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

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

Gou

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E. Fulladosa, P. Duran-Montgé, X. Serra, Pierre A. Picouet, O. Schimmer, et al. Estimation of dry-cured ham composition using dielectric time domain reflectometry. Meat Science, 2012, 93 (4), pp.873 - 879. 10.1016/j.meatsci.2012.12.002. hal-05073461

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

E. Fulladosa<sup>a,*</sup>, P. Duran-Montgé<sup>b</sup>, X. Serra<sup>a</sup>, P. Picouet<sup>a</sup>, O. Schimmer<sup>c</sup>, P. Gou<sup>a</sup>

## ARTICLE INFO

**Article history:**

Received 3 May 2012

Received in revised form 29 November 2012

Accepted 3 December 2012

Available online 7 December 2012

**Keywords:**

NaCl

Water

Fat

Dielectric time domain reflectometry

Predictive models

Non-destructive

## ABSTRACT

Development of real-time, non-destructive methods to characterize dry-cured ham is of interest to the food industry. Since dielectric properties change depending on the composition of the food product studied, time domain reflectometry (TDR) could be a useful method to characterize dry-cured ham. In this study, samples with different compositions were measured with a TDR device equipped with an open-ended coaxial line sensor. Partial least square regression (PLSR) analysis was used to develop predictive models to determine salt, water and fat contents and $a_w$ in dry-cured ham. Results show that salt content (RMSEV = 0.22%), water content (RMSEV = 1.67%) and $a_w$ (RMSEV = 0.0087) can be accurately determined, though fat content is determined with less precision (RMSEV = 2.81%). Saltiness, dryness and fatness of the samples, in the studied range, did not affect the accuracy of the predictions. Developed predictive models were accurate enough to consider the TDR device as a useful tool for characterizing and classifying dry-cured ham in industry.

© 2012 Elsevier Ltd. All rights reserved.

## 1. Introduction

During recent years development of non-destructive technologies based on electromagnetic radiation at different frequency ranges, such as X-rays (Fulladosa, Santos-Garcés, Picouet, & Gou, 2010; Garcia-Gil et al., 2011; Santos-Garcés, Muñoz, Gou, Sala, & Fulladosa, 2012) or near infrared spectroscopy (Collell, Gou, Arnau, & Comaposada, 2011) to determine quality parameters and provide solutions to food industry problems has increased. In the microwave frequency range, the dielectric properties of biological tissues are closely correlated with their water content and state (Kent & Jason, 1974) thus giving relevant information of the product's characteristics and composition (Danez & Clerjon, 2008). Many authors have shown how the composition of a foodstuff and its influence on the dielectric spectra can be used to predict different quality parameters (Sosa-Morales, Valerio-Junco, López-Malo, & García, 2010). Microwave dielectric spectroscopy has been used to determine the concentration of acetic acid in vinegar (Bohigas & Tejada, 2009), the sugar content in yogurt (Bohigas, Amigó, & Tejada, 2008), the maturity of Granny Smith apples (Castro-Giráldez, Fito, Chenoll, & Fito, 2010) and the internal quality of peaches (Lleó, Ruiz-Altisent, Hernández, & Gutiérrez, 2007). Dielectric spectroscopy can also provide accurate predictions of added water in processed prawns (*Pandalus borealis*), cod (*Gadus morhua*), pork and poultry meats (Kent & Anderson, 1996; Kent, Knöchel, Daschner, & Berger, 2000; Kent, Peymann, Gabriel, &

Knight, 2002). Differences between pale, soft and exudative (PSE), dark, firm and dry (DFD) and red, firm and non-exudative meats (RFN) have also been observed, which demonstrates that microwave dielectric spectroscopy is a suitable tool to determine pork meat quality (Castro-Giráldez, Aristoy, Toldrá, & Fito, 2010). Furthermore, it has been demonstrated that the dielectric properties are well suited to determine salt ions from liquid phase or when they are bound to molecules (actin/myosin system) in the raw muscle tissue during the salting treatment (Castro-Giráldez, Fito, & Fito, 2010).

