Rapid calibration of near-infrared spectroscopic measurements of mammalian cell cultivations

Near-infrared (NIR) spectroscopy is a flexible method that can be employed to noninvasively monitor the concentrations of multiple nutrients and wastes in mammalian cell bioreactors. Development of suitable calibrations can be a labor- and time-intensive process that must be repeated when process conditions are altered significantly. To address this difficulty, we have produced a new approach for generating NIR spectroscopic calibrations that requires significantly less time compared with standard calibration schemes. This method reduces development time from the present level of several weeks to several hours. A small number of experimentally collected spectra serve as inputs to a computational procedure that yields a large number of simulated spectra, each containing both analyte-specific and analyte-independent information. Such simulated spectra may be employed as a calibration set for quantifying analytes in experimentally collected spectra. Spectroscopic measurements of the concentrations of five components (ammonia, glucose, glutamate, glutamine, and lactate) can be accomplished with levels of error similar to those obtained with full experimental calibrations. A key to this process is the utilization of random numbers, which randomizes the influence of natural variations, present in each experimentally collected spectrum, on the resultant composite spectrum. This approach may increase the feasibility of employing NIR spectroscopy to monitor bioreactors and other biological processes subjected to varying operating conditions.     

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     

Use of near infrared reflectance spectroscopy to predict nitrogen uptake by winter wheat within fields with high variability in organic matter

In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated. Soil samples were taken from unfertilized plots in one winter wheat field for three years (1997–1999) and in another winter wheat field nearby for one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally cross-validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average error of 12.1 to 15.4 kg N ha⁻¹. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations based on organic C alone had an error from 11.7 to 28.2 kg N ha⁻¹ and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR based calibrations worked better than the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7, respectively. Across fields, the corresponding difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration. It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models can be calibrated.     

In situ near infrared spectroscopy for analyte‐specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations

There are many challenges associated with in situ collection of near infrared (NIR) spectra in a fermentation broth, particularly for highly aerated and agitated fermentations with filamentous organisms. In this study, antibiotic fermentation by the filamentous bacterium Streptomyces coelicolor was used as a model process. Partial least squares (PLS) regression models were calibrated for glucose and ammonium based on NIR spectra collected in situ. To ensure that the models were calibrated based on analyte‐specific information, semisynthetic samples were used for model calibration in addition to data from standard batches. Thereby, part of the inherent correlation between the analytes could be eliminated. The set of semisynthetic samples were generated from fermentation broth from five separate fermentations to which different amounts of glucose, ammonium, and biomass were added. This method has previously been used off line but never before in situ. The use of semisynthetic samples along with validation on an independent batch provided a critical and realistic evaluation of analyte‐specific models based on in situ NIR spectroscopy. The prediction of glucose was highly satisfactory resulting in a RMSEP of 1.1 g/L. The prediction of ammonium based on NIR spectra collected in situ was not satisfactory. A comparison with models calibrated based on NIR spectra collected off line suggested that this is caused by signal attenuation in the optical fibers in the region above 2,000 nm; a region which contains important absorption bands for ammonium. For improved predictions of ammonium in situ, it is suggested to focus efforts on enhancing the signal in that particular region. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Dry matter production and boron concentrations of vegetative and reproductive tissues of canola and sunflower plants grown in nutrient solution

Prediction of nitrogen (N) mineralization is important for specifying the optimum rate of N fertilizer for flooded rice at the time of sowing. To develop a predictive test, soils (0–0.1 m) were sampled from 22 farms throughout the rice-growing region of southern Australia over a 4-year period. Near infrared reflectance (NIR) spectra of the soils were compared with sixteen biological and chemical soil tests for the prediction of N-uptake by rice plants from these soils in the field and glasshouse. The aim of the study was to develop a soil-NIR calibration as an accurate, rapid and economical mineralization test. Nitrogen uptake by field-grown and glasshouse-grown plants was poorly correlated (r = 0.30), even though significant NIR calibrations were developed with both. Since N uptake by rice in the field was affected by varying weather and management, the field calibration is probably spurious. The calibration of soil NIR spectra with N uptake by glasshouse plants was satisfactory, with a standard error (SE) of 13 kg ha^–1 over a range of 11 – 95 kg ha^–1, and a correlation between calculated and measured N uptake (r = 0.87, P<0.001). An even better soil-NIR calibration was found with N-mineralization after 21 days of anaerobic incubation (SE 16 mg kg^–1, range 52–175 mg kg^–1). Analysis of the soil spectra showed that similar wavelengths were correlated with both plant-N uptake and mineralization. NIR spectroscopy shows considerable potential to predict soil N mineralization, and may assist future fertiliser decision support.     

