In situ monitoring of the seed stage of a fermentation process using non-invasive NIR spectrometry

Non-invasive NIR spectrometry has been used to monitor in situ the seed stage of a streptomyces fermentation process. The main spectral change occurred at 7263 cm(-1) in the 1st derivative spectrum, and from comparison with off-line NIR spectra acquired of components present in the fermentation broth, can be attributed to biomass. The biomass signal was constant for the first 20 h of the seed stage, after which it decreased before increasing again. The time at which the minimum occurred in the NIR profile was either the same or slightly earlier than that at which a maximum in the carbon dioxide evolution rate (CER) occurred. The changes observed for the biomass signal in the NIR spectra can be attributed to growth and then fragmentation of mycelia, which indicates a change in metabolic activity. Hence, it may be possible to use NIR spectrometry in situ to determine the optimum transfer time for the seed stage of a fermentation process. Spectra were also acquired of the final stage of the same fermentation process. The variation in the biomass signal in the NIR spectra was more complicated in the final stage owing to changes in stir rate, and biomass concentration and morphology.     

Quality control of the powder pharmaceutical samples of sulfaguanidine by using NIR reflectance spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were applied to the identification of the powder pharmaceutical samples of sulfaguanidine based on near infrared (NIR) diffuse reflectance spectra and their first derivative spectra. This work focused on the comparison of performances of the uni-output TCCCN (Uni-TCCCN) and multi-output (Multi-TCCCN) by near infrared diffuse reflectance spectra and their first derivative spectra of sulfaguanidine. The TCCCN models were verified with independent prediction samples by using the “cross-validation” method. The networks were used to discriminate qualified, un-qualified and counterfeit sulfaguanidines pharmaceutical powders. The results showed that single outputs network generally worked better than the multiple outputs networks, and the first derivative spectra were more suitable for the identification comparing with original diffuse reflectance spectra. With proper network parameters the pharmaceutical powders can be classified at rate of 100% in this work. Also, the effects of parameters and related problems were discussed.     

Real-time reaction monitoring using ion mobility-mass spectrometry

The potential of ion mobility (IM) spectrometry in combination with mass spectrometry (MS) for real-time reaction monitoring is reported. The combined IM-MS approach using electrospray ionization affords gas-phase analyte characterization based on both mass-to-charge (m/z) ratio and gas-phase ion mobility (drift time). The use of IM-MS analysis is demonstrated for the monitoring of the reaction products formed when 7-fluoro-6-hydroxy-2-methylindole is deprotonated by aqueous sodium hydroxide. Real-time reaction monitoring was carried out over a period of several hours, with the reaction mixture sampled and analysed at intervals of several minutes. Product ion relative intensity is enhanced selectively in the ion mobility-selected mass spectrum, compared to mass spectrometry alone. The combined IM-MS approach has potential as a rapid and selective technique to aid pharmaceutical process control and for the elucidation of reaction mechanism.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

Real-time, on-line monitoring of organic chemical reactions using extractive electrospray ionization tandem mass spectrometry

Extractive electrospray ionization mass spectrometry (EESI-MS) for real-time monitoring of organic chemical reactions was demonstrated for a well-established pharmaceutical process reaction and a widely used acetylation reaction in the presence of a nucleophilic catalyst, 4-dimethylaminopyridine (4-DMAP). EESI-MS provides real-time information that allows us to determine the optimum time for terminating the reaction based on the relative intensities of the precursors and products. In addition, tandem mass spectrometric (MS/MS) analysis via EESI-MS permits on-line validation of proposed reaction intermediates. The simplicity and rapid response of EESI-MS make it a valuable technique for on-line characterization and full control of chemical and pharmaceutical reactions, resulting in maximized product yield and minimized environmental costs. Copyright (c) 2008 John Wiley & Sons, Ltd.     

Real-time quantification for sulfite using a turn-on NIR fluorescent probe equipped with a portable fluorescence detector

Sulfur dioxide and its derivative sulfite widely existed in air, water as the environment pollutant. Sulfite is also commonly used as preservative and additive in fresh fruits, vegetables, wines and pharmaceutical materials. Due to sulfite is closely related with human diseases, it is very urgent for the sensitive and rapid quantification of sulfite in various samples. In our study, a turn-on near infrared (NIR) fluorescent probe (MDQ) was developed for sulfite detection based on a Michael addition reaction, with high sensitivity (LOD 4.16 nmol/L), selectivity and fast response time (400 s). Using MDQ, a quantify method for sulfite in traditional Chinese medicines (TCMs) was developed with the advantages of high precision, accuracy and convenient operation. Furthermore, according to the photophysical property of MDQ, a portable fluorescence detector is designed to quantify sulfite for TCMs and surface water in Dalian city of China. Therefore, the developed fluorescent probe MDQ and portable fluorescent detector as a rapid inspection instrument were successfully used to real-time monitor the sulfite in various complex samples.     

