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1
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Identification of Lactobacillus strains at the species level using FTIR spectroscopy and artificial neural networks

100%
Dziuba B.
Polish Journal of Food and Nutrition Sciences
|
2007
|
tom 57
|
nr 3
301-306
Fourier-transform infrared (FTIR) spectroscopy and artificial neural networks were used to identify bacteria of the genus Lactobacillus at the species level. A previously developed method for measuring FTIR spectra, and a strategy for their analysis provided the basis for selecting the FTIR spectra of the tested bacteria, and for creating a spectral library, as described elsewhere [Dziuba et al., 2007b]. In our previous study [Dziuba et al., 2007b] we demonstrated that the FTIR spectral characteristics of Lactobacillus strains based exclusively on the differentiation index D, calculated from the Pearson’s correlation coefficient, and cluster analysis are not sufficient to describe the relationships between FTIR spectra and bacteria as molecular systems in a way that would permit their proper identification. Thus, research was launched in which the spectra collected in the above library were used for developing artificial neural networks. The practical value of these networks was verified based on the results of identification of 17 bacterial strains of known taxonomy as well as 7 strains isolated from dairy products and identified on the basis of their taxonomy and biochemical tests. The application of artificial neural networks, i.e. the most advanced chemometric method, to analysis of FTIR spectra enabled correct identification of 93% of bacterial strains of the genus Lactobacillus.
2
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Identyfikacja wybranych gatunków i podgatunków bakterii z rodzaju Lactococcus z zastosowaniem spektroskopii FTIR i sztucznych sieci neuronowych

100%
Dziuba B.
Żywność Nauka Technologia Jakość
|
2012
|
tom 19
|
nr 6
Podjęto badania nad wykorzystaniem spektroskopii w podczerwieni z transformacją Fouriera (FTIR) oraz sztucznych sieci neuronowych do identyfikacji bakterii z rodzaju Lactococcus na poziomie gatunku i podgatunku. Do selekcji widm FTIR badanych szczepów bakterii i poszerzenia biblioteki widm zastosowano, opracowaną wcześniej, własną metodę pomiaru widm FTIR i strategię ich analizy. Badaniami objęto 7 szczepów referencyjnych bakterii fermentacji mlekowej z rodzaju Lactococcus, pochodzących z Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ) oraz 88 szczepów wyizolowanych z produktów żywnościowych. Szczepy bakterii z rodzaju Lactococcus izolowano z mleka surowego i fermentowanych produktów mleczarskich. Zgromadzone w bibliotece widma FTIR badanych szczepów bakterii zostały użyte do opracowania sztucznych sieci neuronowych. Użytkowa wartość sieci neuronowych została ustalona na podstawie wyników identyfikacji szczepów referencyjnych oraz szczepów wyizolowanych z produktów żywnościowych, których przynależność gatunkową określono na podstawie badań PCR z wykorzystaniem specyficznych gatunkowo primerów. Zastosowanie sztucznych sieci neuronowych do analizy widm FTIR umożliwiło w 90 % poprawnie zidentyfikować bakterie z rodzaju Lactococcus, należące do określonego gatunku.
3
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Identification of Propionibacteria to the species level using fourier transform infrared spectroscopy and artificial neural networks

100%
Dziuba B.
Polish Journal of Veterinary Sciences
|
2013
|
tom 16
|
nr 2
Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN’s) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson’s correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN’s) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level.
4

Identyfikacja bakterii fermentacji mlekowej przy wykorzystaniu spektroskopii FTIR i analizy chemometrycznej

100%
Dziuba B.
Przegląd Mleczarski
|
2006
|
nr 12
48-51
5

Identification of selected Leuconostoc species with the use of FTIR spectroscopy and artificial neural networks

