Electrospun probiotics: an alternative for encapsulation.

Electrospinning has emerged as a potential method to fabricate nonwoven nanofibers. It has application in different fields of biomedicine as it has potential to carry antimicrobial and bioactive agents. The present investigation was conducted to optimize the process conditions and determine the viability of probiotics after being electrospun in fibers. Poly(vinyl alcohol) (PVA) was utilized as electrospun material because it possesses generally recognized as safe (GRAS) status and in dry form it acts as a high oxygen barrier and has high water solubility. This characteristic allows the easy recovery of the bacteria from electrospun fibers. The viability tests, carried out at three different temperatures (room temperature, 4°C and -20°C) showed Bifidobacterium animalis subsp. Lactis Bb12 (probiotic 1) and combination of Streptococcus thermophilus (TH-4®), Lactobacillus paracasei 431® and Bb-12 (probiotic 2) within the electrospun PVOH fibers remained viable after 1 week at room temperature and refrigeration temperature. The nanofibers containing probiotics prepared with 9% poly venyl alcohol showed homogenous, uniform, bead-free and smooth texture. Probiotic 1 demonstrated growth as 1.85×108, 1.57×108 and 1.71×108 before, 0 hour and after 1 week of encapsulation. While probiotic 2 exhibited a growth of 2.1×108 before electrospinning, 1.3 ×108 at 0 hour and 1.97×108 after one week of electrospinning. There was no change in CFU/mL count and remained 108 CFU/mL. The encapsulation efficiency was 84.07% and 85.73% at 0 and one week, respectively, for Probiotic 1, while probitic 2 showed 90.09% and 93.59 % encapsulation efficiency before and after one week, respectively. Considering the prolonged viability of nanofibers containing probiotics noted at room temperature, this technology can be implemented for prolonged viability of probiotics.

Measurement of the attachment and assembly of small amyloid-ß oligomers on live cell membranes at physiological concentrations using single-molecule tools.

It is thought that the pathological cascade in Alzheimer's disease is initiated by the formation of amyloid-ß (Aß) peptide complexes on cell membranes. However, there is considerable debate about the nature of these complexes and the type of solution-phase Aß aggregates that may contribute to their formation. Also, it is yet to be shown that Aß attaches strongly to living cell membranes, and that this can happen at low, physiologically relevant Aß concentrations. Here, we simultaneously measure the aggregate size and fluorescence lifetime of fluorescently labeled Aß(1-40) on and above the membrane of cultured PC12 cells at near-physiological concentrations. We find that at 350 nM Aß concentration, large (>>10 nm average hydrodynamic radius) assemblies of codiffusing, membrane-attached Aß molecules appear on the cell membrane together with a near-monomeric species. When the extracellular concentration is 150 nM, the membrane contains only the smaller species, but with a similar degree of attachment. At both concentrations, the extracellular solution contains only small (∼2.3 nm average hydrodynamic radius) Aß oligomers or monomers. We conclude that at near-physiological concentrations only the small oligomeric Aß species are relevant, they are capable of attaching to the cell membrane, and they assemble in situ to form much larger complexes.

Detection and differentiation of live and heat-treated Salmonella enterica serovars inoculated onto chicken breast using Fourier transform infrared (FT-IR) spectroscopy.

AIMS: To evaluate Fourier transform infrared (FT-IR) techniques for detecting, quantifying, and differentiating viable and heat-treated cells of Salmonella enterica serovars from chicken breast. METHODS AND RESULTS: Salmonella enterica serovars were captured from inoculated chicken breast by filtration and immunomagnetic separation (IMS) prior to spectral collection using an FT-IR spectrometer and IR microscopy. The detection limits, based on amide II peak area (1589 to 1493 cm(-1) ), for the Filtration-FT-IR and IMS-FT-IR methods were 10(6) and 10(4) CFU g(-1) , respectively. The bacteria were detectable after 6 h of culture enrichment during a sensitivity experiment with lower initial inoculum of 10(1) CFU g(-1) . Canonical variate analysis differentiated experimental from control spectra at a level of 10(3) CFU g(-1) . Partial least squares models were established for the quantification of Salm. enterica from chicken breast using Filtration-FT-IR (R(2) ≥ 0·95, RMSEC ≤ 0·62) and IMS-FT-IR (R(2) ≥ 0·80, RMSEC ≤ 1·61) methods. Filtration-FT-IR was also used to detect and quantify live Salm. enterica in the presence of heat-treated cells with R(2) = 0·996, and this approach was comparable to the results of a commercial stain (BacLight™; R(2) = 0·998). Discriminant and canonical variate analyses of the spectra differentiated live and dead cells of different serovars of Salm. enterica. CONCLUSIONS: FT-IR analysis coupled with separation methods is useful for the rapid detection and differentiation of Salm. enterica separated from chicken. SIGNIFICANCE AND IMPACT OF THE STUDY: FT-IR-based methods are faster than traditional microbiological methods and can be used for the detection of live and dead bacteria from complex foods.

Inactivation of Staphylococcus aureus in milk using flow-through pulsed UV-light treatment system.

