Estimation of dry-cured ham composition using dielectric time domain reflectometry

Fulladosa, E.; Duran-Montgé, P.; Serra, X.; Picouet, Pierre; Schimmer, O.; Gou, P.

doi:10.1016/j.meatsci.2012.12.002

Cited by 19 publications

(17 citation statements)

References 21 publications

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“…This phenomenon also occurs between unfrozen and frozen-thawed raw data in this experiment, although it is more evident above 1.2 ns ( Figure 5 ). As stated by Fulladosa et al [ 55 ], this region is related to changes in the salt content, which would explain why this region, although presenting differences in the raw data, does not have a big contribution in the model to discriminate between fresh and frozen-thawed tuna.…”

Section: Discussionmentioning

confidence: 87%

“…In this study, the LV1 of the model ( Figure 4 ) shows that the region between 0.61 ns and 1.17 ns, with a peak at 0.76 ns, has the greatest contribution for the model, and so to distinguish between unfrozen and thawed tuna samples. According to Fulladosa et al [ 55 ], changes in the signal due to the water content of the sample are mainly expected to be found on the rising edge of the step function, which is located in the region between 0.6 ns and 0.8 ns ( Figure 5 ). This suggests that the sensor is sensitive to the water loss suffered during the freezing-thawing process and that the model is using the water content differences to classify between unfrozen and frozen-thawed samples.…”

Section: Discussionmentioning

confidence: 96%

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Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

Nieto-Ortega

Herreros

Foti

et al. 2021

Foods

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The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 96%

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

Nieto-Ortega

Herreros

Foti

et al. 2021

Foods

View full text Add to dashboard Cite

show abstract

“…Examples include structural analysis [ 27 , 28 ], water quality monitoring [ 29 , 30 , 31 ], and medical applications [ 32 , 33 , 34 , 35 , 36 , 37 ]. Aside from research considering quality classification of fresh [ 38 , 39 ] and cured meats [ 40 , 41 ], there is little evidence of microwave sensors making a significant impact in the food industry. This point is supported by a recent comprehensive review of electromagnetic wave sensors (from radio frequencies to X-ray) conducted by Damez and Clerjon [ 42 ].…”

Section: Microwave Spectroscopymentioning

confidence: 99%

Theoretical Basis and Application for Measuring Pork Loin Drip Loss Using Microwave Spectroscopy

Mason

Abdullah

Muradov

et al. 2016

Sensors

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During cutting and processing of meat, the loss of water is critical in determining both product quality and value. From the point of slaughter until packaging, water is lost due to the hanging, movement, handling, and cutting of the carcass, with every 1% of lost water having the potential to cost a large meat processing plant somewhere in the region of €50,000 per day. Currently the options for monitoring the loss of water from meat, or determining its drip loss, are limited to destructive tests which take 24–72 h to complete. This paper presents results from work which has led to the development of a novel microwave cavity sensor capable of providing an indication of drip loss within 6 min, while demonstrating good correlation with the well-known EZ-Driploss method (R2 = 0.896).

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“…As one of the main drawbacks of TDR is that in the microwave frequency, the dielectric properties of the food products are closely related to their composition (Bohigas et al, 2008;Bohigas & Tejada, 2009;Fulladosa et al, 2013) which could mask the relationship between the dielectric properties and the texture of the product. Furthermore, TDR is an invasive technology since the measurements are taken with a sensor that is in close contact with the sample.…”

Section: Drawbacks Of the Technologymentioning

confidence: 99%

“…extracted fromFulladosa et al (2013), shows the normalized reflected TDR signal versus time obtained from samples of dry-cured ham with different ranges of water content. The slope and the plateau of the different curves can be observed.…”

mentioning

confidence: 99%

Use of ultrasound for the characterization and correction of textural defects in dry-cured ham

Ruiz¹

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Para empezar, diré que es el final. No es un final feliz, tan sólo es un final. Pero parece ser que ya no hay vuelta atrás. Estos versos de la canción "Miedo" son perfectos para describir el momento en el que escribo los agradecimientos de mi tesis. Y es que parece ayer cuando Jose me brindó la oportunidad de comenzar esta aventura con ellos. Me acuerdo perfectamente del momento en el que me lo propuso. Luego llegó el papeleo para pedir becas y el apoyo del otro Jose. Poco después empezó todo. He tenido mucha suerte de haber caído aquí. Gracias a los dos por darme la oportunidad de hacer mi tesis doctoral con vosotros y gracias por todas las experiencias que he vivido durante este proceso. Sin duda alguna, una de las experiencias más bonitas hasta la fecha. He aprendido muchísimo y con vuestra ayuda todo ha sido más fácil. Junto a ellos, me gustaría incluir a todos los profesores del grupo ASPA, por ser un gran grupo de investigación en el que nunca faltan los almuerzos. También a todas las personas que han formado parte del laboratorio en algún momento durante estos últimos cuatro años y, sobre todo, a las componentes del Food Team, porque desde que llegaron, todo es más divertido. En especial a Lola, por enseñarme de todo un poco y por hacer que me ría tanto, y a Ángela, que ha sido mi gran amiga en toda esta aventura, compartiendo sabiduría, psicología, risas y sonrisas. De los grandes descubrimientos universitarios de esta etapa, me quedo con Martín, Rubén, Jordi, Domenico, Paolo y Vivian por todos los buenos momentos compartidos con ellos. Gracias a la Universitat Politècnica de València por concederme la beca para realizar mis estudios de doctorado, así como a los proyectos de investigación en los que he participado (RTA2013-00030-C03-02 y RTA2017-00024-C04-03). También agradezco a Amparo Quiles todos los momentos que pasamos en el microscopio aprendiendo de microestructura. De mi estancia de investigación, me gustaría agradecer la ayuda recibida por Megan Povey y Melvin Holmes, fue todo un orgullo trabajar en vuestro laboratorio, y por toda la gente que conocí durante esos meses, fue una experiencia inolvidable. Fuera del laboratorio, quiero agradecer a mi familia todo el apoyo que me ha dado siempre. A mis padres, por enseñarme, quererme y cuidarme tanto. A mi hermana, por ser única e irrepetible. A Pietro, per ascoltarmi ogni sera. Por último, pero no menos importante, me gustaría hacer mención especial a todos los cerditos donados a la ciencia para que pudiésemos hacer e investigar jamones viscosos, pero

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Estimation of dry-cured ham composition using dielectric time domain reflectometry

Cited by 19 publications

References 21 publications

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

Theoretical Basis and Application for Measuring Pork Loin Drip Loss Using Microwave Spectroscopy

Use of ultrasound for the characterization and correction of textural defects in dry-cured ham

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