100%
Dziuba B.
Acta Scientiarum Polonorum. Technologia Alimentaria
|
2011
|
tom 10
|
nr 3
Background. FTIR spectroscopy is becoming an important tool in the differentiation and identification of bacteria. In the present study, lactic acid bacteria of the genus Leuconostoc were differentiated and identified with the use of Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANNs). The aim of the study was to expand the existing library of FTIR spectra of lactic acid and propionic acid bacteria, and to develop multilayer artificial neural networks as part of the same structure. Material and methods. The material for this study were 10 reference strains of the genus Leuconostoc, and 24 strains isolated from food products. The isolated pure cultures were identified with species specific pairs of primers by PCR technique, as a reference method. Bacterial strain samples were subjected to spectroscopic measurements by the transmission method at a wavelength of 4000 cm'1 to 500 cm'1 using a FTIR spectrophotometer. Digitized spectral data were submitted to neural networks training, until an error of less than 0.05 was obtained and than used for identification of isolates. Results. The utility of neural networks has been determined based on the identification of 10 reference strains and 24 bacterial strains of the genus Leuconostoc isolated from food products. The isolated strains have been identified by PCR-based method using speciesspecific primers. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported the correct identification of 83-92% bacteria of the genus Leuconostoc at the species level. Conclusions. The discussed method may be deployed in analytical laboratories for identifying lactic acid bacteria at the genus, species and subspecies level, for monitoring the purity of cultures in strain collections and for fast screening of selected bacterial groups. FTIR delivers a variety of advantages, including simple technology, low cost, high specificity and a wide range of industrial applications.
6

Motifs with potential physiological activity in food proteins - BIOPEP database

63%
Iwaniak A., Dziuba B.
Acta Scientiarum Polonorum. Technologia Alimentaria
|
2009
|
tom 08
|
nr 3
59-85
Proteins are the multifunctional food components affecting the living organ-isms. One of the proteins function is the impact on the body due to the presence of motifs that show specific physiological and biological activities. Due to the worldwide growth of demand for the food containing bioactive components, increasing attention has been paid recently to the use of bioactive peptides as physiologically active food ingredients. They are important elements of the prevention and treatment of various lifestyle diseases. In addition to its primary function and according to current knowledge, each protein may be a reserve source of peptides controlling the life processes of organisms. For this reason, in this work, application of a new, additional criterion for evaluating proteins as a potential source of biologically active peptides, contributes to a more comprehensive and objective definition of their biological value. A complementary part of such research is the strategy for evaluation of the food proteins as precursors of biologically active peptides which involves the database of proteins and bioactive peptides - BIOPEP (available on-line at: http://www.uwm.edu.pl/biochemia). The database contains information on 2123 peptides representing 48 types of bioactivities, their EC50 values and source of origin. Proteins (706 sequences) are considered as bioactive peptide precursors based on newly introduced criteria: the profile of potential biological activity, the frequency of bioactive fragments occurrence and potential biological protein activity. This original and unprecedented so far approach, started to be successfully and more widely applied by other authors. BIOPEP can be interfaced with global databases such as e.g. TrEMBL, SWISS-PROT, EROP and PepBank. Recently the BIOPEP database was enlarged with the data about allergenic proteins, including information about structure of their epitopes and molecular markers
7

Milk proteines-derived bioactive peptides in dairy products: molecular, biological and methodological aspects

63%
Dziuba B., Dziuba M.
Acta Scientiarum Polonorum. Technologia Alimentaria
|
2014
|
tom 13
|
nr 1
Proteins are one of the primary components of the food, both in terms of nutrition and function. They are main source of amino acids, essential for synthesis of proteins, and also source of energy. Additionally, many proteins exhibit specific biological activities, which may have effect on functional or pro-health properties of food products. These proteins and their hydrolysis products, peptides, may influence the properties of food and human organism. The number of commercially available food products containing bioactive peptides is very low, apart from that milk proteins are their rich source. It could be supposed that number of available products with declared activity will rise in near future because of observed strong uptrend on interest in such products. Molecular and biological properties of milk proteins, as precursors of bioactive peptides was characterised in the work. Therefore, the strategy of research and obtaining of such peptides both in laboralory and industrial scale, as well as the range of their commercial application, was presented. Several examples of research efforts presenting high potential to develop new products containing bioactive peptides from milk proteins and predetermined as nutraceuticals was described.
8
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Analiza in silico peptydów immunoaktywnych pochodzących z białek żywności - badania z wykorzystaniem bazy BIOPEP

