Department of Pharmaceutical Technology, School of Pharmacy, Greece.
Department of Microbiology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
J Pharm Biomed Anal. 2018 May 30;154:16-22. doi: 10.1016/j.jpba.2018.03.009. Epub 2018 Mar 5.
Since culture-based methods are costly and time consuming, alternative methods are investigated for the quantification of probiotics in commercial products. In this work ATR- FTIR vibration spectroscopy was applied for the differentiation and quantification of live Lactobacillus (La 5) in mixed populations of live and killed La 5, in the absence and in the presence of enteric polymer Eudragit L 100-55. Suspensions of live (La 5_L) and killed in acidic environment bacillus (La 5_K) were prepared and binary mixtures of different percentages were used to grow cell cultures for colony counting and spectral analysis. The increase in the number of colonies with added%La 5_L to the mixture was log-linear (r = 0.926). Differentiation of La 5_L from La 5_K was possible directly from the peak area at 1635 cm (amides of proteins and peptides) and a linear relationship between%La 5_L and peak area in the range 0-95% was obtained. Application of partial least squares regression (PLSR) gave reasonable prediction of%La 5_L (RMSEp = 6.48) in binary mixtures of live and killed La 5 but poor prediction (RMSEp = 11.75) when polymer was added to the La 5 mixture. Application of artificial neural networks (ANNs) improved greatly the predictive ability for%La 5_L both in the absence and in the presence of polymer (RMSEp = 8.11 × 10 for La 5 only mixtures and RMSEp = 8.77 × 10 with added polymer) due to their ability to express in the calibration models more hidden spectral information than PLSR.
由于基于文化的方法成本高且耗时,因此研究了替代方法来定量商业产品中的益生菌。在这项工作中,ATR-FTIR 振动光谱法用于区分和定量活的乳杆菌(La 5)在活的和死的 La 5、在不存在和存在肠聚合物 Eudragit L 100-55 的混合群体中的数量。制备了活的(La 5_L)和在酸性环境中杀死的芽孢杆菌(La 5_K)的悬浮液,并使用不同百分比的二元混合物来生长细胞培养物以进行菌落计数和光谱分析。随着混合物中添加的%La 5_L 的数量增加,菌落数量呈对数线性(r=0.926)。直接从 1635 cm 处的峰面积(蛋白质和肽的酰胺)区分 La 5_L 和 La 5_K,并且在 0-95%的范围内获得了峰面积与%La 5_L 之间的线性关系。应用偏最小二乘回归(PLSR)可以合理地预测活的和死的 La 5 的二元混合物中的%La 5_L(RMSEp=6.48),但当聚合物添加到 La 5 混合物中时,预测能力较差(RMSEp=11.75)。应用人工神经网络(ANNs)大大提高了预测%La 5_L 的能力,无论是在不存在还是存在聚合物的情况下(对于仅含有 La 5 的混合物,RMSEp=8.11×10,对于添加了聚合物的混合物,RMSEp=8.77×10),这是因为它们能够在校准模型中表达比 PLSR 更多的隐藏光谱信息。