Methodology for real-time,multianalyte monitoring of fermentations using an in-situ mid-infrared sensor

An in-situ, mid-infrared sensor was used to monitor the major analyte concentrations involved in the cultivation of Gluconacetobacter xylinus and the production of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concentrations, three different sets of standard spectra were used to develop calibration models, applying partial least-squares regression. It was possible to build a valid calibration model to predict the 700 spectra collected during the complete time course of the cultivation, using only 12 spectra collected every 10 h as standards. This model was used to reprocess the concentration profiles from 0 to 15 g/L of nine different analytes with a mean standard error of validation of 0.23 g/L. However, this calibration model was not suitable for real-time monitoring as it was probably based on non-specific spectral features, which were correlated only with the measured analyte concentrations. Valid calibration models capable of real-time monitoring could be established by supplementing the set of 12 fermentation spectra with 42 standards of measured analytes. A pulse of 5 g/L ethanol showed the robustness of the model to sudden disturbances. The prediction of the models drifted, however, toward the end of the fermentation. The most robust calibration model was finally obtained by the addition of 34 standard spectra of non-measured analytes. Although the spectra did not contain analyte-specific information, it was believed that this addition would increase the variability space of the calibration model. Therefore, an expanded calibration model containing 88 spectra was used to monitor, in real time, the concentration profiles of fructose, acetic acid, ethanol and gluconacetan and allowed standard errors of prediction of 1.11, 0.37, 0.22, and 0.79 g/L, respectively.     

In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed‐batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off‐line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. (c) 1994 John Wiley & Sons, Inc.     

In vivo prediction of the nutrient status of individual microalgal cells using Raman microspectroscopy

An in vivo method for predicting the nutrient status of individual algal cells using Raman microspectroscopy is described. Raman spectra of cells using 780 nm laser excitation show enhanced bands mainly attributable to chlorophyll a and beta-carotene. The relative intensities of chlorophyll a and beta-carotene bands changed under nitrogen limitation, with chlorophyll a bands becoming less intense and beta-carotene bands more prominent. Although spectra from N-replete and N-starved cell populations varied, each distribution was distinct enough such that multivariate classification methods, such as partial least squares discriminant analysis, could accurately predict the nutrient status of the cells from the Raman spectral data.     

Treatment of missing values for multivariate statistical analysis of gel-based proteomics data

The presence of missing values in gel-based proteomics data represents a real challenge if an objective statistical analysis is pursued. Different methods to handle missing values were evaluated and their influence is discussed on the selection of important proteins through multivariate techniques. The evaluated methods consisted of directly dealing with them during the multivariate analysis with the nonlinear estimation by iterative partial least squares (NIPALS) algorithm or imputing them by using either k-nearest neighbor or Bayesian principal component analysis (BPCA) before carrying out the multivariate analysis. These techniques were applied to data obtained from gels stained with classical postrunning dyes and from DIGE gels. Before applying the multivariate techniques, the normality and homoscedasticity assumptions on which parametric tests are based on were tested in order to perform a sound statistical analysis. From the three tested methods to handle missing values in our datasets, BPCA imputation of missing values showed to be the most consistent method.     

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Determinations of fluoroquinolones and nonsteroidal anti-inflammatory drugs in urine by extractive spectrophotometry and photoinduced spectrofluorimetry using multivariate calibration

Multivariate calibration methods are chemometric tools that may be applied to the analysis of spectroscopic data with multichannel detection. Two procedures, based on spectrophotometric and fluorimetric signals, are reported for the simultaneous determination of two fluoroquinolones (ciprofloxacin and ofloxacin) and two nonsteroidal anti-inflammatory drugs (diclofenac and mefenamic acid) using first- and second-order multivariate calibration methods. In the spectrophotometric method, an extractive procedure into chloroform using trioctylmethylammonium chloride-adogen as counter ion was optimized, with the object of extracting the analytes from urine samples and eliminating matrix interferences. After separation, the absorption spectrum of the organic phase was used as the analytical signal in a partial least squares method. A photoinduced spectrofluorimetric (PIF) method using excitation-emission fluorescence matrices, is proposed, to apply three-way chemometric calibration, with the aim of analyzing ofloxacin, ciprofloxacin, and diclofenac in urine samples without the previous extractive sample-cleaning step. For both procedures, recoveries around 100% were found for all the analytes. However, the PIF three-way chemometric method provides the most sensitive and selective procedure as the urine interferences are modulated using the three-way chemometric technique.     

