Monitoring of water spectral patterns of lactobacilli development as a tool for rapid selection of probiotic candidates

Abstract

Nowadays a quick and inexpensive method, which allows rapid, in vivo comprehensive probiotic bacteria identification, is needed. To elucidate a new concept to evaluate probiotic bacteria, near infrared spectroscopy with aquaphotomics were applied to monitor the growth of eight Lactobacillus bulgaricus and one Lactobacillus gasseri bacteria strains. Their resistance to low pH (1.8) in the presence of pepsin and bile were measured and further used as reference data for analysis of the simultaneously acquired spectral data. The acquired spectral data in the region of 1100–1300 nm were subjected to various methods for multivariate data analyses—principal component analysis, linear discriminant analysis, soft independent modeling of class analogy, and partial least squares regression. The results showed high accuracy of bacteria strains classification according to their resistance and the potential of the tested wavelength region for rapid selection and prediction of some basic phenotypic characteristics of probiotic candidates. Results of the current study also revealed different suitability of each growth stage when using near infrared spectra for the classification of bacteria strains.

Keywords

Probiotics aquaphotomics near infrared spectroscopy near infrared chemometrics lactic acid bacteria

Get full access to this article

View all access options for this article.

References

Parvez

Malik

Ah Kang

et al.

Probiotics and their fermented food products are beneficial for health. J Appl Microbiol 2006; 100: 1171–1185.

Kalliomäki

Isolauri

. Probiotics and down-regulation of the allergic response. Immunol Allergy Clin North Am 2004; 24: 739–52,viii.

Isolauri

. Dietary modification of atopic disease: use of probiotics in the prevention of atopic dermatitis. Curr Allergy Asthma Rep 2004; 4: 270–275.

McFarland

. Meta-analysis of probiotics for the prevention of traveler’s diarrhea. Travel Med Infect Dis 2007; 5: 97–105.

Rastall

Gibson

Gill

et al.

Modulation of the microbial ecology of the human colon by probiotics, prebiotics and synbiotics to enhance human health: an overview of enabling science and potential applications. FEMS Microbiol Ecol 2005; 52: 145–152.

Mack

Michail

Wei

et al.

Probiotics inhibit enteropathogenic E. coli adherence in vitro by inducing intestinal mucin gene expression. Am J Physiol Gastrointest Liver Physiol 1999; 276: G941–G950.

Miraglia del Giudice

De Luca

. The role of probiotics in the clinical management of food allergy and atopic dermatitis. J Clin Gastroenterol 2004; 38: S84–S85.

Walker

. Role of nutrients and bacterial colonization in the development of intestinal host defense. J Pediatr Gastroenterol Nutr 2000; 30(Suppl 2): S2–S7.

Pessi

Sutas

Hurme

et al.

Interleukin-10 generation in atopic children following oral Lactobacillus rhamnosus GG. Clin Exp Allergy 2000; 30: 1804–1808.

10.

Marteau

Flourie

Pochart

et al.

Effect of the microbial lactase (EC 3.2.1.23) activity in yoghurt on the intestinal absorption of lactose: an in vivo study in lactase-deficient humans. Br J Nutr 2007; 64: 71.

11.

Slavchev

Kovacs

Koshiba

et al.

Monitoring of water spectral pattern reveals differences in probiotics growth when used for rapid bacteria selection. PLoS One 2015; 10: e0130698.

12.

Zotta

Asterinou

Rossano

et al.

Effect of inactivation of stress response regulators on the growth and survival of Streptococcus thermophilus Sfi39. Int J Food Microbiol 2009; 129: 211–220.

13.

Pitino

Randazzo

Mandalari

et al.

Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract. Food Microbiol 2010; 27: 1121–1127.

14.

Savitzky

Golay

MJE

. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 1964; 36: 1627–1639.

15.

Naes

Isaksson

Fearn

et al.

A user friendly guide to multivariate calibration and classification, Chichester, UK: NIR Publications, 2002.

16.

Cowe

McNicol

. The use of principal components in the analysis of near-infrared spectra. Appl Spectrosc 1985; 39: 257–266.

17.

Berrueta

Alonso-Salces

Héberger

. Supervised pattern recognition in food analysis. J Chromatogr A 2007; 1158: 196–214.

18.

Kinoshita

Miyazaki

Morita

et al.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda. Sci Rep 2012; 2: 856.

19.

De Angelis

Gobbetti

. Environmental stress responses in Lactobacillus: a review. Proteomics 2004; 4: 106–122.

20.

Madigan M, Clark D, Stahl D, et al. Brock biology of microorganisms. 13th ed. 2010. Benjamin Cummings.

21.

Perni

Andrew

Shama

. Estimating the maximum growth rate from microbial growth curves: definition is everything. Food Microbiol 2005; 22: 491–495.

22.

Mytilinaios

Salih

Schofield

et al.

Growth curve prediction from optical density data. Int J Food Microbiol 2012; 154: 169–176.

23.

Widdel

. Theory and measurement of bacterial growth. Di dalam Grundpraktikum Mikrobiol 2007, pp. 1–11.

24.

Headrick

Diken

Walters

et al.

Spectral signatures of hydrated proton vibrations in water clusters. Science 2005; 308: 1765–1769.

25.

Siesler HW, Ozaki Y, Kawata S, et al. (eds) Wiley: near-infrared spectroscopy: principles, instruments, applications. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2001. Epub ahead of print November 2001. DOI: 10.1002/9783527612666.