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.     

Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms

The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr ² of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr ² of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q₁₂₀ (percentage dissolved in 120 minutes), respectively. Published: September 20, 2005 The opinions expressed in this paper are of the authors' personal views. They do not necessarily reflect the views or policies of the FDA.     

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R² = 0.99), 0.13 MJ/kg (R² = 0.99), 0.42% (R² = 0.58), and 0.57% (R² = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.     

Monitoring of an antigen manufacturing process

Fluorescence spectroscopy in combination with multivariate statistical methods was employed as a tool for monitoring the manufacturing process of pertactin (PRN), one of the virulence factors of Bordetella pertussis utilized in whopping cough vaccines. Fluorophores such as amino acids and co-enzymes were detected throughout the process. The fluorescence data collected at different stages of the fermentation and purification process were treated employing principal component analysis (PCA). Through PCA, it was feasible to identify sources of variability in PRN production. Then, partial least square (PLS) was employed to correlate the fluorescence spectra obtained from pure PRN samples and the final protein content measured by a Kjeldahl test from these samples. In view that a statistically significant correlation was found between fluorescence and PRN levels, this approach could be further used as a method to predict the final protein content.     

Analytical monitoring of alcoholic fermentation using NIR spectroscopy

Alcoholic fermentation under Saccharomyces cerevisiae yeasts is governed largely by glucose uptake, biomass formation, ethanol and glycerin production, and acidification. In this work, PLS calibration models were developed with a view to determining these analytical parameters from near infrared spectra and analytical data provided by the corresponding reference methods. The models were applied to a set of samples obtained from various fermentation processes. The glucose, ethanol, and biomass values predicted by the models exhibited a high correlation with those provided by the reference method.     

Application of dielectric spectroscopy for monitoring high cell density in monoclonal antibody producing CHO cell cultivations

The application of dielectric spectroscopy was frequently investigated as an on-line cell culture monitoring tool; however, it still requires supportive data and experience in order to become a robust technique. In this study, dielectric spectroscopy was used to predict viable cell density (VCD) at industrially relevant high levels in concentrated fed-batch culture of Chinese hamster ovary cells producing a monoclonal antibody for pharmaceutical purposes. For on-line dielectric spectroscopy measurements, capacitance was scanned within a wide range of frequency values (100–19,490 kHz) in six parallel cell cultivation batches. Prior to detailed mathematical analysis of the collected data, principal component analysis (PCA) was applied to compare dielectric behavior of the cultivations. PCA analysis resulted in detecting measurement disturbances. By using the measured spectroscopic data, partial least squares regression (PLS), Cole–Cole, and linear modeling were applied and compared in order to predict VCD. The Cole–Cole and the PLS model provided reliable prediction over the entire cultivation including both the early and decline phases of cell growth, while the linear model failed to estimate VCD in the later, declining cultivation phase. In regards to the measurement error sensitivity, remarkable differences were shown among PLS, Cole–Cole, and linear modeling. VCD prediction accuracy could be improved in the runs with measurement disturbances by first derivative pre-treatment in PLS and by parameter optimization of the Cole–Cole modeling.     

Multivariate Analysis of Phenol in Freeze-Dried and Spray-Dried Insulin Formulations by NIR and FTIR

Dehydration is a commonly used method to stabilise protein formulations. Upon dehydration, there is a significant risk the composition of the formulation will change especially if the protein formulation contains volatile compounds. Phenol is often used as excipient in insulin formulations, stabilising the insulin hexamer by changing the secondary structure. We have previously shown that it is possible to maintain this structural change after drying. The aim of this study was to evaluate the residual phenol content in spray-dried and freeze-dried insulin formulations by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy using multivariate data analysis. A principal component analysis (PCA) and partial least squares (PLS) projections were used to analyse spectral data. After drying, there was a difference between the two drying methods in the phenol/insulin ratio and the water content of the dried samples. The spray-dried samples contained more water and less phenol compared with the freeze-dried samples. For the FTIR spectra, the best model used one PLS component to describe the phenol/insulin ratio in the powders, and was based on the second derivative pre-treated spectra in the 850–650 cm⁻¹ region. The best PLS model based on the NIR spectra utilised three PLS components to describe the phenol/insulin ratio and was based on the standard normal variate transformed spectra in the 6,200–5,800 cm⁻¹ region. The root mean square error of cross validation was 0.69% and 0.60% (w/w) for the models based on the FTIR and NIR spectra, respectively. In general, both methods were suitable for phenol quantification in dried phenol/insulin samples.     