Feasibility study using surface-enhanced Raman spectroscopy for the quantitative detection of tyrosine and serine phosphorylation

We investigate the feasibility of colloid-based surface enhanced Raman scattering (SERS) as a highly sensitive technique for detecting peptide phosphorylation at serine and tyrosine residues. Using the recently reported drop-coating deposition Raman method we validate our SERS spectra against normal Raman spectra that would otherwise be unobtainable at such low concentrations. Compared with existing techniques for quantifying peptide phosphorylation, such as high-performance liquid chromatography (HPLC), the short scanning and processing time associated with SERS makes it an attractive alternative for near-real-time measurement at sub micro-molar concentrations. Following pre-processing by Savistky-Golay second derivative (SGSD), the degree of phosphorylation of synthetic peptides is determined using multivariate spectral classification, interval partial least squares (iPLS). Furthermore, our results show that the technique is robust to interference from complex proteins and other phosphorylated compounds present at concentrations typically found in a screening assay.     

Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

Process analytical technology has elevated the role of sensors in pharmaceutical manufacturing. Often the ideal technology must be selected from many suitable candidates based on limited data. Net analyte signal (NAS) theory provides an effective platform for method characterization based on multivariate figures of merit (FOM). The objective of this work was to demonstrate that these tools can be used to characterize the performance of 2 dissimilar analyzers based on different underlying spectroscopic principles for the analysis of pharmaceutical compacts. A fully balanced, 4-constituent mixture design composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and starch was generated; it consisted of 29 design points. Six 13-mm tablets were produced from each mixture at 5 compaction levels and were analyzed by near-infrared and Raman spectroscopy. Partial least squares regression and NAS analyses were performed for each component, which allowed for the computation of FOM. Based on the calibration error statistics, both instruments were capable of accurately modeling all constituents. The results of this work indicate that these statistical tools are a suitable platform for comparing dissimilar analyzers and illustrate the complexity of technology selection.     

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media.

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.     

The first step of hen egg white lysozyme fibrillation, irreversible partial unfolding, is a two-state transition

Amyloid fibril depositions are associated with many neurodegenerative diseases as well as amyloidosis. The detailed molecular mechanism of fibrillation is still far from complete understanding. In our previous study of in vitro fibrillation of hen egg white lysozyme, an irreversible partially unfolded intermediate was characterized. A similarity of unfolding kinetics found for the secondary and tertiary structure of lysozyme using deep UV resonance Raman (DUVRR) and tryptophan fluorescence spectroscopy leads to a hypothesis that the unfolding might be a two-state transition. In this study, chemometric analysis, including abstract factor analysis (AFA), target factor analysis (TFA), evolving factor analysis (EFA), multivariate curve resolution-alternating least squares (ALS), and genetic algorithm, was employed to verify that only two principal components contribute to the DUVRR and fluorescence spectra of soluble fraction of lysozyme during the fibrillation process. However, a definite conclusion on the number of conformers cannot be made based solely on the above spectroscopic data although chemometric analysis suggested the existence of two principal components. Therefore, electrospray ionization mass spectrometry (ESI-MS) was also utilized to address the hypothesis. The protein ion charge state distribution (CSD) envelopes of the incubated lysozyme were well fitted with two principal components. Based on the above analysis, the partial unfolding of lysozyme during in vitro fibrillation was characterized quantitatively and proven to be a two-state transition. The combination of ESI-MS and Raman and fluorescence spectroscopies with advanced statistical analysis was demonstrated to be a powerful methodology for studying protein structural transformations.     

