On-line Near Infrared Spectroscopy Research Articles

On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development

NIR spectroscopy is a non-destructive tool for in-situ, on-line bioprocess monitoring. One of its most frequent applications is the determination of metabolites during cultivation, especially glucose. Previous studies have usually investigated the applicability of Near Infrared (NIR) spectroscopy at one bioreactor scale but the effect of scale up was not explored. In this study, the complete scale up from shake flask (1 L) through 20 L, 100 L and 1000 L up to 5000 L bioreactor volume level was monitored with on-line NIR spectroscopy. The differences between runs and scales were examined using principal component analysis. The bioreactor runs were relatively similar regardless of scales but the shake flasks differed strongly from bioreactor runs. The glucose concentration throughout five 5000 L scale bioreactor runs were predicted by partial least squares regression models that were based on pre-processed spectra of bioreactor runs and combinations of them. The model that produced the lowest error of prediction (4.18 mM on a 29 mM concentration range) for all five runs in the prediction set was based on the combination of 20 L and 100 L data. This result demonstrated the capabilities and the limitations of an NIR system for glucose monitoring in mammalian cell cultivations.

Characterization of fast pyrolysis bio-oil properties by near-infrared spectroscopic data

Pyrolysis transforms bulky and heterogeneous lignocellulosic biomass into more easily-handled oils that can be upgraded into bio-based transportation fuels. Existing systems for monitoring pyrolysis processes and characterizing their products rely on slow and time-consuming wet chemical analyses. On-line near-infrared (NIR) spectroscopy could potentially replace such analyses, providing real-time data and reducing costs. To test the usefulness of NIR methods in characterizing pyrolysis oils and processes, biomass from conifers, Salix, and reed canary grass was milled and pyrolyzed at 675, 750, and 775 °C. Two separate pyrolytic fractions (aerosol and condensed) were produced in each experiment, and NIR spectra were collected for each fraction. Multivariate modelling of the resulting data clearly showed that the samples’ NIR spectra could be used to accurately predict important properties of the pyrolysis oils such as their energy values, main organic element (C, H and O) contents, and water content. The spectra also contained predictive information on the samples’ origins, fraction, and temperature treatment, demonstrating the potential of on-line NIR techniques for monitoring pyrolytic production processes and characterizing important properties of pyrolytic oils from lignocellulosic biomass.

Innovative on-line near-infrared (NIR) spectroscopy to estimate content of each phase in composite polymer particles prepared by seeded emulsion polymerization

How to on-line analyze the morphology of composite polymer particles is a great challenge to develop the high-performed polymer nanoparticle materials, not only academia aspect but also industrial applications. We aim to on-line detect the weight fraction of each phase in composite polymer particles with a multilayered structure during emulsion polymerization. This work is also hoped to contribute to the development of on-line analysis on the morphology of composite polymer particles in the future. The weight fractions of core phase, interfacial layer, and shell layer (Wc, Wi, and Ws, respectively) in the multilayered composite polymer particles prepared by semi-continuous seeded emulsion terpolymerization of styrene (St), butyl acrylate (BA), and methyl methacrylate (MMA) with the existence of St-BA copolymer seed particles were successfully analyzed by using on-line near-infrared (NIR) spectroscopy for the first time on the basis of the results obtained by multi-frequency temperature-modulated differential scanning calorimetry (TOPEM-DSC), where NIR calibration models on Wc, Wi, and Ws were successfully established. By using NIR spectroscopy and the established models, the values of Wc, Wi, and Ws could be accurately on-line detected. It is suggested that there is a possibility to estimate the formation of unique morphology in composite polymer particles by on-line NIR measurement.