The methodology used in the above described works relied on the measurement of the complex dielectric properties of samples by analyzing the response obtained within a wide range of microwave frequencies (1 Hz-20 GHz) and by using different types of probes. Nevertheless, there are techniques such as time domain reflectometry (TDR) that can measure dielectric properties by the interaction of an electromagnetic step containing a wide range of frequencies at the same time with the sample (Miura, Yagihara, & Mashimo, 2003). In this case, the obtained response is a time domain curve which integrates the information of all the emitted frequencies. Since TDR devices can be built smaller, and acquire the measurement signal faster in comparison to broadband frequency domain instruments (vector network analyzers), devices based on this technique could better fulfill the industrial requirements of on-site and online measurements. TDR instruments could facilitate the implementation of this technology in industry, allowing a non-destructive quality control, characterization and classification of food products. TDR, in combination with multivariable analysis, has been found to be useful to predict different variables

<footnote><sup>a</sup> IRTA, XaRTA, Food Technology, Finca Camps i Armet, E-17121 Monells (Girona), Spain</footnote>

<footnote><sup>b</sup> CENTA, Tecnio, Finca Camps i Armet, E-17121 Monells (Girona), Spain</footnote>

<footnote><sup>c</sup> Sequid GmbH, Anne-Conway-Str. 1, D-28359 Bremen, Germany</footnote>

<footnote>* Corresponding author. Tel.: +34 972 630 052; fax: +34 972 630 373. E-mail address: elena fulladosa@irta.es (E. Fulladosa).</footnote>
associated with loss of quality of stored fish (Kent et al., 2004, 2007). Nevertheless, no studies to determine the composition of dry-cured meat products have been carried out using either dielectric spectroscopy or TDR technology.

The aim of the present work was to study the variation of TDR curves depending on different dry-cured ham compositions and to develop predictive models to determine salt, water and fat contents and $a_w$ in dry-cured ham using TDR. The influence of saltiness, dryness and fatness of the samples on the accuracy of the prediction models was evaluated.

## 2. Material and methods

### 2.1. Physical basis of the technology

TDR in combination with an appropriate sensor, as for example the open-ended coaxial line sensor, is a well investigated method for acquiring information on the dielectric properties (complex permittivity) of materials within a wide frequency range. TDR most commonly generates an electromagnetic step signal (alternatively pulse signals can be used) which is applied to the material under test via the coaxial line sensor. The shape of the reflected TDR signal (voltage-over-time) is defined by the shape of the emitted step signal, the geometry of the sensor and most importantly by the complex permittivity of the material under test (Kent et al., 2007). The exact relationship between TDR curves and permittivity is rather complex and shall not be described in the context of this work.

The permittivity of a material is given by the relative complex permittivity (Risman, 1991) (Eq. (1)),

$$ \epsilon_r = \epsilon'_r - j \times \epsilon''_r \quad (1) $$

where $\epsilon'_r$ is the real part of relative permittivity (often named dielectric constant, even if it is frequency dependent) and $\epsilon''_r$ the relative loss factor with $j = \sqrt{(-1)}$. In a pure aqueous solution $\epsilon'_r$ and $\epsilon''_r$ can be described by the Debye relation and are temperature and frequency dependent (Ryynänen, 1995). The real part of the permittivity describes the ability of the material to store electromagnetic energy while the loss factor represents the ability of the material to convert the electromagnetic energy into thermal energy (Nunes, Bohigas, & Tejada, 2006).

When salts are dissolved in aqueous solution there is a modification of the relative complex permittivity, with a reduction of the dielectric constant and the increase of the loss factor. The loss factor can be represented by the Hasted-Debye model (Mudgett, 1985) which adds an ionic loss component. This added term (Eq. (2)) depends on the conductivity (and thus, on the concentration of ions) and frequency (Nunes et al., 2006)

$$ \epsilon''_r = \epsilon''_{rD} + \frac{\sigma}{\omega \times \epsilon_0} \quad (2) $$

where $\epsilon''_{rD}$ describes the polarization losses from the Debye relation and the latter term the losses due to ionic conductivity in which $\omega$ is the angular frequency, $\sigma$ the conductivity of the sample and $\epsilon_0$ permittivity of the free space.

