Artificial neural networks (Fuzzy ARTMAP) analysis of the data obtained with an electronic tongue applied to a ham-curing process with different salt formulations

Gil-Sánchez, Luís; Garrigues, Jose; García‐Breijo, Eduardo; Grau, Raúl; Aliño, M.; Baigts, Diana; Baviera, José Manuel Barat

doi:10.1016/j.asoc.2014.12.037

Cited by 27 publications

(6 citation statements)

References 25 publications

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“…Artificial intelligence methods were mainly investigated for the assessment of meat sensory qualities such as the tenderness, color, or marbling score/level [ 18 ]. In relation to dry-cured ham, ANNs have been used for the classification of hams according to the maturation time [ 19 ], the identification of feeding and the ripening time of ham [ 20 ], and for the assessment of the curing of hams [ 21 ]. To our knowledge, there is no data in the literature on the prediction of sensory attributes in dry-cured ham based on NIR information by ANNs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Hernández-Ramos

Quintana

Revilla

et al. 2020

Sensors

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Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters

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Section: Introductionmentioning

confidence: 99%

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Hernández-Ramos

Quintana

Revilla

et al. 2020

Sensors

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“…An interesting example of application of sensor arrays is the monitoring of a ham-curing process. Such samples were recently discriminated according to the curing time with 100% effectiveness by means of a potentiometric matrix [ 112 ].…”

Section: Main Applicationsmentioning

confidence: 99%

Electronic Tongue—A Tool for All Tastes?

et al. 2017

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Electronic tongue systems are traditionally used to analyse: food products, water samples and taste masking technologies for pharmaceuticals. In principle, their applications are almost limitless, as they are able to almost completely reduce the impact of interferents and can be applied to distinguish samples of extreme complexity as for example broths from different stages of fermentation. Nevertheless, their applications outside the three principal sample types are, in comparison, rather scarce. In this review, we would like to take a closer look on what are real capabilities of electronic tongue systems, what can be achieved using mixed sensor arrays and by introduction of biosensors or molecularly imprinted polymers in the matrix. We will discuss future directions both in the sense of applications as well as system development in the ever-growing trend of low cost analysis.

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“…This unit is an important part of the electronic tongue system; each one of the several sensing elements of the array generates a complicated response in a multi-component environment. Therefore, a relevant multidimensional data processing unit is needed; various pattern recognition methods have been applied for this purpose, such as PCA, Artificial Neural Networks (ANN), Partial Least Squares (PLS), and several others [ 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. These pattern recognition methods aim to recognize the sensed taste, categorize between different kinds of tastes, and classify tastes into given sets.…”

Section: Theory and Principle Of Operation Of The Electronic Tongumentioning

confidence: 99%

A High Sensitivity IDC-Electronic Tongue Using Dielectric/Sensing Membranes with Solvatochromic Dyes

Khan

Khalilian

Kang

2016

Sensors

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In this paper, an electronic tongue/taste sensor array containing different interdigitated capacitor (IDC) sensing elements to detect different types of tastes, such as sweetness (glucose), saltiness (NaCl), sourness (HCl), bitterness (quinine-HCl), and umami (monosodium glutamate) is proposed. We present for the first time an IDC electronic tongue using sensing membranes containing solvatochromic dyes. The proposed highly sensitive (30.64 mV/decade sensitivity) IDC electronic tongue has fast response and recovery times of about 6 s and 5 s, respectively, with extremely stable responses, and is capable of linear sensing performance (R2 ≈ 0.985 correlation coefficient) over the wide dynamic range of 1 µM to 1 M. The designed IDC electronic tongue offers excellent reproducibility, with a relative standard deviation (RSD) of about 0.029. The proposed device was found to have better sensing performance than potentiometric-, cascoded compatible lateral bipolar transistor (C-CLBT)-, Electronic Tongue (SA402)-, and fiber-optic-based taste sensing systems in what concerns dynamic range width, response time, sensitivity, and linearity. Finally, we applied principal component analysis (PCA) to distinguish between various kinds of taste in mixed taste compounds.

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Artificial neural networks (Fuzzy ARTMAP) analysis of the data obtained with an electronic tongue applied to a ham-curing process with different salt formulations

Cited by 27 publications

References 25 publications

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Electronic Tongue—A Tool for All Tastes?

A High Sensitivity IDC-Electronic Tongue Using Dielectric/Sensing Membranes with Solvatochromic Dyes

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