Reliability of online near-infrared spectroscopy analysis of the extraction process of Huatan Qushi decoction for phlegm-dampness constitution

ObjectiveTo establish a quantitative model for the online analysis of hesperidin content in the extraction process of Huatan Qushi decoction for individuals with phlegm-dampness constitution by using near-infrared (NIR) spectroscopy and partial least squares (PLS) methods. MethodsThe reference content of hesperidin, an effective ingredient of Huatan Qushi decoction, was determined using high-performance liquid chromatography (HPLC). The modeling band was selected using synergy interval PLS (SiPLS) algorithms, and a PLS model demonstrating the relationship between NIR predictive values and HPLC reference values was established. The optimal modeling route was selected through global optimization of process trace parameters. ResultsThe root mean square error of cross-validation was 9.8410. The coefficients of determination of the calibration set and prediction set were 0.9860 (R2cal) and 0.9458 (R2pre), respectively. The residual predictive deviation was 3.0115. The parameters demonstrated high predictive capability and reliability. The band was selected via the SiPLS method. There were seven latent variables. ConclusionThis study provides a favorable comprehensive evaluation method for variable selection and quality control of the Traditional Chinese Medicine extraction process.

Developments and Challenges in Online NIR Spectroscopy for Meat Processing.

Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

Development and testing an online near-infrared spectroscopy brain–computer interface tailored to an individual with severe congenital motor impairments

Purpose: For non-verbal individuals, brain–computer interfaces (BCIs) are a potential means of communication. Near-infrared spectroscopy (NIRS) is a brain-monitoring modality that has been considered for BCIs. To date, limited NIRS-BCI testing has involved online classification, particularly with individuals with severe motor impairments.Materials and methods: We tested an online NIRS-BCI developed for a non-verbal individual with severe congenital motor impairments. The binary BCI differentiated categorical verbal fluency task (VFT) performance and rest using prefrontal measurements. The participant attended five sessions, the last two of which were online with classification feedback.Results: An online classification accuracy of 63.33% was achieved using a linear discriminant classifier trained on a four-dimensional feature set. An offline, cross-validation analysis of all data yielded an optimal adjusted classification accuracy of 66.6 ± 9.11%. Inconsistent functional responses, contradictory effects of feedback, participant fatigue and motion artefacts were identified as challenges to online classification specific to this participant.Conclusions: Results suggest potential in using an NIRS-BCI controlled by the VFT in instances of severe congenital impairments. Further testing with users with severe disabilities is necessary.Implications for RehabilitationBrain–computer interfaces (BCIs) can provide a non-motor based means of communication for individuals with severe motor impairments.Near-infrared spectroscopy (NIRS) is a haemodynamic-based brain-imaging modality used in BCIs.To date, NIRS-BCIs have not been thoroughly tested with potential target users.This case study shows that NIRS-BCIs may offer a means of practical communication for individuals with severe congenital impairments and continued exploration is advisable.

Quantification of Lycopene, β-Carotene, and Total Soluble Solids in Intact Red-Flesh Watermelon (Citrullus lanatus) Using On-Line Near-Infrared Spectroscopy.

A great interest has recently been focused on lycopene and β-carotene, because of their antioxidant action in the organism. Red-flesh watermelon is one of the main sources of lycopene as the most abundant carotenoid. The use of near-infrared spectroscopy (NIRS) in post-harvesting has permitted us to rapidly quantify lycopene, β-carotene, and total soluble solids (TSS) on single intact fruits. Watermelons, harvested in 2013–2015, were submitted to near-infrared (NIR) radiation while being transported along a conveyor belt system, stationary and in movement, and at different positions on the belt. Eight hundred spectra from 100 samples were collected as calibration set in the 900–1700 nm interval. Calibration models were performed using partial least squares (PLS) regression on pre-treated spectra (derivatives and SNV) in the ranges 2.65–151.75 mg/kg (lycopene), 0.19–9.39 mg/kg (β-carotene), and 5.3%–13.7% (TSS). External validation was carried out with 35 new samples and on 35 spectra. The PLS models for intact watermelon could predict lycopene with R2 = 0.877 and SECV = 15.68 mg/kg, β-carotene with R2 = 0.822 and SECV = 0.81 mg/kg, and TSS with R2 = 0.836 and SECV = 0.8%. External validation has confirmed predictive ability with R2 = 0.805 and RMSEP = 16.19 mg/kg for lycopene, R2 = 0.737 and RMSEP = 0.96 mg/kg for β-carotene, and R2 = 0.707 and RMSEP = 1.4% for TSS. The results allow for the market valorization of fruits.

