Objective monitoring of milk quality using a dynamic headspace gas chromatograph and computer-aided data processing

This research was undertaken because of the need to develop an objective method for quality control of milk. A major problem in the dairy industry is off-flavour often found in milk. Quality control of milk is heavily dependent upon sensory evaluations supported by microbiological and chemical analyses. The chief purpose of this research was to demonstrate a simple and economical system for quality control of milk using a gas chromatogf aph and computer-aided data processing. Two experiments were conducted: one using microbial off-flavours and another using chemically induced off-flavours. First, Ultra High Temperature (UHT)-sterilized milk was inoculated with Pseudomonas fragi, Psuedomonas fluorescens, Lactococcus lactis, Enterobacter aerogenes, Bacillus subtilis and a mixed culture (L. lactis: E. aerogenes: P. fragi = 1:1:1) with approximately 10⁴ CFU mL⁻¹. The samples were stored at 4°C up to 10 days for P. fragi and P. fluorescens and at 30°C up to 24 hours for L. lactis, E. aerogenes, B. subtilis and the mixed culture. Several multivariate analyses were applied to the standaridized peak areas of GC data. A new multivariate analysis technique, principal component similarity analysis (PCS), was capable of classifying milk samples with regard to bacterial species and storage time. Artificial neural networks (ANN), partial least squares regression analysis and principal component regression analysis were also applied. ANN provided the most accurate means of classification. Secondly, pasteurized milk was treated to develop different off-flavours (lightinduced, oxidized, cooked and heated) according to the procedures of the American Dairy Science Association. The same pasteurized milk samples as those used for gas chromatographic analysis were used for sensory evaluation. Gas chromatography (GC) combined with PCS was more effective than sensory evaluation as a means of distinguishing milk samples. It was concluded that a combination of GC and chemometric methods may have great potential in evaluating the chemical and microbial quality of milk.Land and Food Systems, Faculty ofGraduat

Improving Emulsifying Properties of Egg White Protein by Partial Hydrolysis Combined with Heat Treatment

The present study investigated proteolysis combined with heat treatment to make hen Egg White (EW) anefficient emulsifier. EW was hydrolyzed by protease (Thermoase®) at various Enzyme Concentrations (EC) (w/w,0.1%, 0.2%, 0.4%, 0.8%), followed by heating at 90°C for 8 min. Results showed that optimal emulsifying abilityand stability, determined by measurement of emulsion turbidity, were obtained when EC was 0.4%, followed byheat treatment at 90°C. The hydrolysate thus prepared had higher emulsifying ability and stability than either nativeegg white (nEW) or small molecular weight EW peptides (Runpep®), close to the properties of Egg Yolk (EY)which was a reference as a food emulsifier. Surface hydrophobicity (H0) was found to be linearly related to theemulsifying activity and stability of hydrolyzed egg white proteins

Random-Centroid Optimization

The objectives of this chapter are to illustrate the RCO steps on computers, to monitor with special emphasis on the mapping process for determining the search directions, and to improve the efficiency of RCO as well as RCG later. It is our strong belief that the peptide QSAR could be the most convenient, user-ftiendly strategy in the proteomics as well as genomics projects in the near future.Fil: Nakai, Shuryo. University of British Columbia; CanadáFil: Horimoto, Yasumi. University Of Guelph. Department Of Integrative Biology.; CanadáFil: Dou, Jinglie. University of British Columbia; CanadáFil: Verdini, Roxana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentin

Conserved Prosegment Residues Stabilize a Late-Stage Folding Transition State of Pepsin Independently of Ground States

<div><p>The native folding of certain zymogen-derived enzymes is completely dependent upon a prosegment domain to stabilize the folding transition state, thereby catalyzing the folding reaction. Generally little is known about how the prosegment accomplishes this task. It was previously shown that the prosegment catalyzes a late-stage folding transition between a stable misfolded state and the native state of pepsin. In this study, the contributions of specific prosegment residues to catalyzing pepsin folding were investigated by introducing individual Ala substitutions and measuring the effects on the bimolecular folding reaction between the prosegment peptide and pepsin. The effects of mutations on the free energies of the individual misfolded and native ground states and the transition state were compared using measurements of prosegment-pepsin binding and folding kinetics. Five out of the seven prosegment residues examined yielded relatively large kinetic effects and minimal ground state perturbations upon mutation, findings which indicate that these residues form strengthened and/or non-native contacts in the transition state. These five residues are semi- to strictly conserved, while only a non-conserved residue had no kinetic effect. One conserved residue was shown to form native structure in the transition state. These results indicated that the prosegment, which is only 44 residues long, has evolved a high density of contacts that preferentially stabilize the folding transition state over the ground states. It is postulated that the prosegment forms extensive non-native contacts during the process of catalyzing correct inter- and intra-domain contacts during the final stages of folding. These results have implications for understanding the folding of multi-domain proteins and for the evolution of prosegment-catalyzed folding.</p></div

Brønsted plot.

<p>A comparison of the mutation effects on the folding activation energy as a function of the change in equilibrium stability. Dashed lines indicate the trend lines for ΔΔ<i>G</i> values that would give rise to Φ-values of 0 or 1 and error bars show ± SD.</p

Changes in binding and folding constants^a and associated free energies^b upon mutation of the PS.

a<p>Folding rate constants were measured at 15°C while binding constants were determined at 20°C. Data are given as the mean ± SD obtained from non-linear curve fitting.</p>b<p>Free energy units are in kcal/mol with ± SD derived by propagation of errors.</p

Effects of PS point mutants on binding and catalyzing pepsin folding.

<p>(A) Structure of pepsinogen (PDB code: 3PSG) with the PS (pink) located between the N- and C-terminal lobes, forming part of a six-stranded β-sheet, and K36 of the PS interacts with the catalytic residues, D32 and D215 (red). PS residues selected for mutation to Ala are shown in space-filling form and coloured according to type (grey-hydrophobic, orange-polar, blue-basic, red-acidic). (B) Comparison of wild-type and mutant PS-catalyzed folding of pepsin. The rate of PS-catalyzed folding (<i>k</i>_f) was determined by adding PS to Rp, at pH 5.3, 15°C (see <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101339#pone.0101339.s009" target="_blank">Text S2</a></b>: folding rate followed Arrhenius temp-dependence from 0—15°C, shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101339#pone.0101339.s005" target="_blank">Fig S5</a></b>), and measuring the formation of Np based on enzyme activity measured at pH 1.2, 25°C. The data were fit according to a monoexponential function to obtain <i>k</i>_f. (C) Comparison of wild-type and mutant PS affinity for Rp. PS-Rp binding was determined by following the increase in Trp-fluorescence of pepsin as a function of [PS]. The data were fit according to eq 1 to determine the dissociation constant, <i>K</i>_d, at 20°C, pH 5.3. (d) Comparison of wild-type and mutant PS affinity for Np. The reduction in Np activity was measured as a function of [PS]. The data were fit according to a competitive inhibitor model, eq 2, to determine the inhibition (dissociation) constant, <i>K</i>_i, at 20°C, pH 5.3. All data are reported as the average ± SD of 3-5 measurements for each PS peptide.</p