Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material

The aim of this study was to produce calibration equations between near-infrared reflectance (NIR) spectra and the concentrations of carbon, nitrogen, and phosphorus in heterogeneous material: from living needles to litter in Pinus halepensis stands subjected to prescribed burnings. The aim was to determine whether calibrations should be conducted within each stage in the transformation of needles (local calibrations), giving relationships that were accurate but valid only for each particular stage, or whether it was possible to integrate the various forms of variation in needles (global calibrations) while retaining an acceptable accuracy. A principal component analysis calculated from the sample spectral data was used to distinguish three different sets, each sharing spectral characteristics and corresponding to three categories of needle: needles collected on the pines (N), falling needles (F), and litter (L), and each containing samples collected from the burnt sites and a control site. Samples representative of all the forms of variation in spectral properties were selected from within each category and their carbon, nitrogen, and phosphorus concentrations were measured using standard wet chemistry methods; these constituted the calibration sets n, f, and l. Calibrations were produced between the nutrient concentrations and the NIR spectra of the calibration sets n, f, and l and the grouped sets (n+f, f+l, n+f+l). The results of local calibrations made from each individual category showed that the carbon, nitrogen, and phosphorus concentrations were accurately predictable by NIR spectra. The global calibrations made by lumping together several categories were valid for a wider range of concentrations and for spectrally heterogeneous materials and in most cases were just as accurate as the local calibrations produced from each individual category. Received: 2 March 1998 / Accepted: 19 November 1998     

Simultaneous measurement of glucose and glutamine in insect cell culture media by near infrared spectroscopy

The purpose of this study was to develop non-invasive techniques to monitor the composition of cell culture media in insect cell bioreactors. Such a monitor could be used in conjunction with a fed-batch feeding scheme to ensure that cells are maintained in an optimal environment for growth and protein production. Glucose and glutamine concentrations in an insect cell culture bioreactor were determined off-line with near-infrared (NIR) absorption spectroscopy. Spectra were collected from 5000 to 4000 cm(-1) with a 1.5-mm optical path length. Partial least squares (PLS) regression was applied to correlate the collected spectra with the concentration of the desired analytes. Under the culture conditions evaluated here, glucose and glutamine concentrations ranged from 38 to 55 mM and from 3 to 13 mM, respectively. Accurate measurements of glucose and glutamine in insect cell culture samples were possible over these entire ranges. The standard error of prediction (SEP) and mean percent error (MPE) for glutamine were 0.52 mM and 5.3%, respectively. Glucose could be measured with an SEP of 1.30 mM and an MPE of 2.3%. These levels of error are quite low considering the changing complexity of the growth media due to the shifting levels of amino acids, carbohydrates, yeastolate, proteins, and cell debris. This study represents an important step in the development of noninvasive on-line monitoring devices for cell culture bioreactors. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 11-15, 1997.     

In-Line Monitoring of a High-Shear Granulation Process Using the Baseline Shift of Near Infrared Spectra

Although near infrared (NIR) spectra are primarily influenced by undesired variations, i.e., baseline shifts and non-linearity, and many applications of NIR spectroscopy to the real-time monitoring of wet granulation processes have been reported, the granulation mechanisms behind these variations have not been fully discussed. These variations of NIR spectra can be canceled out using appropriate pre-processing techniques prior to spectral analysis. The present study assessed the feasibility of directly using baseline shifts in NIR spectra to monitor granulation processes, because such shifts can reflect changes in the physical properties of the granular material, including particle size, shape, density, and refractive index. Specifically, OPUSGRAN^®, a novel granulation technology, was investigated by in-line NIR monitoring. NIR spectra were collected using a NIR diffuse reflectance fiber optic probe immersed in a high-shear granulator while simultaneously examining the morphology, particle size, density, strength, and Raman images of the mixture during granulation. The NIR baseline shift pattern was found to be characteristic of the OPUSGRAN^® technology and was attributed to variations in the light transmittance, reflection, and scattering resulting from changes in the physicochemical properties of the samples during granulation. The baseline shift also exhibited an inflection point around the completion of granulation, and therefore may be used to determine the endpoint of the process. These results suggest that a specific pattern of NIR baseline shifts are associated with the unique OPUSGRAN^® granulation mechanism and can be applied to monitor the manufacturing process and determine the endpoint.     

