Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

In this work, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant’s response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These were compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.     

Design of Experiments-Based Monitoring of Critical Quality Attributes for the Spray-Drying Process of Insulin by NIR Spectroscopy

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q² of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.KEY WORDS: moisture content, multivariate analysis, NIR, particle size, spray-drying     

Determination of total protein in hyperimmune serum samples by near-infrared spectrometry and multivariate calibration

In the present work, the determination of the total protein concentration in hyperimmune serum samples was performed through a partial least-squares near-infrared (NIR-PLS) method. The method was based on the chemometric treatment of the NIR spectra of samples. The influences of spectra preprocessing and spectral window utilized in the construction of PLS model were studied. Models were built using reference data of 19 samples selected through the use of hierarchical cluster analysis (HCA) of NIR spectra of samples and another 24 samples were employed for external validation of the method. A model with better prediction capacity was obtained after whole spectra preprocessing by multiplicative scattering correction (MSC) algorithm and using data in the spectral range of 2158-2209 nm. Under optimized conditions a RMSEP of 0.21 g dl⁻¹ and a quality coefficient value (QC) of only 5.8% were obtained for the prediction of total protein content in the samples used for external validation. Also, a determination coefficient, r², of 0.97 was obtained in the correlation of predicted and reference data of samples situated in the validation set.     

Rapid analysis of sugars in fruit juices by FT-NIR spectroscopy.

A simple analytical procedure using FT-NIR and multivariate techniques for the rapid determination of individual sugars in fruit juices was evaluated. Different NIR detection devices and sample preparation methods were tested by using model solutions to determine their analytical performance. Aqueous solutions of sugar mixtures (glucose, fructose, and sucrose; 0-8% w/v) were used to develop a calibration model. Direct measurements were made by transflection using a reflectance accessory, by transmittance using a 0.5-mm cell, and by reflectance using a fiberglass paper filter. FT-NIR spectral data were transformed to the second derivative. Partial least-squares regression (PLSR) was used to create calibration models that were cross-validated (leave-one-out approach). The prediction ability of the models was evaluated on fruit juices and compared with HPLC and standard enzymatic techniques. The PLSR loading spectra showed characteristic absorption bands for the different sugars. Models generated from transmittance spectra gave the best performance with standard error of prediction (SEP) <0.10% and R(2) of 99.9% that accurately and precisely predicted the sugar levels in juices, whereas lower precision was obtained with models generated from reflectance spectra. FT-NIR spectroscopy allowed for the rapid ( approximately 3 min analysis time), accurate and non-destructive analysis of sugars in juices and could be applied in quality control of beverages or to monitor for adulteration or contamination.     

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.     

A versatile chiral selector for determination of enantiomeric composition of fluorescent and nonfluorescent chiral molecules using steady-state fluorescence spectroscopy

A fluorescent chiral molecular micelle (FCMM), poly (sodium N-undecanoyl-L-phenylalaninate) (poly-L-SUF), was developed as a chiral selector for enantiomeric recognition and determination of enantiomeric composition of four fluorescent and four nonfluorescent chiral molecules by use of steady-state fluorescence spectroscopy. The influence of FCMM concentration, buffer pH and complexation medium on FCMM-analyte host-guest complexation, and the emission spectral properties of the resulting complexes were investigated. The chiral interactions of the analytes,1,1'-binaphthyl-2,2'-diamine, 1-(9-anthryl)-2,2,2-trifluoroethanol, propranolol, naproxen, chloromethyl menthyl ether (CME), citramalic acid, tartaric acid, and limonene (LIM), in the presence of poly-L-SUF were based on diastereomeric complex formation. The figures of merit obtained from the partial-least-squares regression modeling of the calibration samples suggested good prediction ability for the validation of six of the eight chiral analytes. Better host-guest complexation of the more hydrophobic molecules, CME and LIM, were obtained in methanol/water mixtures, resulting in better predictability of the regression models. Prediction ability of the models was evaluated by use of the root-mean-square percent relative error (RMS%RE) and was found to range from 1.77 to 15.80% (buffer), 1.26 to 7.95% (25:75 methanol/water), and 1.21 to 4.28% (75:25 methanol/water).     