用氨基葡萄糖含量的测定值间接表示红曲菌固态发酵过程中生物量的研究

【目的】为准确快速地了解紫色红曲菌固态发酵中生物量的变化,【方法】采用理化方法测定菌体量和氨基葡萄糖含量,研究了不同培养时间、培养基组成、培养方式下菌体量与氨基葡萄糖含量的关系,建立生物量和氨基葡萄糖含量的换算关系式;构建关联该菌固态培养物近红外光谱数据与实测氨基葡萄糖含量的PLS模型。【结果】建立了可通过近红外光谱法测定氨基葡萄糖来快速预测固态发酵生物量的方法,其中最优近红外模型的校正集内部交叉验证均方根误差(RMSECV)为0.209 4,预测集相关系数(Rp)和均方根误差(RMSEP)分别为0.993 4和0.217 3;同时利用所建的换算关系式也大大提高了生物量计算的准确性。【结论】基于所建立的生物量和氨基葡萄糖的换算关系式,利用近红外光谱法可以快速并且较准确地测定紫色红曲菌固态发酵过程中生物量的变化。     

Evaluation of spectrofluorometry as a tool for estimation in fed-batch fermentations

Native culture fluorescence was investigated as an additional source of information for predicting biomass and glucose concentrations in a fed-batch fermentation of Alcaligenes eutrophus. Partial least squares (PLS) regression and a feed forward neural network (FFNN) coupled with principle component analysis (PCA) were each used to model the kinetics of the fermentation. Data from three fermentations was combined to form a training set for model calibration and data from a fourth fermentation was used as the testing set. The fluorescent soft-sensors were compared with a previously developed feed forward neural network soft-sensor model which used oxygen uptake rate (OUR), carbon dioxide evolution rate (CER), aeration rate, feed rate, and fermentor volume to estimate biomass and glucose concentrations. The best model performance for predicting both biomass and glucose concentrations was achieved using the native fluorescence-based models. Real data predictions of the biomass concentration in the testing set were obtained using both the PLS and FFNN PCA modeling utilizing fluorescence measurements plus the rate of change of the fluorescence measurements. Accurate predictions of the glucose concentration in the testing set were obtained using the FFNN PCA modeling technique utilizing the rate of change of the fluorescence measurements. Substrate exhaustion was indicated qualitatively by a first-order PLS model utilizing the rate of change of fluorescence measurements. These results indicate that native culture fluorescence shows promise for providing additional valuable information to enhance predictive modeling which cannot be extracted from other easily acquired measurements.     

Feasibility of Spectroscopic Characterization of Algal Lipids: Chemometric Correlation of NIR and FTIR Spectra with Exogenous Lipids in Algal Biomass