In situ near infrared spectroscopy for analyte‐specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations

There are many challenges associated with in situ collection of near infrared (NIR) spectra in a fermentation broth, particularly for highly aerated and agitated fermentations with filamentous organisms. In this study, antibiotic fermentation by the filamentous bacterium Streptomyces coelicolor was used as a model process. Partial least squares (PLS) regression models were calibrated for glucose and ammonium based on NIR spectra collected in situ. To ensure that the models were calibrated based on analyte‐specific information, semisynthetic samples were used for model calibration in addition to data from standard batches. Thereby, part of the inherent correlation between the analytes could be eliminated. The set of semisynthetic samples were generated from fermentation broth from five separate fermentations to which different amounts of glucose, ammonium, and biomass were added. This method has previously been used off line but never before in situ. The use of semisynthetic samples along with validation on an independent batch provided a critical and realistic evaluation of analyte‐specific models based on in situ NIR spectroscopy. The prediction of glucose was highly satisfactory resulting in a RMSEP of 1.1 g/L. The prediction of ammonium based on NIR spectra collected in situ was not satisfactory. A comparison with models calibrated based on NIR spectra collected off line suggested that this is caused by signal attenuation in the optical fibers in the region above 2,000 nm; a region which contains important absorption bands for ammonium. For improved predictions of ammonium in situ, it is suggested to focus efforts on enhancing the signal in that particular region. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Verification of performance with the automated direct optical TIRF immunosensor (River Analyser) in single and multi-analyte assays with real water samples

In order to verify the reproducibility, precision, and robustness of the optical immunosensor River Analyser (RIANA), we investigated two common statistical methods to evaluate the limit of detection (LOD) and the limit of quantification (LOQ). Therefore, we performed a simultaneous multi-analyte calibration with atrazine, bisphenol A, and estrone in Milli-Q water. Using an automated biosensor, it was possible for the first time to achieve a LOD below 0.020 microg L(-1) using a common statistically based method without sample pre-treatment and pre-concentration for each of the analytes in a simultaneous multi-analyte calibration. This biosensor setup shows values comparable to those obtained by more classical analytical methods. Based on this calibration, we measured spiked and un-spiked real water samples with complex matrices (samples from different water bodies, from ground water sources, and tap water samples). The comparison between our River Analyser and common analytical methods (like GC-MS and HPLC-DAD) shows overall comparable values for all three analytes. Furthermore, a calibration of isoproturon (IPU) (in single analyte mode) resulted in a LOD of 0.016 microg L(-1), and a LOQ of 0.091 microg L(-1). In compliance with guidelines of the Association of Analytical Communities International (AOAC), six out of nine recovery rates (recovery rate: measured concentration divided by real concentration in percent) for three surface water samples with different matrices (spiked and un-spiked) could be obtained between 70 and 120% (recovery rates between 70 and 120%, as demanded by the guidelines of the AOAC International). The reproducibility was checked by measuring replica of each sample within independent repetitions. Robustness could be demonstrated by long-term stability tests of the biosensor surface. These studies show that the biosensor used offers the necessary reproducibility, precision, and robustness required for an analytical method.     

Quantitative determination of concentration profiles of skin components and topically applied oils by tailored multivariate curve resolution-alternating least squares using in vivo confocal Raman micro-spectroscopy

The main components of the stratum corneum (SC), water, lipids, and proteins, are non-homogeneously distributed throughout the depth. The quantitative determination of their concentration profiles and penetration depth of topically applied substances are urgent topics of dermatological and cosmetic research. Confocal Raman micro-spectroscopy has distinct advantages when determining semi-quantitative concentrations of SC components and topically applied substances non-invasively and in vivo. In this work, we applied a tailored multivariate curve resolution-alternating least squares (tMCR-ALS) method to analyze Raman spectra of the SC in the 2000–4000 cm⁻¹ region for quantitatively determining the concentrations of water, lipids, proteins, and topically applied oils using substance-related spectral loadings which were allowed to change depth-dependently from the SC's surface toward its bottom. tMCR-ALS makes matching of depth-dependent signal attenuation, that is, the normalization on keratin, unnecessary and requires only a few additional experiments for calibration – Raman spectra of the pure materials and their densities.     