Blend uniformity evaluation during continuous mixing in a twin screw granulator by in-line NIR using a moving F-test

This study focuses on the twin screw granulator of a continuous from-powder-to-tablet production line. Whereas powder dosing into the granulation unit is possible from a container of preblended material, a truly continuous process uses several feeders (each one dosing an individual ingredient) and relies on a continuous blending step prior to granulation.     

Construction of a universal model for non-invasive identification of penicillins for injection using near-infrared diffuse reflectance spectroscopy

A universal NIR model for identification of 24 types of penicillins for injection has been developed. A total of 194 batches of 24 products from 87 manufacturers in China were used in the study. The classification model is a principal component analysis (PCA) based model consisting of a primary identification library with four sub-libraries. The spectral frequency regions used were 6000–6400 cm⁻¹ and 8400–8900 cm⁻¹ in the main library, 6000–6800 cm⁻¹ in sub-library 1, 4100–12,000 cm⁻¹ in sub-libraries 2 and 3, and 6200–6400 cm⁻¹ and 4700–5000 cm⁻¹ in sub-library 4. The data preprocessing method is the first derivative with nine-point smoothing followed by vector normalization. The distances between spectra were calculated using factors 2–5 for the primary identification library, factors 4–7 for sub-library 1, and factor 2 for sub-libraries 2–4. The specificity of the model was validated, and it had a correct identification rate of approximately 99%. This study has not only confirmed, but also improved the strategy described in our early report (Chong et al. (2009) [11]) to build such a library for the identification of different medicines by NIR.     

Construction of a universal model for non-invasive identification of cephalosporins for injection using near-infrared diffuse reflectance spectroscopy

A universal classification model has been developed for identification of 26 different products of cephalosporins for injection using near-infrared (NIR) spectroscopy. A total of 324 batches of the 26 products, from 166 manufacturers in China, were used in the study. The classification model was a principle component analysis (PCA)-based method consisting of a primary identification library with four sub-libraries. The accuracy and specificity of the model were tested and were both found to be approximately 95%. The transfer of the classification model between different instruments of the same brand and the same instrument model has been investigated in this study. This study has shown that it is feasible to build a universal classification model to quickly screen for counterfeit drugs in the open market and distribution channels.     

Construction of a universal model for non-invasive identification of penicillins for injection using near-infrared diffuse reflectance spectroscopy

A universal NIR model for identification of 24 types of penicillins for injection has been developed. A total of 194 batches of 24 products from 87 manufacturers in China were used in the study. The classification model is a principal component analysis (PCA) based model consisting of a primary identification library with four sub-libraries. The spectral frequency regions used were 6000–6400 cm⁻¹ and 8400–8900 cm⁻¹ in the main library, 6000–6800 cm⁻¹ in sub-library 1, 4100–12,000 cm⁻¹ in sub-libraries 2 and 3, and 6200–6400 cm⁻¹ and 4700–5000 cm⁻¹ in sub-library 4. The data preprocessing method is the first derivative with nine-point smoothing followed by vector normalization. The distances between spectra were calculated using factors 2–5 for the primary identification library, factors 4–7 for sub-library 1, and factor 2 for sub-libraries 2–4. The specificity of the model was validated, and it had a correct identification rate of approximately 99%. This study has not only confirmed, but also improved the strategy described in our early report (Chong et al. (2009) [11]) to build such a library for the identification of different medicines by NIR.     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

Real-time characterization of cytotoxicity using single-cell impedance monitoring

Cellular impedance sensors have attracted great attention as a powerful characterization tool for real-time, label-free detection of cytotoxic agents. However, impedance measurements with conventional cell-based sensors that host multiple cells on a single electrode neither provide optimal cell signal sensitivity nor are capable of recording individual cell responses. Here we use a single-cell based platform to monitor cellular impedance on planar microelectrodes to characterize cellular death. In this study, individual cells were selectively patterned on microelectrodes with each hosting one live cell through ligand-mediated natural cell adhesion. Changes in cellular morphology and cell-electrode adherence were monitored after the patterned cells were treated with varying concentrations of hydrogen peroxide, sodium arsenite, and disodium hydrogen arsenate, three potent toxicants related to neurotoxicity and oxidative stress. At low toxicant concentrations, impedance waveforms acquired from individual cells showed variable responses. A time- and concentration-dependent response was seen in the averaged single-cell impedance waveform for all three toxicants. The apoptosis and necrosis characterizations were performed to validate cell impedance results. Furthermore, time constants of apoptosis and necrosis in response to toxicant exposure were analytically established using an equivalent circuit model that characterized the mechanisms of cell death.     