On-line near-infrared spectroscopy optimizing and monitoring biotransformation process of γ-aminobutyric acid

Near-infrared spectroscopy (NIRS) with its fast and nondestructive advantages can be qualified for the real-time quantitative analysis. This paper demonstrates that NIRS combined with partial least squares (PLS) regression can be used as a rapid analytical method to simultaneously quantify l-glutamic acid (l-Glu) and γ-aminobutyric acid (GABA) in a biotransformation process and to guide the optimization of production conditions when the merits of NIRS are combined with response surface methodology. The high performance liquid chromatography (HPLC) reference analysis was performed by the o-phthaldialdehyde pre-column derivatization. NIRS measurements of two batches of 141 samples were firstly analyzed by PLS with several spectral pre-processing methods. Compared with those of the HPLC reference analysis, the resulting determination coefficients (R2), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of the external validation for the l-Glu concentration were 99.5%, 1.62g/L, and 11.3, respectively. For the GABA concentration, R2, RMSEP, and RPD were 99.8%, 4.00g/L, and 16.4, respectively. This NIRS model was then used to optimize the biotransformation process through a Box-Behnken experimental design. Under the optimal conditions without pH adjustment, 200g/L l-Glu could be catalyzed by 7148 U/L glutamate decarboxylase (GAD) to GABA, reaching 99% conversion at the fifth hour. NIRS analysis provided timely information on the conversion from l-Glu to GABA. The results suggest that the NIRS model can not only be used for the routine profiling of enzymatic conversion, providing a simple and effective method of monitoring the biotransformation process of GABA, but also be considered to be an optimal tool to guide the optimization of production conditions.

Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain-computer interface.

Brain-computer interfaces (BCIs) allow individuals to use only cognitive activities to interact with their environment. The widespread use of BCIs is limited, due in part to their lack of user-friendliness. The main goal of this work was to develop a more user-centered BCI and determine if: (1)individuals can acquire control of an online near-infrared spectroscopy BCI via usability and performance-informed selection of mental tasks without compromising classification accuracy and (2)the combination of usability and performance-informed selection of mental tasks yields subjective ease-of-use ratings that exceed those attainable with prescribed mental tasks. Twenty able-bodied participants were recruited. Half of the participants served as a control group, using the state-of-the-art prescribed mental strategies. The other half of the participants comprised the study group, choosing their own personalized mental strategies out of eleven possible tasks. It was concluded that users were, in fact, able to acquire control of the more user-centered BCI without a significant change in accuracy compared to the prescribed task BCI. Furthermore, the personalized BCI yielded higher subjective ease-of-use ratings than the prescribed BCI. Average online accuracies of [Formula: see text] and [Formula: see text] were achieved by the personalized and prescribed mental task groups, respectively.

Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate.

Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R 2) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.

Research on quality parameters of Scutellariae Radix(formula particles) using on-line NIR in pilot with different extraction process

The on-line monitor for the changes in the content of baicalin in Scutellariae Radix formula particles during the extraction process was conducted by using near infrared spectroscopy (NIR). High performance liquid chromatography (HPLC) was used as a reference method. Kennard-Stone (KS) was used to divide sample sets, so as to compare different pretreatment methods. The synergy interval partial least squares (SiPLS) was used to screen out modeling wave band to establish partial least-squares models. The relative error method was applied to predict forecast set samples of Scutellariae Radix in three extraction phases. The results showed that the model established by Savitzky-Golay smoothing with 11 points (SG11 points) was the best, with the root mean square with cross validation (RMSECV), root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP) of baicalin of 0.092 7, 0.134 4 and 0.114 8, respectively, the determination coefficient R2 of greater than 0.99, and the relative error of baicalin content of less than 5%. This indicates that the on-line near infrared reflectance spectroscopy could be applied in on-line monitor and quality control of the extraction process of Scutellariae Radix formula particles.

Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest

Objective. Near-infrared spectroscopy (NIRS) has recently gained attention as a modality for brain–computer interfaces (BCIs), which may serve as an alternative access pathway for individuals with severe motor impairments. For NIRS–BCIs to be used as a real communication pathway, reliable online operation must be achieved. Yet, only a limited number of studies have been conducted online to date. These few studies were carried out under a synchronous paradigm and did not accommodate an unconstrained resting state, precluding their practical clinical implication. Furthermore, the potentially discriminative power of spatiotemporal characteristics of activation has yet to be considered in an online NIRS system. Approach. In this study, we developed and evaluated an online system-paced NIRS–BCI which was driven by a mental arithmetic activation task and accommodated an unconstrained rest state. With a dual-wavelength, frequency domain near-infrared spectrometer, measurements were acquired over nine sites of the prefrontal cortex, while ten able-bodied participants selected letters from an on-screen scanning keyboard via intentionally controlled brain activity (using mental arithmetic). Participants were provided dynamic NIR topograms as continuous visual feedback of their brain activity as well as binary feedback of the BCI's decision (i.e. if the letter was selected or not). To classify the hemodynamic activity, temporal features extracted from the NIRS signals and spatiotemporal features extracted from the dynamic NIR topograms were used in a majority vote combination of multiple linear classifiers. Main results. An overall online classification accuracy of 77.4 ± 10.5% was achieved across all participants. The binary feedback was found to be very useful during BCI use, while not all participants found value in the continuous feedback provided. Significance. These results demonstrate that mental arithmetic is a potent mental task for driving an online system-paced NIRS–BCI. BCI feedback that reflects the classifier's decision has the potential to improve user performance. The proposed system can provide a framework for future online NIRS–BCI development and testing.

Validation of NIR Model for On-line Monitoring of Flos Lonicera Japonica Extraction Process with Different Batches of Materials

In this study, on-line NIRs (Near Infrared Spectroscopy) monitoring technology, as a useful tool, has been verified during the extraction process of Flos Lonicera Japonica. To gain representative samples and accurate model result, the detrimental effects from NIR collecting circumstance were significantly reduced with meticulous designs. Based on what, different batches of Flos Lonicera Japonica material were real-time monitored in the extraction process. 10 design schemes were used to verify whether or not different batches could disturb predictive results of the quantitative model. According to a series of chemometric indicators, such as SEC, SECV, SEP, RPD, it is concluded that PLS models established by samples of different batches of raw herbs from the same source could be used to monitor the extraction process with acceptable accuracy. Our research has demonstrated that on-line NIRs monitoring technology as a fast, non-destructive and real-time monitoring tool could be utilized in traditional Chinese medicine preparation.

Multivariate detection limits of on-line NIR model for extraction process of chlorogenic acid from Lonicera japonica

A methodology is proposed to estimate the multivariate detection limits (MDL) of on-line near-infrared (NIR) model in Chinese Herbal Medicines (CHM) system. In this paper, Lonicera japonica was used as an example, and its extraction process was monitored by on-line NIR spectroscopy. Spectra of on-line NIR could be collected by two fiber optic probes designed to transmit NIR radiation by a 2mm-flange. High performance liquid chromatography (HPLC) was used as a reference method to determine the content of chlorogenic acid in the extract solution. Multivariate calibration models were carried out including partial least squares regression (PLS) and interval partial least-squares (iPLS). The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111mg to 0.068mg, and the R2 parameter increased from 0.9434 to 0.9801. Furthermore, MDL values were determined by a multivariate method using the type of errors and concentration ranges. The MDL of iPLS model was about 14ppm, which confirmed that on-line NIR spectroscopy had the ability to detect trace amounts of chlorogenic acid in L. japonica. As a result, the application of on-line NIR spectroscopy for monitoring extraction process in CHM could be very encouraging and reliable.

Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris

BackgroundNovel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.ResultsThis is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.ConclusionsThe findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

Quadri-pulse stimulation induces stimulation frequency dependent cortical hemoglobin concentration changes within the ipsilateral motor cortical network