On-line near infrared monitoring of glycerol-boosted anaerobic digestion processes: evaluation of process analytical technologies

A study of NIR as a tool for process monitoring of thermophilic anaerobic digestion boosted by glycerol has been carried out, aiming at developing simple and robust Process Analytical Technology modalities for on-line surveillance in full scale biogas plants. Three 5 L laboratory fermenters equipped with on-line NIR sensor and special sampling stations were used as a basis for chemometric multivariate calibration. NIR characterisation using Transflexive Embedded Near Infra-Red Sensor (TENIRS) equipment integrated into an external recurrent loop on the fermentation reactors, allows for representative sampling, of the highly heterogeneous fermentation bio slurries. Glycerol is an important by-product from the increasing European bio-diesel production. Glycerol addition can boost biogas yields, if not exceeding a limiting 5-7 g L(-1) concentration inside the fermenter-further increase can cause strong imbalance in the anaerobic digestion process. A secondary objective was to evaluate the effect of addition of glycerol, in a spiking experiment which introduced increasing organic overloading as monitored by volatile fatty acids (VFA) levels. High correlation between on-line NIR determinations of glycerol and VFA contents has been documented. Chemometric regression models (PLS) between glycerol and NIR spectra needed no outlier removals and only one PLS-component was required. Test set validation resulted in excellent measures of prediction performance, precision: r(2) = 0.96 and accuracy = 1.04, slope of predicted versus reference fitting. Similar prediction statistics for acetic acid, iso-butanoic acid and total VFA proves that process NIR spectroscopy is able to quantify all pertinent levels of both volatile fatty acids and glycerol.     

NIR techniques create added values for the pellet and biofuel industry

A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.     

Application of vibrational spectroscopy in the quality assessment of Buchu oil obtained from two commercially important Agathosma species (Rutaceae)

Quality assessment of natural raw materials and derived consumer products is often done using conventional analytical techniques such as liquid and gas chromatography which are expensive and time consuming. This paper reports on the use of vibrational spectroscopy techniques as possible alternatives for the rapid and inexpensive assessment of the quality of ‘buchu oil’ obtained from two South African species; Agathosma betulina and Agathosma crenulata belonging to the Rutaceae family. Samples of A. betulina (55) and A. crenulata (16) were collected from different natural localities and cultivation sites in South Africa. The essential oil was obtained by hydrodistillation and scanned on Near infrared (NIR), mid infrared (MIR) and Raman spectrometers. The spectral data obtained was processed using chemometric techniques and orthogonal partial least squares discriminant analysis (OPLS-DA) was used to clearly differentiate between A. betulina and A. crenulata. The OPLS-DA technique also proved to be a useful tool to identify wave regions that contain biomarkers (peaks) that contributed to the separation of the two species. The three spectroscopy techniques were also evaluated for their ability to accurately predict the percentage composition of seven major compounds that occur in A. betulina ‘buchu’ oil. Using GC–MS reference data, calibration models were developed for the MIR, NIR and Raman spectral data to predict/profile the major compounds in ‘buchu oil’. A comparison of the three spectroscopy techniques showed that MIR together with PLS algorithms produced the best model (R²X = 0.96; R²Y = 0.88 and Q²Ycum = 0.85) for the quantification of six of the seven major oil constituents. The MIR model showed high predictive power for pseudo-diosphenol (R² = 0.97), isomenthone (R² = 0.97), menthone (R² = 0.90), limonene (R² = 0.91), pulegone (R² = 0.96) and diosphenol (R² = 0.85). These results illustrate the potential of MIR spectroscopy as a rapid and inexpensive alternative to predict the major compounds in buchu oil.     

Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by ¹H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a ¹H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.     

NMR and interval PLS as reliable methods for determination of cholesterol in rodent lipoprotein fractions

Risk of cardiovascular disease is related to cholesterol distribution in different lipoprotein fractions. Lipoproteins in rodent model studies can only reliably be measured by time- and plasma-consuming fractionation. An alternative method to measure cholesterol distribution in the lipoprotein fractions in rat plasma is presented in this paper. Plasma from two rat studies (n = 68) was used in determining the lipoprotein profile by an established ultracentrifugation method and proton nuclear magnetic resonance (NMR) spectra of replicate samples was obtained. From the ultracentrifugation reference data and the NMR spectra, an interval partial least-square (iPLS) regression model to predict the amount of cholesterol in the different lipoprotein fractions was developed. The relative errors of the prediction models were between 12 and 33% and had correlation coefficients (r) between 0.96 and 0.84. The models were tested with an independent test set giving prediction errors between 19 and 46% and r between 0.96 and 0.76. Prediction of High, Low and Very Low Density Lipoprotein (HDL, LDL and VLDL) and total cholesterol was conducted in a study where rats had been supplemented with two doses of air-dried apple-powder. No significant difference in LDL, VLDL and total cholesterol was observed between the groups. The high apple-powder (20%) group had significantly lower HDL cholesterol (11%, P = 0.0452) than the control group. It is concluded that the iPLS approach yielded excellent regression models and thus univocal established chemometric analysis of NMR spectra of rat plasma as a strong and efficient way to quantify lipoprotein fractions in rat studies.     