#### 2.1.1. Equipment

The TDR device RFQ Scan 3.0 (Sequid GmbH, Bremen, Germany) was used to obtain time domain curves from dry-cured ham samples. In this TDR system, a step signal is generated and applied to the material under test via an open-ended coaxial line sensor, which is in close contact with the sample. The TDR applies a step signal with a 100 ps rise time and a repetition frequency of 20 MHz, corresponding to a frequency range from 20 MHz to approximately 5 GHz. The device samples the reflected

time domain response with a time-base resolution of 10 ps. The reflected signal is normalized (Schimmer, Osen, Schönfeld, & Hemmy, 2009) and TDR curves are transferred to a laptop computer for further data processing.

### 2.2. Sample preparation and TDR measurements

#### 2.2.1. NaCl solutions

Sodium chloride solutions were prepared: 1, 1.5, 2, 2.5 and 3 g of NaCl were dissolved in 100 ml of deionized water. TDR measurements were carried out directly on the prepared NaCl solutions and on deionized water at 15 °C. Eight repetition measurements were recorded on each sample avoiding any air bubbles between the sensor and the analyzed solutions.

#### 2.2.2. Dry-cured ham samples

Eighty boned dry-cured hams from different breeds were processed using standard and reduced salting procedures. Seventy boned hams were used to develop the calibration models (calibration set) and ten hams were used to validate the models (validation set).

Slices 2 cm-thick from different regions of the boned hams were obtained and kept at 15 °C. TDR measurements were carried out on *Semimembranosus* (SM), knuckle (KN) and *Biceps femoris* (BF) muscles and in some cases, on BF and *Semitendinosus* (ST) muscles (BF + ST) (Fig. 1), depending on the size and region of the ham from which the slice was taken. The obtained samples covered a wide range of salt (2.3–7.2%), water (37.1–65.6%) and fat (3.3–29.5%) contents. A good contact between the sample and the sensor was necessary to obtain good measurements. Due to the heterogeneity of the measured muscles, 8 repetition measurements were taken from each area. Following the TDR measurements, samples were cut, weighed and homogenized for chemical analysis. Salt and water contents were chemically determined in all the samples, although due to insufficient sample, $a_w$ and fat content were determined only on a subset. TDR measurements on air and on a subcutaneous fat sample were also recorded.

### 2.3. Chemical analysis

All analyses were performed in triplicate. Water content was analyzed by drying at 105 °C ± 2 °C until reaching constant weight (AOAC, 1990); the analytical standard deviation was 0.25%. Chloride content was determined according to ISO 1841-2 (1996) using a potentiometric titrator 785 DMP Titrino (Metrohm AG, Herisau, Switzerland); the analytical standard deviation was 0.05%. The total fat content was performed according to AOAC (2006) method, using the Foss Soxcap 2047 system (hydrolysis step) in combination with Soxtec Extraction 2055 system (Foss Analytical, Denmark); the analytical standard deviation was

<figure><img src="image_1.png" /><figcaption>Fig. 1. Image of a dry-cured ham slice in which different sampled areas are shown.</figcaption></figure>
0.80%. Water activity was measured using Aqualab (AquaLab Series 3TE, Decagon Devices, Inc., Pullman, Washington, USA); the analytical standard deviation was 0.0003.

## 2.4. Data treatment and statistical analysis

The TDR device samples the time domain curve in an interval from 0 ns to 2.56 ns, yielding 256 points with spacing of 10 ps. An interval from 0.6 ns to 2.3 ns was chosen for evaluation. This part of the curve includes the steepest part of the TDR step and therefore contains the most information. A partial least square regression (PLSR) analysis was used to develop the predictive models. A maximum of 10 PLS factors were chosen. The analyses were performed with the statistical package XLSTAT (Addinsoft, Paris, France). The obtained results were expressed in terms of regression parameters: coefficient of determination adjusted for degrees of freedom (R²), standard error of calibration (RMSEC) and standard error of validation (RMSEV) (Santos-Garcés, Gou, Garcia-Gil, Arnau, & Fulladosa, 2010). The residual predictive deviation (RPD) statistic was also used as additional parameter to evaluate the predictive ability of the models. RPD is the ratio between the standard deviation of the reference values and the error of validation. This is a quantitative measure for the assessment of the validation results. The smaller the error of prediction is, compared with the variance of the reference values, the larger the RPD value is and therefore, the better the models.