Real-time monitoring of polyurethane production using near-infrared spectroscopy

A process near-infrared (NIR) spectrophotometer was interfaced directly to a reactor by using a fiber optic bundle interactance immersion probe. This remote sensor configuration enables the production of polyurethanes to be monitored in real-time. A Beer's Law model was derived for the quantitative determination of isocyanate in the urethane polymerization reaction. Statistical process control was used to observe trends in the polymerization reaction. The integration of NIR process analytical instrumentation directly into the process provides real-time chemical information that yields improvements in product quality and consistency, while minimizing reaction time.     

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.     

Real-time identification of gut microbiota with aminopeptidase N using an activable NIR fluorescent probe

A NIR fluorescent probe (DDAA) derived from fluorophore DDAO with alanine as the recognition group was developed for sensing aminopeptidase N (APN) in gut microbiota. Using DDAA as the real-time guidance tool for the fluorescence imaging of intestinal microorganism, target bacteria and saccharomycete possessing active APN were identified successfully from human feces.     

Real-time monitoring of biomolecular interactions in blood plasma using a surface plasmon resonance biosensor

We report a novel approach to biosensor-based observations of biomolecular interactions which enables real-time monitoring of biomolecular interactions in complex media. This approach is demonstrated by investigating the interaction between the human chorionic gonadotropin (hCG) and its antibody in blood plasma using a surface plasmon resonance biosensor and a dispersionless microfluidics system. The real-time binding data obtained in blood plasma are compared with those obtained in buffer and blood plasma using a conventional method. It is also demonstrated that the proposed approach can enhance the capability of the biosensor to detect biomolecules in complex samples in terms of detection time and sensitivity. In the model experiment, this approach is shown to enable direct detection of hCG in blood plasma at levels which are five times lower than those detected using the conventional detection approach.     

Real-time monitoring of double-stranded DNA cleavage using molecular beacons

Traditional methods to assay enzymatic cleavage of DNA are discontinuous, time-consuming and laborious. Here, we report a new approach for real-time monitoring of double-stranded DNA cleavage by restriction endonuclease based on nucleic acid ligation using molecular beacon. Upon cleavage of DNA, the cleavage product can be ligated by DNA ligase, which results in a fluorescence enhancement of the molecular beacon. This method permits real-time monitoring of DNA cleavage and makes it easy to characterize the activity of restriction endonuclease and to study the cleavage reaction kinetics.     

Real-time monitoring of DNAzyme cleavage process using fluorescent assay

Detection of deoxyribozyme(DNAzyme) cleavage process usually needs complex and time-consuming radial labeling,gel electrophoresis and autoradiography.This paper reported an approach to detect DNAzyme cleavage process in real time using a fluorescence probe.The probe was employed as DNAzyme substrate to convert directly the cleavage information into fluorescence signal in real time.Compared with traditional approach,this non-isotope method not only brought a convenient means to monitor the DNAzyme cleavag...     

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.     

Real-time monitoring traces of SF6 in near-source ambient air by ion mobility spectrometry

The leakage of sulphur hexafluoride (SF₆) gas threats the global climate changes and personnel safety. Monitoring the concentration of SF₆ in its application places is an industry regulation. In this study, ion mobility spectrometry (IMS) was developed for fast monitoring traces of SF₆ in near-source ambient air. Due to the water is an important part of the natural air and affects most atmospheric measurements, the operating parameters of IMS monitoring SF₆ were optimised for quantitative analysis of SF₆ at different relative humidity (RH). It is discovered two main product ions SF₆^? and SOF₄^? by IMS at different RH. The calibration curves of SF₆ were investigated by its relationship with the peak intensity of SOF₄^– for real application. The time resolution of the measurement was obtained less than 1 s and the limit of detection (LOD) achieved 0.16–0.68 ppm with a data averaging of 30 times. At last, the simulated application of monitoring SF₆ leakage was tested in the fume hood of our lab. The results showed a great potential application prospect of IMS in monitoring SF₆ in the ambient air of its application places.