Effectiveness of near-infrared spectroscopy as a non-invasive tool to discriminate spectral profiles of in vitro cultured oocytes from goats

Here, we aimed to discriminate between the spectral profiles of spent culture media after oocyte in vitro maturation (IVM) and culture (IVC) from goats of different ages subjected to repeated hormonal treatments. The profiles were discriminated using near infrared (NIR) spectroscopy combined with multivariate methods. A total of 19 goats (young = 10; old = 9) were subjected to serial hormonal stimulation (HS) with gonadotropins. Cumulus oophorus complexes (COCs) were collected using laparoscopic ovum pick-up (LOPU) and subjected to IVM and parthenogenetic activation. The initial embryos were subjected to IVC. Spent culture media were collected after oocyte IVM and on day 2 of IVC and analyzed using NIR spectroscopy. NIR spectral data were interpreted through chemometric methods, such as principle component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The results of PCA analysis clearly showed a separation in the spectral profiles between the experimental groups (HS sessions; young and old animals) both after IVM and IVC. Overall, the main absorption bands were attributed to the C-H group second overtone, first overtone of O-H and N-H, and C-H combinations and may serve as molecular markers. On the other hand, the spectral data obtained using PLS-DA models provided a better classification of the groups. The results showed the possibility of discriminating young and old groups as well as the three HS sessions with high specificity, sensitivity, and accuracy using NIR spectra. Thus, the culture medium analysis using NIR spectroscopy combined with multivariate methods indicated the dissimilarities between the groups and provided an insight into the in vitro development of goat oocytes. This technique serves as an efficient, objective, rapid, and non-invasive method to discriminate spectral profiles.     

Vibrational spectroscopy and chemometrics to characterize and quantitate trehalose crystallization

Investigating the phase behavior of sugars in ice and lyophilized solids is of significant interest in the pharmaceutical industry. In this study, Raman and near infrared (NIR) spectroscopy are used to characterize and quantitate trehalose crystallization using several chemometric models. The predictive behaviors of partial least squares (PLS), principal component analysis (PCA), and multiple linear regression (MLR) models are compared. In general, PLS and PCA outperform linear and MLR models. Changes in specific vibrational modes associated with several coupled motions are described and assigned as a function of crystal content. In addition to characterization and quantitation, our method may be used to localize gradients of amorphous and/or crystallized trehalose within a sample.     

On-line process monitoring and chemometric modeling with 2D fluorescence spectra obtained in recombinant E. coli fermentations

2D spectrofluorometry produces a large volume of spectral data during fermentation processes with recombinant E. coli, which can be analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). An analysis of the spectral data by PCA results in scores and loadings that are not only visualized in the score-loading plots but are also used to monitor the fermentation processes on-line. The score plots provided useful qualitative information on four fermentation processes for the production of extracellular 5-aminolevulinic acid (ALA). Two chemometric models (PCR and PLS) were used to examine the correlation between the 2D fluorescence spectra and a few parameters of the fermentation processes. The results showed that PLS had slightly better calibration and prediction performance than PCR.     

Chemometric modelling based on 2D-fluorescence spectra without a calibration measurement

MOTIVATION: 2D fluorescence spectra provide information from intracellular compounds. Fluorophores like trytophan, tyrosine and phenylalanin as well as NADH and flavins make the corresponding measurement systems very important for bioprocess supervision and control. The evaluation is usually based on chemometric modelling using for their calibration procedure off-line measurements of the desired process variables. Due to the data driven approach lots of off-line measurements are required. Here a methodology is presented, which enables to perform a calibration procedure of chemometric models without any further measurement. RESULTS: The necessary information for the calibration procedure is provided by means of the a priori knowledge about the process, i.e. a mathematical model, whose model parameters are estimated during the calibration procedure, as well as the fact that the substrate should be consumed at the end of the process run. The new methodology for chemometric calibration is applied for a batch cultivation of aerobically grown S. cerevisiae on the glucose Schatzmann medium. As will be presented the chemometric models, which are determined by this method, can be used for prediction during new process runs. AVAILABILITY: The MATHLAB routine is free available on request from the authors.     

