Functional data analysis in shape analysis

Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out.     

Precise segmentation of the bulbar conjunctiva for hyperaemia images

Hyperaemia is an excess of blood in a tissue that causes the appearance of an unusual red hue in the affected area. It is a common occurrence in the bulbar conjunctiva, where it can be related to multiple pathologies, such as conjunctivitis or dry eye syndrome. Specialists grade hyperaemia by means of a tedious, subjective, non-repeatable and time-consuming process. These drawbacks can be solved with the automatisation of the process by means of image processing techniques. The automatic segmentation of the conjunctiva is an important part of the process, as it ensures the absence of noise in posterior stages of the methodology. However, there are several issues of illumination and focus in the input videos that difficult the process. In this work, several segmentation algorithms are proposed and compared in order to obtain an accurate location of the bulbar conjunctiva.     

Assessing performance of skim milk ultrafiltration by using technical parameters

In the present study a mathematical model has been applied to interpret the permeate flux decay that occurs during the process of concentrating skim milk by ultrafiltration using a commercial membrane module. The effects on membrane fouling of two operational variables, temperature and transmembrane pressure, have been studied using technical parameters. An energy analysis has demonstrated that the major energy consumption takes place in the thermal process and not in the mechanical pumping of the fluid. In addition, higher increments in permeate volume can be achieved by increasing transmembrane pressure, not temperature. The mathematical analysis presented here permits the evaluation of optimum values of the engineering parameters necessary to design and operate skim milk ultrafiltration units.     

Reusable Localized Surface Plasmon Sensors Based on Ultrastable Nanostructures

Nanoparticle arrays created by nanosphere lithography are widely used in sensing applications since their localized surface plasmon resonances are extremely sensitive to changes in the local dielectric environment. A major drawback for any biologically oriented sensing application of conventionally produced particle arrays is the lack of stability of the nanoparticles in aqueous media and buffer solutions. Here, a robust and reusable nanoscale sensing platform based on localized surface plasmon resonances of gold nanoparticles embedded in a silicon dioxide matrix is presented. The architecture exhibits extremely high stability in aqueous environments and can be regenerated several times by simple mechanical cleaning of the surface. The platforms surface is ultraflat by design, thus making it an ideal substrate for any bio‐oriented sensing application.     

Specific detection and quantification of Aspergillus flavus and Aspergillus parasiticus in wheat flour by SYBR® Green quantitative PCR

Aflatoxins are important mycotoxins that represent a serious risk for human and animal health. These mycotoxins are mainly produced by Aspergillus flavus and Aspergillus parasiticus, two closely related species with different array of aflatoxins. In this work, two specific quantitative PCR (qPCR) assays were developed to detect and quantify both species in wheat flour using primers based on the multicopy ITS2 rDNA target sequence. The species specificity of the assays was tested in a wide range of strains of these species and others colonizing the same commodities. The sensitivity of the assay was estimated in 2.5 pg/reaction in both species. Discrimination capacity for detection and relative quantification of A. flavus and A. parasiticus DNA were analyzed using samples with DNA mixtures containing also other fungal species at different ratios. Both qPCR assays could detect spore concentrations equal or higher than 10⁶ spores/g in flour samples without prior incubation. These assays are valuable tools to improve diagnosis at an early stage and in all critical control points of food chain integrated in HACCP strategies.     

Reactivity between liquid Si or Si alloys and graphite

The fundamental issues of the reaction at liquid Si/graphite interfaces between Si melting point (1412 °C) and 1600 °C are studied on the basis of results obtained with polycrystalline graphite concerning the growth kinetics of the interfacial reaction layer and the microstructure and morphology of this layer. Experiments were also performed using vitreous carbon substrates. Results are also reported for Si–Al alloys at 1000 °C. The elementary process controlling the growth kinetics is determined and a model is proposed to describe the different stages of the interfacial reaction.     

Revision of ochratoxin a production capacity by the main species of Aspergillus section Circumdati. Aspergillus steynii revealed as the main risk of OTA contamination

Ochratoxin A (OTA) is a highly toxigenic mycotoxin commonly present in a number of diverse agroproducts. Aspergillus Section Circumdati includes some of the most important OTA-producing species: Aspergillus ochraceus, Aspergillus elegans and the recently described Aspergillus steynii and Aspergillus westerdijkiae. In this work, OTA production by several strains of these four species from diverse origins and food matrices was examined. Identification of all the strains was carried out by specific PCR assays. The strains were cultivated in CYA solid medium and OTA was measured by HLPC. This study demonstrated for the first time the high capacity of A. steynii strains to produce OTA at higher levels than A. westerdijkiae,A. ochraceus and A. elegans strains showed low levels or no production at all. In conclusion, the results of this study suggest that the occurrence of A. steynii and A. westerdijkiae might represent the major potential risk for OTA contamination due to their high production and the diversity of commodities that these species may contaminate.     

Authentication of organic and conventional eggs by carotenoid profiling

Organic production benefits from fair competition and sustained consumer confidence. The latter can only be assured by paper trailing and verification assessments. Traditional analytical strategies for guaranteeing quality and uncovering adulteration have relied on the determination of the amount of a marker compound or compounds in a material and a subsequent comparison of the value(s) obtained with those established for equivalent material. Since it is unlikely to find a unique marker that allows discrimination between organic and conventional produce, selective fingerprinting (profiling) combined with chemometrics is a more promising approach. In this study, carotenoid High Performance Liquid Chromatography–Diode Array Detection profiling combined with k-nearest neighbour classification chemometrics was used to predict the production system (organic vs. non-organic) of chicken eggs. A proof-of-concept set (training set for the model) was composed of eggs from 24 organic farms, 12 free range, and 12 barn farms in the Netherlands. The identities of organic, free range, and barn eggs were predicted with success rates of 100%, 100%, and 84%, respectively. The identity of eggs determined using an additional market test set with eggs from 12 organic, 12 free range, and 12 barn farms originating from the Netherlands and New Zealand resulted in correct classifications for 35 of the 36 samples. The results of this study indicate that this fingerprint approach is a promising tool for analytical verification of the production system of organic eggs.     

A boosting approach for corporate failure prediction

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies. E. A. Cortés · M. G. Martínez · N. G. Rubio. The authors teach Statistics at the Faculty of Economic and Business Sciences in the University of Castilla-La Mancha. Esteban Alfaro completed his degree in Business in 1999 and got his Ph.D. in Economics in 2005, both in the University of Castilla-La Mancha. His thesis dealt with the application of ensemble classifiers to corporate failure prediction. Matías Gámez got his degree in Mathematics at the University of Granada in 1991 and finished a Master in Applied Statistics a year after. He completed his Ph.D. in Economics at the University of Castilla-La Mancha in 1998 on the application of geo-statistical techniques to the estimation of housing prices. Noelia García got her degree in Economics at the University of Madrid (UAM) in 1996 and completed her Ph.D. in Economics in 2004 on the construction of an intelligent and automated system for property valuation through the combination of neural nets and a geographic information system (GIS). Current research deals with spatial statistics and the combination of classifiers (decision trees and neural nets) for solving heated topics in the Economics.