51%
Dziuba M., Nalecz D., Dziuba B.
Żywność Nauka Technologia Jakość
|
2014
|
tom 21
|
nr 5
Analizie bioinformatycznej poddano 89 peptydów immunoaktywnych dostępnych w bazie BIOPEP. Obecność fragmentów potencjalnie immunoaktywnych stwierdzono w 90 ze 150 sekwencji analizowanych białek. Sekwencje aminokwasowe badanych peptydów analizowano z uwzględnieniem: długości łańcucha, udziału procentowego poszczególnych aminokwasów, pI, molowego współczynnika ekstynkcji, indeksu hydropatii i ładunku wypadkowego. Ponadto określono możliwości ich uwalniania in silico przez enzymy proteolityczne. Na podstawie komputerowej analizy z zastosowaniem programu ProtParam stwierdzono, że immunoaktywne peptydy to głównie fragmenty hydrofilowe, w sekwencji których przeważają takie aminokwasy, jak: Lys, Arg i Pro, obdarzone ładunkiem dodatnim w neutralnym pH. W komputerowej symulacji proteolizy in silico wybranych 11 białek żywności o największej częstości występowania peptydów immunoaktywnych (parametr A > 0,02) wykazano, że tylko trzy enzymy: chymaza (EC 3.4.21.39), elastaza trzustkowa (EC 3.4.21.36) i endopeptydaza glicylowa (EC 3.4.22.25), spośród dostępnych w bazie BIOPEP, wykazywały specyficzność pozwalającą na uwalnianie peptydów immunoaktywnych w układzie jednego enzymu. Produktami hydrolizy analizowanych białek, przy użyciu wybranych enzymów proteolitycznych, były głównie 2 - 3 aminokwasowe fragmenty peptydowe.
9

Milk proteins as precursors of bioactive peptides

51%
Dziuba M., Dziuba B., Iwaniak A.
Acta Scientiarum Polonorum. Technologia Alimentaria
|
2009
|
tom 08
|
nr 1
71-90
Milk proteins, a source of bioactive peptides, are the subject of numerous research studies aiming to, among others, evaluate their properties as precursors of biologi-cally active peptides. Physiologically active peptides released from their precursors may interact with selected receptors and affect the overall condition and health of humans. By relying on the BIOPEP database of proteins and bioactive peptides, developed by the Department of Food Biochemistry at the University of Warmia and Mazury in Olsztyn (www.uwm.edu.pl/biochemia), the profiles of potential activity of milk proteins were determined and the function of those proteins as bioactive peptide precursors was evaluated based on a quantitative criterion, i.e. the occurrence frequency of bioactive fragments (A). The study revealed that milk proteins are mainly a source of peptides with the following types of activity: antihypertensive (Amax = 0.225), immunomodulating (0.024), smooth muscle contracting (0.011), antioxidative (0.029), dipeptidyl peptidase IV inhibitors (0.148), opioid (0.073), opioid antagonistic (0.053), bonding and transporting metals and metal ions (0.024), antibacterial and antiviral (0.024), and antithrombotic (0.029). The enzymes capable of releasing bioactive peptides from precursor proteins were determined for every type of activity. The results of the experiment indicate that milk proteins such as lactoferrin, α-lactalbumin, β-casein and κ-casein hydrolysed by trypsin can be a relatively abundant source of biologically active peptides.
10

Effect of ethanol stress on Lactobacilli

51%
Dziuba B., Babuchowski B., Niklewicz M.
Polish Journal of Food and Nutrition Sciences
|
2000
|
tom 09/50
|
nr 4
11

Bacteroides spp. – znaczenie kliniczne, lekooporność i metody jej oznaczania

45%
Niestepski S., Harnisz M., Korzeniewska E., Osinska A., Dziuba B.
Postępy Mikrobiologii
|
2017
|
tom 56
|
nr 1
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