基于NIRS法快速检测荞麦蛋白质与淀粉含量的研究

为建立近红外光谱技术测定荞麦蛋白质与淀粉含量的方法,本研究以217份荞麦样品为试验材料,采用最小二乘回归预测和交叉验证构建近红外预测模型。分析表明:前处理采用多元散射校正法(MSC),维数(Rank)分别为5和5,光谱区间6803.9~6094.2/cm所建立的荞麦蛋白质与淀粉含量模型的预测效果较好,其决定系数(R~2)分别为0.9481和0.9167,交叉验证均方根(RMSECV)分别为0.68和2.08,相对分析误差(RPD)分别为4.39和3.46,均大于3.0,外部验证相关系数均大于0.96。本试验所建立的蛋白质与淀粉含量近红外预测模型具有较高的准确度和稳健性,可用于荞麦品质的快速测定。     

Exploring the efficacy of Basella alba mucilage towards the encapsulation of the hydrophobic antioxidants for their better performance

In the present study, the efficacy of Basella alba L. (commonly known as Indian spinach) mucilage (BAM) was explored for the first time towards the encapsulation of hydrophobic antioxidants. The hydrophobic antioxidants were encapsulated into the BAM matrix by modified non-solvent precipitation method and the encapsulated systems were fully characterized on the basis of TGA, DLS and SEM data. Interactions between the components of BAM matrix and the hydrophobic antioxidants are the key factors for the efficient encapsulation process. These interactions were studied with the help of spectroscopic techniques. The BAM-encapsulated antioxidants showed high pH and photo-stability. Moreover, the hydrophobic antioxidants after their encapsulation in the BAM matrix showed enhanced water solubility and hence, bioactivity in aqueous medium. Thus, BAM may be explored in future as an ideal candidate for the encapsulation and delivery of the hydrophobic bioactive compounds in cellular medium.     

The molecular mobility of water in natural polymers: silk Bombyx mori with a low water content as studied by 1H DQF NMR

The molecular mobility of water in fibres of natural silk (Bombyx mori) was studied by the double-quantum-filtered (DQF) and single-pulse 1H NMR techniques. The results obtained showed a slow motion of water molecules and their strong interaction with silk macromolecules. At different model functions for resonance lineshape in 1H NMR spectra, the influence of signal linewidth on the estimation of relaxation times and cross-relaxation parameters was considered. The observed 1H DQF NMR signal in B. mori silk fibres (BC = 0.065) indicated a local order and anisotropic motion of water molecules, which leads to 1H-1H dipolar interactions in natural silk fibers due to the creation of the second-rank tensors (T(2,+1), T(2,-1)). DQF spectra were the difference of two Lorentzians with different linewidths and were analyzed using the theory of 1H DQF NMR and the data on residual dipolar interactions in systems with the anisotropic mobility of water molecules. The residual dipolar interactions was insignificant and, as the humidity increased (0.18), no DQF-signals and residual dipolar interactions were observed.     

Simultaneous Prediction of Human Antithrombin III and Main Metabolites in Animal Cell Culture Processes by Near-Infrared Spectroscopy

Samples of cell culture supernatants were analysed by near-infrared (NIR) spectroscopy. Spectral information was used to generate calibration models by using multivariate partial least squares (PLS) regression. Concentration predictions by these models were in good agreement with the values produced by conventional methods. Due to the simple pre-treatment of the sample and the short time required for measurement and prediction, NIR is a promising tool for automation of a cell culture process.     

Assessment of in-line near-infrared spectroscopy for continuous monitoring of fermentation processes

The application of NIR in-line to monitor and control fermentation processes was investigated. Determination of biomass, glucose, and lactic and acetic acids during fermentations of Staphylococcus xylosus ES13 was performed by an interactance fiber optic probe immersed into the culture broth and connected to a NIR instrument. Partial least squares regression (PLSR) calibration models of second derivative NIR spectra in the 700-1800 nm region gave satisfactory predictive models for all parameters of interest: biomass, glucose, and lactic and acetic acids. Batch, repeated batch, and continuous fermentations were monitored and automatically controlled by interfacing the NIR to the bioreactor control unit. The high frequency of data collection permitted an accurate study of the kinetics, supplying lots of data that describe the cultural broth composition and strengthen statistical analysis. Comparison of spectra collected throughout fermentation runs of S. xylosus ES13, Lactobacillus fermentum ES15, and Streptococcus thermophylus ES17 demonstrated the successful extension of a unique calibration model, developed for S. xylosus ES13, to other strains that were differently shaped but growing in the same medium and fermentation conditions. NIR in-line was so versatile as to measure several biochemical parameters of different bacteria by means of slightly adapted models, avoiding a separate calibration for each strain.