert

2016

Trends in Food Science & Technology

230

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11 12Background 13 There is a growing concern for the problem of food authenticity assessment (and 14 hence the detection of food adulteration), since it cheats the consumer and can pose 15 serious risk to health in some instances. Unfortunately, food safety/integrity incidents 16 occur with worrying regularity, and therefore there is clearly a need for the 17 development of new analytical techniques. 18 Scope and Approach 19In this review, after briefly commenting the principles behind the design of electronic 20 noses and electronic tongues, the most relevant contributions of these sensor 21 systems in food adulteration control and authenticity assessment over the past ten 22 years are discussed. It is also remarked that future developments in the utilization of 23 advanced sensors arrays will lead to superior electronic senses with more 24 capabilities, thus making the authenticity and falsification assessment of food 25 products a faster and more reliable process. 26 Key Findings and Conclusions 27The use of both types of e-devices in this field has been steadily increasing along the 28 present century, mainly due to the fact that their efficiency has been significantly 29 improved as important developments are taking place in the area of data handling 30 and multivariate data analysis methods. 31 32

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“…The taste sensor array consisted of seven sensors based on Chemically Modified Field-Effect-Transistor (CHEMFET) technology and an Ag/AgCl reference electrode that converts the real-time chemical information concerning the presence of the compounds in the complex mixture into electrical signal (Li et al 2007). Taste sensors are partially and cross sensitive and the measurement represents the voltage differences between the sensors and reference electrode (Wei et al 2009). The sensors measure the dissolved chemical compounds in the solution and give the measurement of the voltage difference between the sensors and the reference electrode (Ag/AgCl) (which has a fixed voltage by definition).…”

Section: Samplesmentioning

confidence: 99%

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

et al. 2014

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Han F., Huang X., Teye E., Gu H., Dai H., Yao L. (2014): A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms. Czech J. Food Sci., 32: 532-537.Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (R pre ). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, R pre = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R pre = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.

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Technique potential for classification of honey by electronic tongue

Cited by 74 publications

References 25 publications

Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

Electronic noses and tongues to assess food authenticity and adulteration

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

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