## 3. Results and discussion

Fig. 2 shows time domain curves obtained from aqueous solutions with different salt contents. The reflected signal resulting from the high microwave frequency components is mainly represented at the initial slope of the TDR curve whereas the reflected signal coming from lower frequency components is represented at the plateau. Increase of ionic conductivity (i.e. NaCl ions) mainly affects low microwave frequencies

(Eq. (2)) whereas increase of solution polarity (i.e. water) mainly affects high frequencies. Therefore, changes due to the water content of the sample are mainly expected at the initial slope of the curve (between 0.6 and 0.8 ns) whereas changes due to salt content are mainly expected at the end of the curve (between 1 and 2.3 ns). Nevertheless, the increase of water content also increases the ion contents producing an increase of ion conductivity which in turn results in a lower water reflected signal at the end of the TDR curve. In a simple matrix such as NaCl solutions (Fig. 2), the higher the salt content, the lower the maximum reflected signal intensity obtained in the TDR curve, obtaining a plateau in a first approximation after 1.2 ns. The modification of the reflected signal is directly related to the non-linear increase of the loss factor, especially for frequencies below 1 GHz due to the ionic component of the Hasted-Debye model (Eq. (2)). This evolution of the loss factor induces an increase of the conversion of electromagnetic energy into thermal energy, and the loss of reflected energy by the media. In contrast, a negative intensity was observed between 0.6 and 0.8 ns in all the aqueous solutions (Fig. 2B). The same shape was observed by Kent et al. (2004), using a similar instrumentation on fish fillets. This could be attributed to a frequency dependent phase shift of the reflected signal when it crosses the interface between the probe and the material under test which increases with the permittivity of the samples. In Fig. 2B, a slight effect of NaCl content can also be observed from 0.8 ns even though it is not in the region of the curve where variations due to salt were expected. This slight variation is due to the fact that salt ions bind water molecules in hydrating shells (and therewith decrease the dielectric constant) also producing variations at the initial slope of the TDR curve.

In more complex matrices such as dry-cured ham, other components of the sample such as fat, or the interactions of water molecules and salt ions with the meat matrix, may influence the reflected signal. In order to understand their effect, the average TDR curves of four groups of dry-cured ham samples with different ranges of salt content (Fig. 3), water content (Fig. 4) and fat content (Fig. 5) were investigated.

<figure><img src="image_2.png" /><figcaption>Fig. 2. Typical reflected TDR-signals for different aqueous solutions containing different salt concentrations.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 3. Reflected TDR-signals for average curves of four groups of dry-cured ham samples with different ranges of salt contents.</figcaption></figure>

Table 1 shows the average composition (for salt, water and fat contents) of each group of samples.

The increment of salt content in the dry-cured ham samples induced a decrease of the reflected signal intensity at the plateau of the TDR curve (Fig. 3). Nevertheless, interactions with other components have

to be considered when evaluating variations of the complex permittivity. Dielectric properties are modified not only by the increase of salt content in the ionic form, but also by the changes in the distribution of the water in meat (Picouet et al., 2012), the modification of protein conformation during the drying process, pH variations and the position

<figure><img src="image_4.png" /><figcaption>Fig. 4. Reflected TDR-signals for average curves of four groups of dry-cured ham samples with different ranges of water contents.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 5. Reflected TDR-signals for average curves of four groups of dry-cured ham samples with different ranges of fat contents.</figcaption></figure>

of salt ions in the meat matrix. All these phenomena can produce changes in the reflected signal as observed in Fig. 3, which shows a similar trend in aqueous solutions (Fig. 2). No important effect due to salt content variation was detected between 0.6 and 0.8 ns (Fig. 3B). The effect of salt ions on water molecule polarization (formation of hydrating shells) is not detected probably due to the low water content of samples. Since the average water content and fat content of each group of samples was similar (Table 1), it was concluded that the main differences observed in TDR curves were not due to these compounds.

Fat content also influences the reflected signal, producing an increase of intensity with the increase of fat content (Fig. 4). Fat tissue is mostly transparent to electromagnetic waves in the used frequency range due to the low polarity of fat. Thus, the TDR curve obtained from the measurement on a subcutaneous fat sample (containing

<caption>**Table 1**</caption>

Average salt, water and fat contents values of the four groups of dry-cured ham samples with different ranges of salt, water or fat contents (n = 60).

