Sensory and analytical properties of Spanish dry-cured ham of normal and defective texture

Garcı́a-Garrido, J.A; Quiles-Zafra, R.; Tapiador, J.; Castro, M.D. Luque de

doi:10.1016/s0308-8146(99)00144-2

Cited by 35 publications

(9 citation statements)

References 8 publications

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“…Previous studies suggest that the color of Spanish dry-cured hams has a strong correlation with their texture [ 45 ]. From our results, a negative correlation (at the 0.01 level) can be observed between the attributes of color fat and color homogeneity and the parameters of texture: hardness, chewiness, gumminess, heterogeneity and chewing residue.…”

Section: Resultsmentioning

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: Resultsmentioning

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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“…The proteolysis index (PI) was used for the characterization of pastiness, since a defective texture in ham has been associated with significant proteolysis if compared to ham with a standard texture (Garcia-Garrido et al, 1999;Gou et al, 2008;Ruiz-Ramírez et al, 2006). The range of PI identified for the three groups of pastiness was quite narrow since the values ranged from 28 to 44%.…”

Section: Chemical Characterization Of Pastinessmentioning

confidence: 99%

“…Likewise, sensory analysis, some chemical properties (pH, salt, moisture, non-protein nitrogen, etc. ), proteomic profile and volatile compounds of defective dry-cured ham have also been considered for the characterization of pastiness (Garcia-Garrido et al, 1999;López-Pedrouso et al, 2018;Pérez-Santaescolástica et al, 2018). However, existing literature reveals the need for a deeper understanding and characterization of pastiness.…”

Section: Introductionmentioning

confidence: 99%

Assessing the textural defect of pastiness in dry-cured pork ham using chemical, microstructural, textural and ultrasonic analyses

Contreras

Benedito

Quiles

et al. 2020

Journal of Food Engineering

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The dry-cured pork ham industry lacks non-destructive quality control techniques able to characterize relevant textural defects, such as pastiness or softness. The aim of this study is to analyze the feasibility of using different destructive and non-destructive techniques to characterize pastiness in dry-cured ham. Dry-cured ham processing was modified in order to induce different pastiness intensities over a wide range of moisture and salt contents. Afterwards, pastiness was assessed by sensory analysis and samples classified as non-pasty, medium-pasty and highly-pasty. Finally, chemical, textural, microstructural (LM and TEM) and ultrasonic analyses (velocity and attenuation coefficient) were carried out. Samples with a high degree of pastiness experienced an increase of 16.8% and 16.7% as regards the proteolysis index and relaxation capacity, respectively, and a 67.7% decrease in hardness compared to non-pasty hams. The microstructural analysis revealed that pastiness brought about great structural degradation. Ultrasonic velocity was significantly related to the salt (r=0.79) and moisture contents (r=0.69), but no influence of pastiness was found on the velocity. However, the attenuation coefficient increased as the pastiness rose and could be considered as a useful parameter for characterizing this complex textural defect. Therefore, ultrasound could be used not only to carry out a non-destructive characterization of dry-cured ham composition but also to assess pastiness.

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“…However, quality parameters of SM, as a surface muscle, determine water loss and salt intake dynamics, therefore they are expected to also have an effect on BF. The decision to study the proteolysis in BF derives from the fact that problems with texture and sensory quality due to increased proteolysis were mostly reported for this muscle (Parolari et al, 1994;Arnau et al, 1998;Garcia-Garrido et al, 1999;Morales et al, 2007). As an internal muscle, the BF muscle is not directly exposed to the salted surface and the salt reaches it at the later stages of processing (Toldra, 2002;Fantazzini et al, 2009).…”

Section: Resultsmentioning

confidence: 99%

Green ham pH value affects proteomic profile of dry-cured ham

Škrlep¹,

Mandelc²,

Javornik³

et al. 2010

Ital J Animal Sci

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In the present study we investigated the effect of green ham pH value on the proteomic profile of m. biceps femoris of the 14 months old ‘Kraški pršut’ dry hams. Two groups (n= 12) of samples were chosen according to green ham m. semimembranosus pH (i.e. low pH group with values from 5.51 to 5.60 and high pH group with values from 5.80 to 6.18). Two groups of hams were similar in regard to fat thickness and ham weight. The myofibrillar muscle protein fraction was extracted from dry-cured ham m. biceps femoris and separated using 2-dimensional electrophoresis technique. More than 1000 protein spots were detected on the gels, out of which 100 spots had significantly different intensity according to pH group. Notable clustering of the spots was observed on the gel images. Namely, the protein spots differentiating low and high pH groups were more intense in the acidic part of the gel, for the low pH group, in the basic part for the high pH group. The proteomic approach proved to be a suitable tool to investigate the influence of green ham pH on the pattern of protein degradation. However, further research (protein spots identification, association with sensory properties) is in progress

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Sensory and analytical properties of Spanish dry-cured ham of normal and defective texture

Cited by 35 publications

References 8 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

Assessing the textural defect of pastiness in dry-cured pork ham using chemical, microstructural, textural and ultrasonic analyses

Green ham pH value affects proteomic profile of dry-cured ham

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