A large number of algal biofuels projects rely on a lipid screening technique for selecting a particular algal strain with which to work. We have developed a multivariate calibration model for predicting the levels of spiked neutral and polar lipids in microalgae, based on infrared (both near-infrared (NIR) and Fourier transform infrared (FTIR)) spectroscopy. The advantage of an infrared spectroscopic technique over traditional chemical methods is the direct, fast, and non-destructive nature of the screening method. This calibration model provides a fast and high-throughput method for determining lipid content, providing an alternative to laborious traditional wet chemical methods. We present data of a study based on nine levels of exogenous lipid spikes (between 1% and 3% (w/w)) of trilaurin as a triglyceride and phosphatidylcholine as a phospholipid model compound in lyophilized algal biomass. We used a chemometric approach to corrrelate the main spectral changes upon increasing phospholipid and triglyceride content in algal biomass collected from single species. A multivariate partial least squares (PLS) calibration model was built and improved upon with the addition of multiple species to the dataset. Our results show that NIR and FTIR spectra of biomass from four species can be used to accurately predict the levels of exogenously added lipids. It appears that the cross-species verification of the predictions is more accurate with the NIR models (R ²?=?0.969 and 0.951 and RMECV?=?0.182 and 0.227% for trilaurin and phosphatidylcholine spike respectively), compared with FTIR (R ²?=?0.907 and 0.464 and RMECV?=?0.302 and 0.767% for trilaurin and phosphatidylcholine spike, respectively). A fast high-throughput spectroscopic lipid fingerprinting method can be applied in a multitude of screening efforts that are ongoing in the microalgal research community.     

On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation

In situ near-infrared (NIR) spectroscopy and in-line electronic nose (EN) mapping were used to monitor and control a cholera-toxin producing Vibrio cholerae fed-batch cultivation carried out with a laboratory method as well as with a production method. Prediction models for biomass, glucose and acetate using NIR spectroscopy were developed based on spectral identification and partial-least squares (PLS) regression resulting in high correlation to reference data (standard errors of prediction for biomass, glucose and acetate were 0.20 gl(-1), 0.26 gl(-1) and 0.28 gl(-1)). A compensation algorithm for aerated bioreactor disturbances was integrated in the model computation, which in particular improved the prediction by the biomass model. First, the NIR data were applied together with EN in-line data selected by principal component analysis (PCA) for generating a trajectory representation of the fed-batch cultivation. A correlation between the culture progression and EN signals was demonstrated, which proved to be beneficial in monitoring the culture quality. It was shown that a deviation from a normal cultivation behavior could easily be recognized and that the trajectory was able to alarm a bacterial contamination. Second, the NIR data indicated the potential of predicting the concentration of formed cholera toxin with a model prediction error of 0.020 gl(-1). Third, the on-line biomass prediction based on the NIR model was used to control the overflow metabolism acetate formation of the V. cholerae culture. The controller compared actual specific growth rate as estimated from the prediction with the critical acetate formation growth rate, and from that difference adjusted the glucose feed rate.     

Rapid near infrared spectroscopy for prediction of enzymatic hydrolysis of corn bran after various pretreatments

Efficient generation of a fermentable hydrolysate is a primary requirement in the utilization of fibrous plant biomass as feedstocks in bioethanol processes. The first biomass conversion step usually involves a hydrothermal pretreatment before enzymatic hydrolysis. The purpose of the pretreatment step is to increase the responsivity of the substrate to enzymatic attack and the type of pretreatment affects the enzymatic conversion efficiency. Destarched corn bran is a fibrous, heteroxylan-rich side-stream from the starch industry which may be used as a feedstock for bioethanol production or as a source of xylose for other purposes. In the present study we demonstrate the use of diffuse reflectance near infrared spectroscopy (NIR) as a rapid and non-destructive analytical tool for evaluation of pretreatment effects on destarched corn bran. NIR was used to achieve classification between 43 differently pretreated corn bran samples using principal component analysis (PCA) and hierarchal clustering algorithms. Quantification of the enzymatically released monosaccharides by HPLC was used to design multivariate calibration models (biPLS) on the NIR spectra. The models could predict the enzymatic release of different levels of arabinose, xylose and glucose from all the differently pretreated destarched corn bran samples. The present study also demonstrates a generic, non-destructive solution to determine the enzymatic monosaccharide release from polymers in biomass side-streams, thereby potentially replacing the cumbersome HPLC analysis.     

