Multifractal analysis application to the study of fat and its infiltration in Iberian ham: Influence of racial and feeding factors and type of slicing.

This paper explores the multifractal features of different commercial designations of Iberian ham (acorn 100% Iberian ham, acorn Iberian ham, feed/pasture Iberian ham and feed Iberian ham). This study has been done by taking as input the fatty infiltration patterns obtained from digital image analysis of ham cuts comparing mechanic and manual slicing. The yielded results show the multifractal nature of fatty connective tissue in Iberian ham, only when knife cutting is applied, confirming the differences between the designations according to their genetics and feeding. Thus, the multifractal parameters presented in this work could be considered as additional information for checking Iberian ham quality by using non-destructive methods based on the combination of image analysis and predictive techniques. Meat industry can take advantage of these methods to evaluate meat products, especially when fat-connective tissue with complex pattern distribution is involved.

Multifractal analysis of validated wind speed time series.

Multifractal properties of 30 min wind data series recorded at six locations in Cadiz (Southern Spain) have been studied in this work with the aim of obtaining detailed information for a range of time scales. Wind speed records have been validated, applying various quality control tests as a pre-requisite before their use, improving the reliability of the results due to the identification of incorrect values which have been discarded in the analysis. The scaling of the wind speed moments has been analysed and empirical moments scaling exponent functions K(q) have been obtained. Although the same critical moment (qcrit) has been obtained for all the places, some differences appear in other multifractal parameters like γmax and the value of K(0). These differences have been related to the presence of extreme events and zero data values in the data series analysed, respectively.

Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions.

Ozone is a highly unpredictable pollutant which severely affects living conditions in urban and surrounding areas in the Mediterranean basin. This secondary pollutant periodically reaches extremely high concentrations, damaging human health. Multiple linear regression has been widely used in previous works due to the fact that it is a simple and versatile method for forecasting ozone concentrations. However, these models usually prove their validity using fulfillment of statistical constraints, ignoring other intrinsic characteristics existing in the time series, such as the temporal scaling behavior and the data distribution over different time scales. In previous works, it has been demonstrated that observed ozone time series are of a multifractal nature, meaning that the data distribution can be described by using the multifractal spectrum. This work focuses on the capacity of a forecasting model to reproduce the scaling features existing in an observed time series when several chemical and meteorological explanatory variables are introduced following the stepwise procedure. A comparison between the observed spectrum and the simulated ones for each step is used to check which explanatory variables better reproduce the multifractal nature in real ozone time series. It has been confirmed that a model with few explanatory variables allows reproducing the multifractal nature in the simulated time series with an acceptable accuracy without compromising the values of the coefficient of determination and root-mean-squared error, which were used as performance indicators.

Multifractal analysis application to the characterization of fatty infiltration in Iberian and White pork sirloins.

This paper applies the multifractal analysis based on the sandbox method to describe the distribution of fatty infiltration in Iberian and White pork meat with the aim of characterization and classification. This work was carried out by making photographs of sirloin cuts of both breeds and then treated with image analysis software. The obtained image data were stored in text format and constituted the input for multifractal analysis. The results obtained show that pork sirloin connective fatty tissue exhibits a multifractal type of scaling. Significant correlations were found between some of the parameters governing the multifractal behavior and fat percentage, especially in the case of Iberian sirloin. The differences found for the relationships between the generalized fractal dimensions and fat percentage provide information for the categorization of the studied meat pieces.

Description of the seasonal pattern in ozone concentration time series by using the strange attractor multifractal formalism.

Ozone is a secondary pollutant that, at high concentrations in urban environment, affects human health. Thus, suitable knowledge of ozone dynamics is essential for avoiding undesirable effects on the population. Ten-minute ozone concentration time series recorded at Córdoba (southern Spain) in 2007, for January, April, July and October, have been analysed by using the strange attractor multifractal formalism with the aim of describing the seasonal pattern of this pollutant. The multifractal nature of ozone concentration was detected in a study of the mass exponent functions for autumn-winter and spring-summer seasons. Multifractal spectra showed a greater heterogeneity in the low values of the time series. The presence of rare values in ozone data sets was also investigated, showing that the multifractal approach provided considerable detailed information on the time series structure that completes the statistical descriptive analysis.