Acoustic-resonance spectrometry as a process analytical technology for the quantification of active pharmaceutical ingredient in semi-solids

The purpose of this study was to demonstrate acoustic resonance spectrometry (ARS) as an alternative process analytical technology to near infrared (NIR) spectroscopy for the quantification of active pharmaceutical ingradient (API) in semi-solids such as creams, gels, ointments, and lotions. The ARS used for this research was an inexpensive instrument constructed from readily available parts. Acoustic-resonance spectra were collected with a frequency spectrum from 0 to 22.05 KHz. NIR data were collected from 1100 to 2500 nm. Using 1-point net analyte signal (NAS) calibration, NIR for the API (colloidal oatmeal [CO]) gave anr ² prediction accuracy of 0.971, and a standard error of performance (SEP) of 0.517%CO. ARS for the API resulted in anr ² of 0.983 and SEP of 0.317%CO. NAS calibration is compared with principal component regression. This research demonstrates that ARS can sometimes outperform NIR spectrometry and can be an effective analytical method for the quantification of API in semi-solids. ARS requires no sample preparation, provides larger penetration depths into lotions than optical techniques, and measures API concentrations faster and more accurately. These results suggest that ARS is a useful process analytical technology (PAT). Published: July 14, 2006     

At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy,chemometric modeling and model improvement

An analytical procedure has been developed for at-line (fast off-line) monitoring of 4 key parameters including nisin titer (NT), the concentration of reducing sugars, cell concentration and pH during a nisin fermentation process. This procedure is based on near infrared (NIR) spectroscopy and Partial Least Squares (PLS). Samples without any preprocessing were collected at intervals of 1 h during fifteen batch of fermentations. These fermentation processes were implemented in 3 different 5 l fermentors at various conditions. NIR spectra of the samples were collected in 10 min. And then, PLS was used for modeling the relationship between NIR spectra and the key parameters which were determined by reference methods. Monte Carlo Partial Least Squares (MCPLS) was applied to identify the outliers and select the most efficacious methods for preprocessing spectra, wavelengths and the suitable number of latent variables (n _LV). Then, the optimum models for determining NT, concentration of reducing sugars, cell concentration and pH were established. The correlation coefficients of calibration set (R _c) were 0.8255, 0.9000, 0.9883 and 0.9581, respectively. These results demonstrated that this method can be successfully applied to at-line monitor of NT, concentration of reducing sugars, cell concentration and pH during nisin fermentation processes.     

An open‐source code for Mie extinction extended multiplicative signal correction for infrared microscopy spectra of cells and tissues

Infrared spectroscopy of single cells and tissue is affected by Mie scattering. During recent years, several methods have been proposed for retrieving pure absorbance spectra from such measurements, while currently no user‐friendly version of the state‐of‐the‐art algorithm is available. In this work, an open‐source code for correcting highly scatter‐distorted absorbance spectra of cells and tissues is presented, as well as several improvements of the latest version of the Mie correction algorithm based on extended multiplicative signal correction (EMSC) published by Konevskikh et al. In order to test the stability of the code, a set of apparent absorbance spectra was simulated. To this purpose, pure absorbance spectra based on a Matrigel spectrum are simulated. Scattering contributions where obtained by mimicking the scattering features observed in a set of experimentally obtained spectra . It can be concluded that the algorithm is not depending strongly on the reference spectrum used for initializing the algorithm and retrieves well the underlying pure absorbance spectrum. The calculation time of the algorithm is considerably improved with respect to the resonant Mie scattering EMSC algorithm used by the community today.      

Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale

The modern wine industry needs tools for process control and quality assessment in order to better manage fermentation or bottling processes. During wine fermentation it is important to measure both substrate and product concentrations (e.g. sugars, phenolic compounds), however, the analysis of these compounds by traditional means requires sample preparation and in some cases several steps of purification are needed. The combination of visible/near-infrared (Vis/NIR) spectroscopy and chemometrics potentially provides an ideal solution to accurately and rapidly monitor physical or chemical changes in wine during processing without the need for chemical analysis. The aim of this study was to assess the possibility of combining spectral and multivariate techniques, such as principal component analysis (PCA), discriminant partial least squares (DPLS), or linear discriminant analysis (LDA), to monitor time-related changes that occur during red wine fermentation. Samples (n = 652) were collected at various times from several pilot scale fermentations with grapes from either Cabernet Sauvignon or Shiraz varieties, over three vintages (2001-2003) and scanned using a monochromator instrument (Foss-NIRSystems 6500, Silver Spring, MD) in transmission mode (400-2,500 nm). PCA was used to demonstrate consistent progressive spectral changes that occur through the time course of the fermentation. LDA using PCA scores showed that regardless of variety or vintage, samples belonging to a particular time point in fermentation could be correctly classified. This study demonstrates the potential of Vis/NIR spectroscopy combined with chemometrics, as a tool for the rapid monitoring of red wine fermentation.     

Estimation of the concentrations of cells, astaxanthin and glucose in a culture of Phaffia rhodozyma by near infrared reflectance spectroscopy

Summary Near infrared reflectance spectroscopy (NIR) was employed to estimate the concentrations of cells, astaxanthin and glucose in the culture broth of Phaffia rhodozyma. The culture broth (119 samples) was directly subjected for NIR analysis without any pretreatment. When the data obtained by NIR were compared with those obtained by conventional methods, high correlation coefficients were obtained: 0.98 for cells, 0.99 for astaxanthin and 0.94 for glucose. These results suggest that NIR analysis, which is very simple and requires only 3 to 5 mm for a sample, is applicable to monitor P. rhodozyma cultures.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

Combining near-infrared-excited autofluorescence and Raman spectroscopy improves in vivo diagnosis of gastric cancer

This study aims to evaluate the diagnostic utility of the combined near-infrared (NIR) autofluorescence (AF) and Raman spectroscopy for improving in vivo detection of gastric cancer at clinical gastroscopy. A rapid Raman endoscopic technique was employed for in vivo spectroscopic measurements of normal (n=1098) and cancer (n=140) gastric tissues from 81 gastric patients. The composite NIR AF and Raman spectra in the range of 800-1800 cm(-1) were analyzed using principal component analysis (PCA) and linear discriminant (LDA) to extract diagnostic information associated with distinctive spectroscopic processes of gastric malignancies. High quality in vivo composite NIR AF and Raman spectra can routinely be acquired from the gastric within 0.5s. The integrated intensity over the range of 800-1800 cm(-1) established the diagnostic implications (p=1.6E-14) of the change of NIR AF intensity associated with neoplastic transformation. PCA-LDA diagnostic modeling on the in vivo tissue NIR AF and Raman spectra acquired yielded a diagnostic accuracy of 92.2% (sensitivity of 97.9% and specificity of 91.5%) for identifying gastric cancer from normal tissue. The integration area under the receiver operating characteristic (ROC) curve using the combined NIR AF and Raman spectroscopy was 0.985, which is superior to either the Raman spectroscopy or NIR AF spectroscopy alone. This work demonstrates that the complementary Raman and NIR AF spectroscopy techniques can be integrated together for improving the in vivo diagnosis and detection of gastric cancer at endoscopy.     

Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors

Cell culture process development requires the screening of large numbers of cell lines and process conditions. The development of miniature bioreactor systems has increased the throughput of such studies; however, there are limitations with their use. One important constraint is the limited number of offline samples that can be taken compared to those taken for monitoring cultures in large‐scale bioreactors. The small volume of miniature bioreactor cultures (15 mL) is incompatible with the large sample volume (600 µL) required for bioanalysers routinely used. Spectroscopy technologies may be used to resolve this limitation. The purpose of this study was to compare the use of NIR, Raman, and 2D‐fluorescence to measure multiple analytes simultaneously in volumes suitable for daily monitoring of a miniature bioreactor system. A novel design‐of‐experiment approach is described that utilizes previously analyzed cell culture supernatant to assess metabolite concentrations under various conditions while providing optimal coverage of the desired design space. Multivariate data analysis techniques were used to develop predictive models. Model performance was compared to determine which technology is more suitable for this application. 2D‐fluorescence could more accurately measure ammonium concentration (RMSE_CV 0.031 g L^?1) than Raman and NIR. Raman spectroscopy, however, was more robust at measuring lactate and glucose concentrations (RMSE_CV 1.11 and 0.92 g L^?1, respectively) than the other two techniques. The findings suggest that Raman spectroscopy is more suited for this application than NIR and 2D‐fluorescence. The implementation of Raman spectroscopy increases at‐line measuring capabilities, enabling daily monitoring of key cell culture components within miniature bioreactor cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:337–346, 2017     

Identification of seed sources and parents of Pinus sylvestris L. using visible–near infrared reflectance spectra and multivariate analysis

The potential of visible (VIS) and near infrared (NIR) spectroscopy for identifying seed sources and parents of Pinus sylvestris L. was studied. Seeds of a single family (clones AC1005 × BD1178) collected from three localities in Sweden—Sävar (north), Röskär (central) and Degeberga (south)—and seeds from four maternal (clone no. BD1032, AC1014, BD1178 and AC1005) and four paternal (Y3020, BD1178, AC1014 and BD1032) parents were used to evaluate the method. VIS and NIR reflectance spectra were recorded on individual seeds using a NIRSystems Model 6500 spectrometer from 400 to 2,498 nm with a resolution of 2 nm. The VIS + NIR spectroscopic data were pre-treated with multiplicative signal correction, and analysed by soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA). The computed models were later applied to classify samples in the external test sets. The results show that seed sources were identified with 100% classification accuracy using PLS-DA models in the VIS + NIR, VIS and NIR regions. The average classification accuracy for maternal parents ranged from 92% to 96%, while paternal parents were identified with 91.2–96% accuracies. The classification accuracy using the SIMCA approach was relatively low for seed sources as well as maternal and paternal parents. It can be concluded that VIS + NIR spectroscopy could be employed as a rapid and non-destructive method for monitoring putative seed sources. The result underscores the prospect of the technique for characterizing seeds based on genotype, thereby serving as a tool in tree improvement and breeding.     

Interest of locally weighted regression to overcome nonlinear effects during in situ NIR monitoring of CHO cell culture parameters and antibody glycosylation

Animal cell culture processes have become the standard platform to produce therapeutic proteins such as recombinant monoclonal antibodies (mAb). Since the mAb quality could be subject to significant changes depending on manufacturing process conditions, real time monitoring and control systems are required to ensure mAb specifications mainly glycosylation and patient safety. Up to now, real time monitoring glycosylation of proteins has received scarce attention. In this article, the use of near infrared (NIR) to monitor mAb glycosylation has been reported for the first time. Whereas monitoring models are mainly constructed using linear partial least squares regressions (PLSR), evidences presented in this study indicate nonlinearity relationship between in situ captured spectra and compound concentrations, compromising the PLSR performances. A novel and simple approach was proposed to fit nonlinearity using the locally weighted regression (LWR). The LWR models were found to be more appropriate for handling information contained in spectra so that real time monitoring of cultures were accurately performed. Moreover, for the first time, the LWR calibration models allowed mAb glycosylation to be monitored, in a real time manner, by using in situ NIR spectroscopy. These results represent a further step toward developing active-control feedback of animal cell processes, particularly for ensuring properties of biologics.     