BackgroundImaging studies investigating repetitive transcranial magnetic stimulation (rTMS) mediated hemodynamic consequences revealed inconsistent results, mainly due to differences in rTMS parameters and technical difficulties with simultaneous recordings during rTMS. Objective/HypothesisQuadri-pulse rTMS (QPS) induces bidirectional long-term plasticity of the human primary motor cortex (M1). To evaluate its on-line effects, near infrared spectroscopy (NIRS) recordings were performed during QPS. We hypothesized that on-line effects during QPS are different from long-term aftereffects. MethodsUsing a novel TMS - on-line multi-channel NIRS setup we recorded hemoglobin concentration [Hb] changes at the stimulated M1 and adjacent sensory-motor areas during QPS protocols inducing oppositely directed aftereffects (QPS-5: interstimulus interval (ISI) 5 ms, potentiation; QPS-50: ISI 50 ms, depression). In two experiments we studied NIRS changes during either single or repeated QPS bursts. ResultsThe repetitive QPS-5 bursts significantly decreased oxyhemoglobin concentration ([oxy-Hb]) in the ipsilateral M1. A single QPS-5 burst decreased [oxy-Hb] in the M1 and premotor cortex. QPS-50 induced no significant NIRS changes at any sites. ConclusionsQPS can significantly alter cortical hemodynamics depending on the stimulation frequency. While bidirectional long-term aftereffects of QPS reflect synaptic efficacy changes, unidirectional on-line effects during QPS may represent pure electrophysiological property changes within the cell membrane or synapse. Since neuronal postexcitatory inhibitory postsynaptic potentials typically peak within the first 10–20 ms, only pulses delivered at higher frequencies may lead to summation of the inhibitory effects, resulting in [oxy-Hb] decrease only after QPS-5. Our new TMS-NIRS setup may be valuable to investigate TMS induced neurovascular coupling mechanisms in humans.

Using near-infrared spectroscopy to predict postharvest quality.

Abstract Near-infrared spectroscopy offers great potential for controlling postharvest quality attributes of fruits and vegetables. It constitutes a very useful tool to ensure their acceptability and characterizing their nutritional properties. The literature regarding its use for postharvest quality parameters prediction on fresh fruit and vegetables, has been reviewed. The aspects of quality that can be measured by near-infrared spectroscopy (NIRS), its evolution, research on different products and most important areas for action, are highlighted. Desirable criteria for evaluating the reliability of the models and spectrometer features such as portability are discussed. Likely events in the evolution of online and portable NIRS in the near future are suggested.

Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Abstract Near infrared (NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples. The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques. Moreover, since each brand contains a large number of samples, an improved dendrogram was proposed to show the classification of different brands. The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) performs well in discrimination of the different brands, and the improved dendrogram could provide more information about the difference of the brands.

Online near-Infrared Spectroscopy Combined with Alternating Trilinear Decomposition for Process Analysis of Industrial Production and Quality Assurance

Process analysis is a great challenge in industry due to the complexity of industrial production. In the present work, an approach was developed based on online near-infrared spectroscopy and alternating trilinear decomposition (ATLD) method for industrial process analysis. The basic idea of the approach is to extract the common information that represents the common property of the batches using ATLD. Using common information, the production process can be monitored by investigating the variation of the common property, and quality assurance can be achieved by discrimination analysis. Taking the tobacco production as an example, the results show that the method is able to capture the intrinsic information of the products and performs well in process analysis and quality assurance.

A new PAT/QbD approach for the determination of blend homogeneity: Combination of on-line NIRS analysis with PC Scores Distance Analysis ( PC-SDA)

A novel and straightforward multivariate analytical tool for the qualitative determination of powder blend uniformity using on-line Near-Infrared Spectroscopy (NIRS) is presented. The approach combines current chemometric methods, e.g. spectral pre-processing and Principal Component Analysis (PCA), with (1) a new approach of data analysis to determine the end-point of the blending process, (2) building a design space (DS) for blend homogeneity and (3) developing a solid statistical rationale to stop blending according to Quality-by-Design (QbD) principles of FDA’s Process Analytical Technology (PAT) initiative. The new approach comprises calculation of Euclidean distances between PCA scores in a multidimensional space and determination of Moving Block Standard Deviations (MBSDs) of successive Principal Component (PC) scores distances to estimate a time-window during blending where spectral variability decreases to a preset minimum. Hotelling’s T 2 statistics is then used to monitor and report blend homogeneity. This technique is called “Principal Component Scores Distance Analysis” ( PC-SDA). A Central Composite Design resulting in 10 batches mixed in a bin-blender (same composition, different blender fill level, different number of revolutions) was executed. NIR Chemical Imaging (NIR-CI) in combination with Symmetry Parameter Image Analysis ( SPIA) was used to verify the NIRS analyzer response and assess homogeneity of all NIR-active components.