<table><thead><tr><th></th><th>Average salt<br>content (%)</th><th>Average water<br>content (%)</th><th>Average fat<br>content (%)</th></tr></thead><tbody><tr><td><strong>Salt content range (%):</strong></td><td></td><td></td><td></td></tr><tr><td>2.7–3.9%</td><td>3.6</td><td>52.7</td><td>8.1</td></tr><tr><td>4.0–4.9%</td><td>4.5</td><td>48.9</td><td>10.6</td></tr><tr><td>5.0–5.9%</td><td>5.5</td><td>50.4</td><td>8.4</td></tr><tr><td>6.2–7.2%</td><td>6.9</td><td>48.9</td><td>7.6</td></tr><tr><td><strong>Fat content range (%):</strong></td><td></td><td></td><td></td></tr><tr><td>3.3–7.0%</td><td>4.7</td><td>52.7</td><td>5.3</td></tr><tr><td>7.1–8.9%</td><td>4.4</td><td>52.3</td><td>8.1</td></tr><tr><td>9.1–16.8%</td><td>5.2</td><td>47.6</td><td>11.6</td></tr><tr><td>18.1–29.5%</td><td>4.9</td><td>46.0</td><td>22.2</td></tr><tr><td><strong>Water content range (%):</strong></td><td></td><td></td><td></td></tr><tr><td>37.6–44.7%</td><td>4.6</td><td>43.0</td><td>14.0</td></tr><tr><td>45.3–49.9%</td><td>5.4</td><td>47.2</td><td>10.9</td></tr><tr><td>50.2–53.9%</td><td>5.2</td><td>52.2</td><td>6.7</td></tr><tr><td>54.0–57.7%</td><td>4.0</td><td>55.4</td><td>7.0</td></tr></tbody></table>

a 95% fat approximately) was similar to that of air. In contrast, the intensity of time domain signal decreases with the increase of water content of the sample (Fig. 5). This fact is due to the increase of the loss factor because of the increase of polarizability of the water molecules (rotation) when an electromagnetic field is applied.

Both the initial slope and the plateau of the time domain curves were affected by the fat and water contents of the dry-cured ham samples. As expected, between 0.6 and 0.8 ns (Figs. 4B and 5B), there was a clear slope separation effect that was not observed in the case of salt when measured either in aqueous solutions (Fig. 2B) or in the dry-cured ham samples (Fig. 3B). Nevertheless, the effect of fat and water contents could not be distinguished or studied separately since they were negatively correlated and the average water content decreased when the average fat content increased (Table 1). The fact that the plateau of the curve was also affected by the fat and/or water contents was attributed to the complex interactions of water with the meat matrix (Picouet et al., 2012) and to other polar molecules which could influence the reflected signal. Besides, as explained previously, increase of water content also produces an increase of the content of ions affecting the ionic loss component (Eq. (2)), producing a decrease of the intensity at the plateau of the TDR curve. In any case, the results obtained highlight that the effect of fatness or dryness of the samples on the estimation of salt, water or fat contents needs to be studied.

Table 2 shows the regression parameters (R², RMSEC and RMSEV) of the predictive models obtained from partial least square regression (PLSR) analysis. RMSEV was similar to RMSEC for salt, water and fat contents but it was more than twice the RMSEC for $a_w$. The relationship between the measured and the predicted salt content using the developed predictive models is shown in Fig. 6. Salt predictions fitted well to the measured values for all the samples obtained from different areas of the slice with RMSEV of 0.22%. No effects of dryness of the sample on the prediction errors were observed (results not shown), obtaining similar errors of prediction for samples having very different water contents. In samples with fat content information, no underestimation of salt content was observed. Thus, the application of the
<caption>**Table 2**</caption>

R-square and error of the model of prediction (RMSEC) and error of validation (RMSEV) for the prediction models to determine salt, water and fat contents and $a_w$ in dry-cured ham.

