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.     

Lactobacillus casei strains isolated from cheese reduce biogenic amine accumulation in an experimental model

Tyramine and histamine are the biogenic amines (BAs) most commonly found in cheese, in which they appear as a result of the microbial enzymatic decarboxylation of tyrosine and histidine respectively. Given their toxic effects, their presence in high concentrations in foods should be avoided. In this work, samples of three cheeses (Zamorano, Cabrales and Emmental) with long ripening periods, and that often have high BA concentrations, were screened for the presence of BA-degrading lactic acid bacteria (LAB). Seventeen isolates were found that were able to degrade tyramine and histamine in broth culture. All 17 isolates were identified by 16S rRNA sequencing as belonging to Lactobacillus casei. They were typed by plasmid S1-PFGE and genomic macrorestriction-PFGE analysis. Two strains (L. casei 4a and 5b) associated with high degradation rates for both BAs were selected to test how this ability might affect histamine and tyramine accumulation in a Cabrales-like mini-cheese manufacturing model. The quantification of BAs and the monitoring of the strains' growth over ripening were undertaken by RP-HPLC and qPCR respectively. Both strains were found to reduce histamine and tyramine accumulation. These two strains might be suitable for use as adjunct cultures for reducing the presence of BAs in cheese.     

Evaluating the In Vitro Metabolism of Docosahexaenoic Acid in Sheep Rumen Fluid

Rumen metabolism (e.g., biohydrogenation) of dietary unsaturated fatty acids (FA) is one of the main reasons why ruminant fats tend to be highly saturated and contain many isomerized FA intermediates. The process by which long-chain (20- to 24-carbon FA) polyunsaturated FA (LC-PUFA) are metabolized by rumen bacteria is not as well understood as that of linoleic or linolenic acids. In order to better understand the fate of LC-PUFA in the rumen several concentrations of docosahexaenoic acid (DHA) were evaluated in in vitro batch incubations ranging from 100 to 1,500 μg per 6 mL of incubation volume using rumen fluid from sheep and incubated for 0, 1, 2, 3, and 6 h. From the results, it was shown that DHA was extensively metabolized at low (100 to 300 μg/6 mL incubation volume), but not at high level of inclusion (800 μg). At 300 μg of DHA most of the depleted DHA was recovered as LC-DHA metabolites within the first 6 h of incubation, and at the lowest levels (100 μg of incubation volume) further metabolism is apparent at 6 h. Using SP-2560 GC columns several LC-DHA metabolites were shown to elute after 24:0 and just past DHA, a region generally free of interfering FA. The present in vitro study would appear to be a useful method to evaluate the production of DHA metabolites in combination with its depletion.     

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.     

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.     

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.