A multivariate calibration procedure for UV/VIS spectrometric monitoring of BHK‐21 cell metabolism and growth

Monitoring mammalian cell culture with UV–vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off‐line UV–vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK‐21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.50 ± 0.10 mM and 2.21 ± 0.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV–vis at‐line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed‐batch feeding schemes. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:241–248, 2014     

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.     

Discriminating pulmonary hypertension caused by monocrotaline toxicity from chronic hypoxia by near-infrared spectroscopy and multivariate methods of analysis

A new method has been developed for the determination of tissue pathology caused by chronic hypoxia and monocrotaline toxicity. The method is based on the use of near-infrared (NIR) spectrophotometry to measure spectra of lung tissue from normal chronic hypoxia (CH) and monocrotaline (MCT) models of pulmonary hypertension (PH), followed by analysis using multivariate methods, that is, principal component analysis (PCA) and partial least squares (PLS). Synergistic use of NIR with the PCA/PLS method makes it possible, for the first time, not only to divide different lung tissue samples into their respective groups (normal, CH, and MCT) but also to gain insight into mechanisms of PH caused by CH and MCT toxicity. Specifically, MCT metabolites and other hypertensive conditions are known to produce subtle and minor chemical changes in the compositions of tissue (e.g., proteins, carbohydrates, lipids). Although these changes were detected by the NIR technique, they were too small to be discerned through visual inspection of the spectra. However, they can be accurately classified and properly assigned by the PCA/PLS method. The fact that different tissue types can be accurately divided into their corresponding groups by the NIR and PCA/PLS methods suggests that chemical alterations and mechanisms of pulmonary vascular remodeling and PH induced by MCT are different from those induced by CH.     

Vibrational spectroscopy-based quantification of liver steatosis

The liver plays a central role in lipid metabolism, and abnormal lipid accumulation in the liver is a key feature of Non-Alcoholic Fatty Liver Disease. In experimental studies, quantification of liver steatosis is commonly based on lipids staining or biochemical analysis. Here, we present a spectroscopic approach for quantitative analysis of the lipid content in the freeze-dried liver. The method is based on vibrational spectroscopy (Raman and infrared) measurements applied for Partial Least Squares (PLS) regression modeling. The obtained PLS models show a good correlation of the spectroscopic data with the reference histological evaluation of steatosis based on Oil Red O (ORO)-stained images of liver cross sections. Vibrational spectroscopy with PLS-based modeling described here represents a useful approach for the fast assessment of the liver steatosis in a small sample of freeze-dried liver tissue. In conclusion, our work demonstrates the easy-to-use method that can be applied in laboratory routine as a beneficial alternative to the established ORO staining.     

Chemometric prediction of alginate monomer composition: A comparative spectroscopic study using IR, Raman, NIR and NMR

The potential of using infrared (IR), Raman and near infrared (NIR) spectroscopy combined with chemometrics for reliable and rapid determination of the ratio of mannuronic and guluronic acid (M/G ratio) in commercial sodium alginate powders has been investigated. The reference method for quantification of the M/G ratio was solution-state ¹H nuclear magnetic resonance (NMR) spectroscopy. For a set of 100 commercial alginate powders with a M/G ratio range of 0.5–2.1 quantitative calibrations using partial least squares regression (PLSR) were developed and compared for the three spectroscopic methods. All three spectroscopic methods yielded models with prediction errors (RMSEP) of 0.08 and correlation coefficients between 0.96 and 0.97. However, the model based on extended inverted signal corrected (EISC) Raman spectra stood out by only using one PLS component for the prediction. The results are comparable to that of the experimental error of the reference method estimated to be between 0.01 and 0.08.     

Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to probe the potential of Fourier transform near-infrared (FT-NIR) techniques in qualitatively classifying separated cornstover fractions and in quantitatively analyzing chemical compositions of cornstover by developing calibration models to predict chemical compositions of cornstover based on FT-NIR spectra. Large variations of cornstover chemical composition for wide calibration ranges, which is required by a reliable calibration model, were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Different botanic fractions, having total saccharide content in descending order, are husk, sheath, pith, rind, leaf, and node. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover fractions, and partial least square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was successfully demonstrated in classifying botanical fractions of cornstover. The developed PLS model yielded root mean square error of prediction (RMSEP %w/w) of 0.92, 1.03, 0.17, 0.27, 0.21, 1.12, and 0.57 for glucan, xylan, galactan, arabinan, mannan, lignin, and ash, respectively. The results showed the potential of FT-NIR techniques in combination with multivariate analysis to be utilized by biomass feedstock suppliers, bioethanol manufacturers, and bio-power producers in order to better manage bioenergy feedstocks and enhance bioconversion.     

Using FTIR to predict saccharification from enzymatic hydrolysis of alkali-pretreated biomasses

Fourier transform infrared, attenuated total reflectance (FTIR-ATR) spectroscopy combined with partial least squares (PLS) regression accurately predicted 72-h glucose and xylose conversions (g sugars/100 g potential sugars) and yields (g sugars/100 g dry solids) from cellulase-mediated hydrolysis of alkali-pretreated lignocellulose. Six plant biomasses that represent a variety of potential biofuel feedstocks--two switchgrass cultivars, big bluestem grass, a low-impact, high-diversity mixture of 32 species of prairie biomasses, mixed hardwood, and corn stover--were subjected to four levels of low-temperature NaOH pretreatment to produce 24 samples with a wide range of potential digestibility. PLS models were constructed by correlating FTIR spectra of pretreated samples to measured values of gluose and xylose conversions and yields. Variable selection, based on 90% confidence intervals of regression-coefficient matrices, improved the predictive ability of the models, while simplifying them considerably. Final models predicted sugar conversions with coefficient of determination for cross-validation (Q(2)) values of 0.90 for glucose and 0.89 for xylose, and sugar yields with Q(2) values of 0.92 for glucose and 0.91 for xylose. The sugar-yield models are noteworthy for their ability to predict enzymatic saccharification per mass dry solids without a priori knowledge of the composition of the solids. All peaks retained in the final regression coefficient matrices were previously assigned to chemical bonds and functional groups in lignocellulose, demonstrating that the models were based on real chemical information. This study demonstrates that FTIR spectroscopy combined with PLS regression can be used to rapidly estimate sugar conversions and yields from enzymatic hydrolysis of pretreated plant biomass.     

Exploring the Use of Near Infrared (NIR) Reflectance Spectroscopy to Predict Starch Pasting Properties in Whole Grain Barley

This study aimed to evaluate the ability of using near infrared reflectance (NIR) spectroscopy to predict parameters generated by the rapid visco analyser (RVA) in whole grain barley samples to further study starch pasting characteristics in a breeding program. A total of 130 whole grain barley samples from the University of Adelaide germplasm collection, harvested over three seasons (2009, 2010 and 2011) were analysed using both NIR and RVA instruments and calibrations developed using partial least squares (PLS) regression. The coefficient of determination in cross validation (R ²) and the standard error in cross validation (SECV) were 0.88 [SECV?=?477.5 (RVU?=?rapid visco units)] for peak viscosity (PV), 0.82 (SECV?=?635.5 RVU) for trough (THR), 0.92 (SECV?=?190.4 RVU) for breakdown (BKD), 0.61 (SECV?=?151.1 RVU) for setback (SET), 0.84 (SECV?=?698.0 RVU) for final viscosity (FV), 0.70 (SECV?=?0.54 s) for time to peak (TTP) and 0.36 (SECV?=?2.2 min) for pasting temperature (PT). We have demonstrated that NIR spectroscopy shows promise as a rapid, non-destructive method to measure PV in whole grain barley. In this context, NIR spectroscopy has the potential to significantly reduce analytical time and cost for screening novel lines for starch properties for pasting properties.