Fermentation process tracking through enhanced spectral calibration modeling

The FDA process analytical technology (PAT) initiative will materialize in a significant increase in the number of installations of spectroscopic instrumentation. However, to attain the greatest benefit from the data generated, there is a need for calibration procedures that extract the maximum information content. For example, in fermentation processes, the interpretation of the resulting spectra is challenging as a consequence of the large number of wavelengths recorded, the underlying correlation structure that is evident between the wavelengths and the impact of the measurement environment. Approaches to the development of calibration models have been based on the application of partial least squares (PLS) either to the full spectral signature or to a subset of wavelengths. This paper presents a new approach to calibration modeling that combines a wavelength selection procedure, spectral window selection (SWS), where windows of wavelengths are automatically selected which are subsequently used as the basis of the calibration model. However, due to the non-uniqueness of the windows selected when the algorithm is executed repeatedly, multiple models are constructed and these are then combined using stacking thereby increasing the robustness of the final calibration model. The methodology is applied to data generated during the monitoring of broth concentrations in an industrial fermentation process from on-line near-infrared (NIR) and mid-infrared (MIR) spectrometers. It is shown that the proposed calibration modeling procedure outperforms traditional calibration procedures, as well as enabling the identification of the critical regions of the spectra with regard to the fermentation process.     

Organelle specific simultaneous Raman/green fluorescence protein microspectroscopy for living cell physicochemical studies

We demonstrate a novel bio‐spectroscopic technique, “simultaneous Raman/GFP microspectroscopy”. It enables organelle specific Raman microspectroscopy of living cells. Fission yeast, Schizosaccharomyces pombe, whose mitochondria are green fluorescence protein (GFP) labeled, is used as a test model system. Raman excitation laser and GFP excitation light irradiate the sample yeast cells simultaneously. GFP signal is monitored in the anti‐Stokes region where interference from Raman scattering is negligibly small. Of note, 13 568 Raman spectra measured from different points of 19 living yeast cells are categorized according to their GFP fluorescence intensities, with the use of a two‐component multivariate curve resolution with alternate least squares (MCR‐ALS) analysis in the anti‐Stokes region. This categorization allows us to know whether or not Raman spectra are taken from mitochondria. Raman spectra specific to mitochondria are obtained by an MCR‐ALS analysis in the Stokes region of 1389 strongly GFP positive spectra. Two mitochondria specific Raman spectra have been obtained. The first one is dominated by protein Raman bands and the second by lipid Raman bands, being consistent with the known molecular composition of mitochondria. In addition, the second spectrum shows a strong band of ergosterol at 1602 cm^?1, previously reported as “Raman spectroscopic signature of life of yeast.”     

基于SVR算法的林地土壤氮含量高光谱测定

提出了一种利用高光谱技术进行杉木林土壤全氮测定的新方法。以FieldSpec®3地物光谱仪采集杉木林土壤148份, 随机分成校正集(100份)和检验集(48份)。以不同方法实现了土壤光谱的预处理, 并采用偏最小二乘回归算法(PLS)建立土壤氮含量估测模型对其进行比较分析, 发现小波除噪结合多元散射校正能最有效地消除原始光谱的噪声与背景信息, 此时PLS模型校正集与预测集R²分别为0.891与0.885。为进一步优化模型, 对经小波除噪结合多元散射校正处理后的光谱采用主成分分析法(PCA)降维, 以前4个主成份为输入变量, 采用小二乘支持向量机回归算法(LS-SVR)建立了土壤氮含量估测模型, 其校正集与预测集R²分别提高至0.921与0.917, 具有比PLS算法更高的精度。结果表明:以高光谱技术进行林地土壤氮含量快速监测是可行的, 其中小波去噪结合多元散射校正系光谱预处理的优选方法, 而LS-SVR则是建模的优选方法。     