<table><thead><tr><th rowspan="2">Models</th><th colspan="4">Calibration</th><th colspan="3">Validation</th></tr><tr><th>n</th><th>Range</th><th>R<sup>2</sup></th><th>RMSEC</th><th>n</th><th>Range</th><th>RMSEV</th></tr></thead><tbody><tr><td>Salt (%)</td><td>245</td><td>2.3–7.2</td><td>0.94</td><td>0.22</td><td>27</td><td>2.5–6.6</td><td>0.22</td></tr><tr><td>Water (%)</td><td>245</td><td>37.1–65.6</td><td>0.95</td><td>1.34</td><td>27</td><td>37.7–63.2</td><td>1.67</td></tr><tr><td>a<sub>w</sub></td><td>85</td><td>0.830–0.948</td><td>0.98</td><td>0.0042</td><td>10</td><td>0.843–0.949</td><td>0.0087</td></tr><tr><td>Fat (%)</td><td>60</td><td>3.3–29.5</td><td>0.62</td><td>2.99</td><td>10</td><td>3.9–16.6</td><td>2.81</td></tr></tbody></table>

n = number of samples; models were developed using 10 PLS factors.

predictive model for salt content in areas with different intramuscular fat content (in the range studied here, 3.3 to 29.5%) or in a product with different drying levels (in the range studied here, 37.1 to 65.6%) is feasible. The actual relationship in Fig. 6 appears to express a quadratic tendency. It could be due to the lower number of samples with a high and low salt content in comparison with those with an intermediate salt content.

Regression parameters of the water content and $a_w$ prediction models are also presented in Table 2 and the relationship between the measured and predicted water content and $a_w$ is presented in Figs. 7 and 8, respectively. Both water content and $a_w$ were predicted accurately, showing RMSEV of 1.67% and 0.0087, respectively. Neither salt nor water contents of the sample influenced the water and $a_w$ prediction errors. In samples with fat content information, no deviation of the predictions because of this parameter was observed (results not shown).

Fat content could also be estimated, but with less precision (Table 2) with $R^2$ of 0.62, a RMSEV of 2.88% and a RPD of 1.7. Since fat content in the dry-cured ham samples was inversely proportional to the water content (Table 1), a covariance between water and fat existed. In such a complex matrix, it is difficult to say if the relationship between the time domain curve and the fat contents was due to the fat content itself or to its relationship with the water content. This fact has previously been reported in fish by Kent (1990).

In the case of salt and water contents and $a_w$, although the precision of predictive models was lower than the reference methods, it could be high enough for screening purposes in industry since RPD values were 4.0, 3.5 and 3.1, respectively (Conzen, 2006). A direct application of this technology in dry-cured ham in industry would be the characterization and further classification of sliced dry-cured ham as a function of its salt content. The main drawback of the method is that it gives a local estimation of the studied parameters, the

<figure><img src="image_6.png" /><figcaption>**Fig. 7.** Relationship between measured and predicted water content.</figcaption></figure>

penetration of the microwave being no deeper than 3 mm. Thus, the applicability of the TDR method in the meat industry to classify and characterize the product depends on the homogeneity of the product and on the representativeness of the areas of measurement. As an example, it was observed that estimation of the overall salt content of a slice was possible, but the area of measurement had an important influence on the salt content estimation. The most representative area for giving the overall salt content of the slice was the one containing *Biceps femoris* and *Semitendinosus* muscles. When the measurement was taken in this area, the average salt content of the whole slice (lean part) was predicted with an error of only 0.18%. When the measurement was performed on other areas, such as *Semimembranosus* or knuckle muscles, the error of the overall salt content estimation increased to 0.38% and 0.32%, respectively. Results suggest that classification of sliced dry-cured ham in industry performed at the end of process could be feasible using TDR.

## 4. Conclusions

Salt content (RMSEV = 0.22%), water content (RMSEV = 1.67%) and $a_w$ (RMSEV = 0.0087) in dry-cured ham can be accurately determined

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Relationship between measured and predicted NaCl content.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 8.** Relationship between measured and predicted $a_w$.</figcaption></figure>
by using dielectric time domain reflectometry. Fat content is determined with less precision (RMSEV = 2.81%). No significant effect of fatness or dryness of the samples on any of the prediction errors was observed. Developed predictive models were accurate enough to consider the TDR device as a useful non-destructive tool for characterizing and classifying sliced dry-cured ham online in industry.

## Acknowledgments

This work was supported by INIA (contract no. RTA2010-00029-CO4-01 and PET-2007-08-C11-08 del Plan Específico de Investigación de Teruel) and the Sixth RTD Framework Programme Q-porkchains (contract no. FOOD-CT-2007-036245). The information in this document reflects only the authors' views and the community is not liable for any use that may be made of the information contained therein.

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