Quantification of live Lactobacillus acidophilus in mixed populations of live and killed by application of attenuated reflection Fourier transform infrared spectroscopy combined with chemometrics
Introduction
Probiotics are traditionally used in dairy products and as therapeutic supplements to confer health benefits to the host. They have been shown to help in the prevention of gastrointestinal disorders and improvement of the immunological system by reinforcing the epithelial barrier of the gastrointestinal tract [1]. In the recent years, their administration as supplementary therapy has become common in cases of long term treatment with antibiotics that causes damage to the GI mucosa by altering the balance of the microflora. Lactobacillus acidophilus is the main probiotic administered orally in doses of usually 109 colony forming units (CFU) twice daily [2].
Probiotics can be formulated into many different types of consumer products including foods, drugs and dietary supplements [3]. Loss of viability during processing into consumer products and storage could occur due to exposure to unfavorable conditions such as high compaction pressures during tableting, high temperature and humidity [3,4] or acidic environments [4]. Also, considerable loss of viability due to exposure to acidic pH after oral administration has been reported [5,6]. Therefore, an analytical method for their quantification is important.
So far, the classical culture-based plate counting microbiological method is practiced. It is reliable and allows direct observation and measurement of the CFUs for microbes that are capable of replicating under experimental conditions. However, it has certain drawbacks: it is costly due to needed consumables, requires long incubation periods, has relatively low precision and it can be difficult to automate. Besides, it does not provide information about the presence of dead bacteria and may underestimate sublethally damaged cells present in the sample [7]. The limitations of the CFUs were recently revised in the USP General Informational Chapter 〈1223〉 (Validation of Alternative Microbiological Methods, Reference December 1, 2015), where it was stated that it may not be a gold standard for validation when there are many signals available other than CFUs for the detection, enumeration and identification of microorganisms in water, air, pharmaceutical ingredients and drug products [8].
For these reasons there is upcoming interest in rapid microbiological methods (RMMs) which are summarized below. Light scattering methods have been used for the detection of pathogens in food and water [9]. However, they cannot differentiate between live and dead microorganisms, and hence do not provide viability data [10]. Adenosine triphosphate (ATP) quantification based bioluminescence method utilizing the enzymatic reaction between (ATP) with luciferase was applied for the estimation of bacterial cell viability. The light that is emitted during the reaction was measured with a luminometer and was proportional to the ATP in the sample [11]. Direct counting of microbial cells stained with a fluorescent specific dye has been proposed for quantifying viable cryptococci in mixtures of live and heat-killed [12]. However, the direct counting methods give higher estimations than plate counting, which is partly due to the presence of viable but non-culturable (VBNC) microorganisms and partly due to the inability of the method to distinguish between the live and the dead microorganisms [10]. Furthermore, a number of RMMs based on polymerase chain reaction technology have been proposed. The main obstacle to their application is the need for special reagents which are often specific to the instrument used [13].
From the short account above, it appears that the current RMMs require expensive and specific instruments and very specialized techniques which limit their applicability. On the other hand, Fourier transform infrared spectroscopy (FTIR) is a simple, low cost and rapid method which in conjunction with chemometrics enables examination and differentiation of bacteria in the solid or liquid state [14]. In particular, Oberreuter et al. [15] applied FTIR spectroscopy with PLSR to determine ratios of different microorganisms in mixtures. Oust et al. [16] identified Lactobacillus at the species level using FTIR in combination with PLSR. Dziuba et al. [17], applied FTIR and cluster analysis to differentiate and identify lactic acid bacteria at the genus and species level. Dziuba and Nalepa [18] identified lactic acid and propionic acid bacteria using FTIR spectroscopy and artificial neural networks (ANNs). Davis et al. [7] differentiated live and dead E.coli O157:H7 using FTIR in combination with PLSR and canonical variate analysis (CVA).
Therefore, since FTIR is strain specific and can reveal characteristic features of cellular components such as fatty acids, proteins, polysaccharides and nucleic acids [17,18] it will be applied in the present work for the first time, in order to compare, discriminate and quantitatively estimate live Lactobacillus acidophilus in mixtures of live and killed Lactobacillus. Since discrimination and quantification may be obscured by the presence of numerous peaks arising from the many chemical substances and their possible interactions, multivariate calibration methods in conjunction with FTIR will be applied [[14], [15], [16], [17], [18]]. Probiotics are usually administered in formulations together with polymers for protection from the acidic gastric environment. Therefore, the quantification will be performed in the absence and in the presence of poly(methacrylic acid, ethyl acrylate 1:1) polymer.
PLSR will be applied as classical chemometrics and predictive models will be developed of live Lactobacillus acidophilus in mixed populations of live and killed using different sets of standard samples for internal calibration and external validation. However, since PLSR may not be able to model data having non-linear characteristics, artificial neural networks (ANNs) will also be used as a more advanced multivariate analysis method. ANNs can be trained directly from the experimental data and have the ability to model both linear and non-linear relationships [19]. The linearity and predictive ability of the developed models will be evaluated using correlation coefficient, root mean square error of cross validation (RMSEcv) and root mean square error of prediction (RMSEp).
Section snippets
Materials
Lactobacillus acidophilus (La 5) was gift from Chr-Hansen Hellas (Athens, Greece) and was stored at −80 °C before use. La 5 was grown (1% w/v) in deMann Ragosa and Sharpe (MRS) broth for 24 h at 37 °C. Eudragit® L100-55 (Poly(methacrylic acid ethyl acrylate 1:1)) was gift from Evonic Industries AG (Essen, Germany). All reagents used were of analytical grade.
Preparation of suspensions of live and killed Lactobacillus and their mixtures
The culture of La 5_L in MRS broth was spun at 1500 rpm for 10 min and the resulting pellet re-suspended in 10% skimmed milk, frozen for
Application of cell cultures for the quantification of live Lactobacillus in mixed populations of live and killed
Petri dishes prepared using 6 percentages of La 5_L (0%, 20%, 40%, 60%, 80% and 100%) were examined (Supplementary Fig. 1). The efficacy of the acidic treatment for the preparation of killed La 5 was verified by the complete absence of colonies on the 0% Petri dish. On the other hand, distinct colonies were obtained for mixtures 20% to 100% confirming correct choice of experimental conditions and the ability of live La 5 to replicate. From the CFUs counted the total number of viable La 5 in the
Conclusions
Correct dosing of Lactobacillus in dosage forms and protection during passage through the stomach where acidic conditions prevail are critical for the efficacy of administration. For these reasons quantitative estimation of live Lactobacillus during processing and in the final dosage form, as well as during dissolution testing in various media is important. ATR-FTIR spectroscopy could directly discriminate live La 5 in mixed populations of live and killed from differences in peak intensities at
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