Near‐infrared spectroscopy for metabolite quantification and species identification

Near‐infrared (NIR) spectroscopy is a high‐throughput method to analyze the near‐infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. In this study, we investigate the accuracy of NIR spectroscopy in determining triglyceride level and species of wild‐caught Drosophila. We employ the chemometric approach to produce a multivariate calibration model. The multivariate calibration model is the mathematical relationship between the changes in NIR spectra and the property of interest as determined by the reference analytical method. Once the calibration model was developed, we used an independent set to validate the accuracy of the calibration model. The optimized calibration model for triglyceride quantification yielded coefficients of determination of 0.73 for the calibration test set and 0.70 for the independent test set. Simultaneously, we used NIR spectroscopy to discriminate two species of Drosophila. Flies from independent sets were correctly classified into Drosophila melanogaster and Drosophila simulans with accuracy higher than 80%. These results suggest that NIRS has the potential to be used as a high‐throughput screening method to assess a live individual insect's triglyceride level and taxonomic status.     

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km² and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg⁻¹; R² (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg⁻¹; R² = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R² and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy.     

Clinical application of near infrared fiber optic spectroscopy for noninvasive bone assessment

Approaches for noninvasive bone quality assessment are of great clinical need, particularly in individuals that require close monitoring of disease progression. X‐ray measurements are standard approaches to assess bone quality; however, they have several disadvantages. Here, a nonionizing approach for noninvasive assessment of the second metacarpal bone based on near infrared (NIR) spectroscopy was investigated. Transcutaneous bone signal detection was experimentally confirmed with cadaveric hand data, and Monte Carlo modeling further indicated that 50% of the measured signals arise from bone. Spectral data were collected via a NIR fiber optic from the bone of individuals with osteogenesis imperfecta, a disease marked by frequent bone fractures and fragility. Multiple significant correlations were found between spectral parameters related to water, protein and fat, and standard bone quality parameters obtained by X‐ray measurements. The results from this preliminary study highlight the potential application of NIR spectroscopy for the noninvasive assessment of bone quality.     

Monitoring a complex medium fermentation with sample-sample two-dimensional FT-NIR correlation spectroscopy

The possibility of monitoring state transitions during the time course of a fermentation through the analysis of the on-line collected near-infrared (NIR) spectra with sample-sample two-dimensional correlation spectroscopy (SS-2DCoS) was investigated. SS-2DCoS has proved to be useful for extracting process information directly from the spectra collected on-line. The complexity of the system studied prevented the extraction of concentration profiles, but nevertheless, the application of SS-2DCoS enables the identification of fermentation state transitions due to metabolic and morphological changes of the microorganism.     

Application of Raman spectroscopy in monoclonal antibody producing continuous systems for downstream process intensification

Monoclonal antibodies (mAbs) are biopharmaceuticals produced by mammalian cell lines in bioreactors at a variety of scales. Cell engineering, media optimization, process monitoring, and control strategies for in vitro production have become crucial subjects to meet increasing demand for these high value pharmaceuticals. Raman Spectroscopy has gained great attention in the pharmaceutical industry for process monitoring and control to maintain quality assurance. For the first time, this article demonstrated the possibility of subclass independent quantitative mAb prediction by Raman spectroscopy in real time. The developed model estimated the concentrations of different mAb isotypes with average prediction errors of 0.2 (g/L) over the course of cell culture. In situ Raman spectroscopy combined with chemometric methods showed to be a useful predictive tool for monitoring of real time mAb concentrations in a permeate stream without sample removal. Raman spectroscopy can, therefore, be considered as a reliable process analytical technology tool for process monitor, control, and intensification of downstream continuous manufacturing. The presented results provide useful information for pharmaceutical industries to choose the most appropriate spectroscopic technology for their continuous processes.     

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm^?1, 7515.24 to 7108.33 cm^?1, and 5257.00 to 5098.87 cm^?1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm^?1was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.