This study investigated the efficacy of pulsed UV-light for continuous-flow milk treatment for the inactivation of Staphylococcus aureus, a pathogenic microorganism frequently associated with milk safety concerns. Pulsed UV light is an emerging technology, which can be used for the inactivation of this pathogen in milk in a relatively short time. Pulsed UV light damages the DNA of the bacteria by forming thymine dimers that lead to bacterial death. The effect of sample distance from the quartz window of the UV-light source, number of passes, and flow rate was investigated. A response surface methodology was used for the design and analysis of experiments. Milk was treated at 5-, 8-, or 11-cm distance from a UV-light strobe at 20, 30, or 40 mL/min flow rate and treated up to 3 times by recirculation of milk to assess the effect of the number of passes on inactivation efficiency. Log10 reductions varied from 0.55- to 7.26-log10 CFU/mL. Complete inactivation was obtained in 2 cases and no growth was observed following an enrichment protocol. Predicted results were in agreement with the experimental data. Overall, this work demonstrates that pulsed UV-light has a potential for inactivation of milk pathogens.

Spectroscopic evaluation of the surface quality of apple.

Different spectroscopic techniques based on infrared and Raman were used to evaluate the natural wax and related surface quality of apple fruit. Transmission near-infrared (NIR) spectroscopy was applied to solutions of single wax components and extracted apple wax. Fourier transform infrared (FTIR) spectroscopy was used for transmission measurements of wax films on NaCl crystals, diffuse reflectance spectroscopy (DRIFTS) was used to analyze wax powders, and FT-Raman spectroscopy was explored to examine intact wax layers on whole fruit. The natural wax layers of apple fruit from a maximum of three different cultivars (Jonagold, Jonagored, and Elshof) from three picking dates (early, commercial, and late), three controlled atmosphere storage durations (0, 4, and 8 months), and three shelf life periods (0, 1, and 2 weeks) within each storage duration were examined. Canonical discriminant analysis was carried out on the first derivative NIR and FTIR spectra to describe the information contained in the spectra. Discrimination between cultivars and between storage duration based on wax layer properties was achieved with reasonable accuracy from both of the techniques. Information contained in the spectra of apples from different picking dates and shelf life periods was not significant. Differences between cultivars and storage periods in this analysis mostly related to differences in the number of aliphatic chains (e.g., alkanes and esters) and the presence of alpha-farnesene. No satisfactory results were obtained by means of Raman spectroscopy and DRIFTS.

Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy.

The feasibility of measuring tetracycline at the ppb levels in milk was investigated by Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopic techniques. Milk samples spiked with different concentrations of tetracycline were scanned using FT-MIR and FT-NIR spectroscopy. Suitable spectral wave number regions were selected for principal least square (PLS) regression models development. Prediction errors were high when the calibration model was developed using the wide range of tetracycline concentrations (4 to 2000 ppb) in milk. Maximum correlation coefficient (R2) value of about 0.89 was obtained for the validation models developed using different concentration ranges. Prediction errors were high for FT-NIR method. Results indicated that FT-MIR spectroscopy could be used for rapid detection of tetracycline hydrochloride residues in milk.

Monitoring a bioprocess for ethanol production using FT-MIR and FT-Raman spectroscopy.

The application of Fourier transform mid-infrared (FT-MIR) spectroscopy and Fourier transform Raman (FT-Raman) spectroscopy for process and quality control of fermentative production of ethanol was investigated. FT-MIR and FT-Raman spectroscopy along with multivariate techniques were used to determine simultaneously glucose, ethanol, and optical cell density of Saccharomyces cerevisiae during ethanol fermentation. Spectroscopic measurement of glucose and ethanol were compared and validated with the high-performance liquid chromatography (HPLC) method. Spectral wave number regions were selected for partial least-squares (PLS) regression and principal component regression (PCR) and calibration models for glucose, ethanol, and optical cell density were developed for culture samples. Correlation coefficient (R(2)) value for the prediction for glucose and ethanol was more than 0.9 using various calibration methods. The standard error of prediction for the PLS first-derivative calibration models for glucose, ethanol, and optical cell density were 1.938 g/l, 1.150 g/l, and 0.507, respectively. Prediction errors were high with FT-Raman because the Raman scattering of the cultures was weak. Results indicated that FT-MIR spectroscopy could be used for rapid detection of glucose, ethanol, and optical cell density in S. cerevisiae culture during ethanol fermentation.

Examination of full fat and reduced fat Cheddar cheese during ripening by Fourier transform infrared spectroscopy.

The Fourier transform infrared spectra of reduced fat Cheddar cheese and full fat Cheddar cheese during ripening were examined. A suitable sampling procedure was used to obtain the spectra of cheese samples. The spectra were affected by the moisture contents of the samples, but this effect could be eliminated by equilibration of the sample for at least 10 min before data collection. Strong and well-separated bands at 1744, 1450, 1240, 1170, and 1115 cm-1 arising from fat were observed in the frozen samples. The bands observed at 1650 and 1540 cm-1 were attributed to the protein contents of the cheese samples. The absorption intensities of protein and fat bands corresponded to the amounts of protein and fat that were present in the samples. Distinct changes in the bands of fat and protein of reduced fat and full fat cheese samples occurred during ripening. This technique could provide a basis for a rapid characterization and determination of the age of cheese undergoing ripening.

Application of a multi-structure neural network (MSNN) to sorting pistachio nuts.

A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptors of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.