Direct quantitative determination of amlodipine enantiomers in urine samples for pharmacokinetic study using on-line coupled isotachophoresis-capillary zone electrophoresis separation method with diode array detection

The present work illustrates possibilities of column-coupling capillary electrophoresis (CE-CE) combined with chiral selector (2-hydroxypropyl-beta-cyclodextrin, HP-beta-CD) and fiber-based diode array detection (DAD) for the direct quantitative enantioselective determination of trace drug (amlodipine, AML) in biological multicomponent ionic matrices (human urine). Capillary isotachophoresis (ITP) served as an ideal injection technique in CE-CE. Moreover, the ITP provided an effective on-line sample pretreatment prior to the capillary zone electrophoresis (CZE) separation. Enhanced separation selectivity due to the combination of different separation mechanisms (ITP vs. CZE-HP-beta-CD) enabled to obtain pure zones of the analytes, suitable for their detection and quantitation. The DAD, unlike single wavelength UV detection, enabled to characterize the purity (i.e. spectral homogeneity) of the analytes zones. A processing of the raw DAD spectra (the background correction and smoothing procedure) was essential when a trace analyte signal was evaluated. Obtained results indicated pure (i.e. spectrally homogeneous) zones of interest confirming effective ITP-CZE separation process. The proposed ITP-CZE-DAD method was characterized by favorable performance parameters (sensitivity, linearity, precision, recovery, accuracy, robustness, selectivity) and successfully applied to an enantioselective pharmacokinetic study of AML.     

Quantitative Detection of Pharmaceuticals Using a Combination of Paper Microfluidics and Wavelength Modulated Raman Spectroscopy

Raman spectroscopy has proven to be an indispensable technique for the identification of various types of analytes due to the fingerprint vibration spectrum obtained. Paper microfluidics has also emerged as a low cost, easy to fabricate and portable approach for point of care testing. However, due to inherent background fluorescence, combining Raman spectroscopy with paper microfluidics is to date an unmet challenge in the absence of using surface enhanced mechanisms. We describe the first use of wavelength modulated Raman spectroscopy (WMRS) for analysis on a paper microfluidics platform. This study demonstrates the ability to suppress the background fluorescence of the paper using WMRS and the subsequent implementation of this technique for pharmaceutical analysis. The results of this study demonstrate that it is possible to discriminate between both paracetamol and ibuprofen, whilst, also being able to detect the presence of each analyte quantitatively at nanomolar concentrations.     

Spectrophotometric Analysis of Pigments: A Critical Assessment of a High-Throughput Method for Analysis of Algal Pigment Mixtures by Spectral Deconvolution

The Gauss-peak spectra (GPS) method represents individual pigment spectra as weighted sums of Gaussian functions, and uses these to model absorbance spectra of phytoplankton pigment mixtures. We here present several improvements for this type of methodology, including adaptation to plate reader technology and efficient model fitting by open source software. We use a one-step modeling of both pigment absorption and background attenuation with non-negative least squares, following a one-time instrument-specific calibration. The fitted background is shown to be higher than a solvent blank, with features reflecting contributions from both scatter and non-pigment absorption. We assessed pigment aliasing due to absorption spectra similarity by Monte Carlo simulation, and used this information to select a robust set of identifiable pigments that are also expected to be common in natural samples. To test the method’s performance, we analyzed absorbance spectra of pigment extracts from sediment cores, 75 natural lake samples, and four phytoplankton cultures, and compared the estimated pigment concentrations with concentrations obtained using high performance liquid chromatography (HPLC). The deviance between observed and fitted spectra was generally very low, indicating that measured spectra could successfully be reconstructed as weighted sums of pigment and background components. Concentrations of total chlorophylls and total carotenoids could accurately be estimated for both sediment and lake samples, but individual pigment concentrations (especially carotenoids) proved difficult to resolve due to similarity between their absorbance spectra. In general, our modified-GPS method provides an improvement of the GPS method that is a fast, inexpensive, and high-throughput alternative for screening of pigment composition in samples of phytoplankton material.