Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese

The objective of this study was to determine the potential of mid-infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid-infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid-infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Knowledge transfer: the facilitating attributes in supply-chain partnerships

This article examines the operational characteristics of supply-chain partnerships and identifies the relational attributes that cultivate knowledge transfer in such partnerships. A set of theoretical propositions are developed. A case study of a computer manufacturer's supply chain was conducted to examine their validity. The findings support the view that trust, commitment, interdependence, shared meaning, and balanced power facilitate knowledge transfer in supply-chain partnerships, and that knowledge transfer should be treated as a dynamic multistage process.

Temperature-tuned vector phase conjugation in azobenzene dye impregnated polymer films

Near-perfect vector phase conjugation was achieved at 488 nm in a methyl red dye impregnated polymethylmethacrylate film by employing a temperature tuning technique. Using a degenerate four-wave mixing geometry with vertically polarized counterpropagating pump beams, intensity and polarization gratings were written in the dye/polymer system using a vertically or horizontally polarized weak probe beam. Over a limited temperature range, as the sample was heated, the probe reflectivity from the polarization grating dropped but the reflectivity from the intensity grating rose sharply. At a sample temperature of approximately 50°C, the reflectivities of the gratings were measured to be equal and we confirmed that, at this temperature, the measured vector phase conjugate fidelity was very close to unity. We discuss a possible explanation of this effect.

Household attributes and the future demand for retirement housing

Ageing populations provoke the question of how much bespoke housing should be provided for the elderly. Older people are generally reluctant to move but as they age health circumstances may encourage moves into specialised accommodation. This paper reports an exercise in estimating the future demand for specialised independent living housing and the extent to which that demand will be for owner occupied accommodation or renting, using data for England. The approach is based on a behavioral model related to health and housing issues. The forecasts indicate a substantial increase in demand, growing at a faster rate than the population as a whole. If supply does not rise to meet these demands, serious problems arise in the quality of life of, and cost of caring for, older people; with implications for health care and social services.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Chemical characterisation and determination of sensory attributes of hydrolysates produced by enzymatic hydrolysis of whey proteins following a novel integrative process

The overall aim of this work was to characterize the major angiotensin converting enzyme (ACE) inhibitory peptides produced by enzymatic hydrolysis of whey proteins, through the application of a novel integrative process. This process consisted of the combination of adsorption and microfiltration within a stirred cell unit for the selective immobilization of β-lactoglobulin and casein derived peptides (CDP) from whey. The adsorbed proteins were hydrolyzed in-situ which resulted in the separation of peptide products from the substrate and fractionation of peptides. Two different hydrolysates were produced: (i) from CDP (IC50 =287μg/mL) and (ii) from β-lactoglobulin (IC50=128μg/mL). IC50 is the concentration of inhibitor needed to inhibit ACE by half. The well known antihypertensive peptide IPP and several novel peptides that have structural similarities with reported ACE inhibitory peptides were identified and characterized in both hydrolysates. Furthermore, the hydrolysates were assessed for bitterness. No significant difference was found between the control (milk with no hydrolysate) and hydrolysate samples at different concentrations (at, below and above the IC50).

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

Environmental regulation and colonization attributes of the long polar fimbriae (LPF) of Escherichia coli O157:H7

The ability of Escherichia coli O157:H7 to colonize the intestinal epithelia is dependent on the expression of intimin and other adhesins. The chromosome of E. coli O157:H7 carries two loci encoding long polar fimbriae (LPF). These fimbriae mediate adherence to epithelial cells and are associated with colonization of the intestine. In order to increase our knowledge about the conditions controlling their expression and their role in colonization of an animal model, the environmental cues that promote expression of lpf genes and the role of E. coli O157:H7 LPF in intestinal colonization of lambs were investigated. We found that expression of lpf1 was regulated in response to growth phase, osmolarity, and pH; that lpf2 transcription was stimulated during late exponential growth and iron depletion; and that LPF impacts the ability of E. coli O157:H7 to persist in the intestine of infected 6-week-old lambs.

Using attribute importance rankings within discrete choice experiments: an application to valuing bread attributes

We present a new Bayesian econometric specification for a hypothetical Discrete Choice Experiment (DCE) incorporating respondent ranking information about attribute importance. Our results indicate that a DCE debriefing question that asks respondents to rank the importance of attributes helps to explain the resulting choices. We also examine how mode of survey delivery (online and mail) impacts model performance, finding that results are not substantively a§ected by the mode of survey delivery. We conclude that the ranking data is a complementary source of information about respondent utility functions within hypothetical DCEs