Comparative determination of polymorphs of indomethacin in powders and tablets by chemometrical near-infrared spectroscopy and X-ray powder diffractometry

The purpose of this research was to develop a rapid chemometrical method based on near-infrared (NIR) spectroscopy to determine indomethacin (IMC) polymorphic content in mixed pharmaceutical powder and tablets. Mixed powder samples with known polymorphic contents of forms α and γ were obtained from physical mixing of 50% of IMC standard polymorphic sample and 50% of excipient mixed powder sample consisting of lactose, corn starch, and hydroxypropyl-cellulose. The tablets were obtained by compressing the mixed powder at 245 MPa. X-ray powder diffraction profiles and NIR spectra were recorded for 6 kinds of standard materials with various polymorphic contents. The principal component regression analysis was performed based on normalized NIR spectra sets of mixed powder standard samples and tablets. The relationships between the actual and predicted polymorphic contents of form g in the mixed powder measured using x-ray powder diffraction and NIR spectroscopy show a straight line with a slope of 0.960 and 0.995, and correlation coefficient constants of 0.970 and 0.993, respectively. The predicted content values of unknown samples by x-ray powder diffraction and NIR spectroscopy were reproducible and in close agreement, but those by NIR spectroscopy had smaller SDs than those by x-ray powder diffraction. The results suggest that NIR spectroscopy provides a more accurate quantitative analysis of polymorphic content in pharmaceutical mixed powder and tablets than does conventional x-ray powder diffractometry.     

Monitoring Granulation Rate Processes Using Three PAT Tools in a Pilot-Scale Fluidized Bed

The purpose of this research was to analyze and compare the responses of three Process Analytical Technology (PAT) techniques applied simultaneously to monitor a pilot-scale fluidized bed granulation process. Real-time measurements using focused beam reflectance measurement (Lasentec FBRM) and near-infra red spectroscopy (Bruker NIR) were taken by inserting in-line probes into the fluidized bed. Non-intrusive acoustic emission measurements (Physical Acoustic AE) were performed by attaching piezoelectric sensors on the external wall of the fluidized bed. Powder samples were collected at regular intervals during the granulation process and characterized offline using laser diffraction, scanning electron microscopy, stereo-optical microscopy and loss on drying method. PAT data comprising chord length distribution and chord count (from FBRM), absorption spectra (from NIR) and average signal levels and counts (from AE) were compared with the particle properties measured using offline samples. All three PAT techniques were able to detect the three granulation regimes or rate processes (wetting and nucleation, consolidation and growth, breakage) to varying degrees of sensitivity. Being dependent on optical signals, the sensitivities of the FBRM and NIR techniques were susceptible to fouling on probe windows. The AE technique was sensitive to background fluidizing air flows and external interferences. The sensitivity, strengths and weaknesses of the PAT techniques examined may facilitate the selection of suitable PAT tools for process development and scale-up studies.     

Short Communication: The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain,using Iberian hams as an example

This communication assesses the use of a portable near infrared (NIR) instrument to measure quantitative (fatty acid profile) properties and qualitative (‘Premium’ and ‘Non-premium’) categories of individual Iberian pork carcasses at the slaughterhouse. Acorn-fed Iberian pigs have more unsaturated fats than pigs fed conventional compound feed. Recent advances in miniaturisation have led to a number of handheld NIR devices being developed, allowing processing decisions to be made earlier, significantly reducing time and costs. The most common methods used for assessing quality and authenticity of Iberian hams are analysis of the fatty acid composition of subcutaneous fat using gas chromatography and DNA analysis. In this study, NIR calibrations for fatty acids and classification as premium or non-premium ham, based on carcass fat measured in situ, were developed using a portable NIR spectrometer. The accuracy of the quantitative equations was evaluated through the standard error of cross validation or standard error of prediction of 0.84 for palmitic acid (C16:0), 0.94 for stearic acid (C18:0), 1.47 for oleic acid (C18:1) and 0.58 for linoleic acid (C18:2). Qualitative calibrations provided acceptable results, with up to 98% of samples (n = 234) correctly classified with probabilities ⩾0.9. Results indicated a portable NIR instrument has the potential to be used to measure quality and authenticity of Iberian pork carcasses.     

Influence of morphology on the near-infrared spectra of mycelial biomass and its implications in bioprocess monitoring

Bioprocesses that employ mycelial microorganisms are commercially important. The application of optical techniques for the measurement of biomass in such processes is limited by the morphological heterogeneity exhibited by the mycelial microorganism employed. We investigated the influence of morphology on the near-infrared (NIR) spectra of the biomass of Streptomyces fradiae, a filamentous microorganism, by studying the spectra of mycelial suspensions that were manipulated to generate a range of morphological forms. Computerized image analysis was used to characterize the morphological forms. Principal component analysis was used to assess the spectral variations and study correlations to the manipulated mycelial morphology. Although morphology was found to influence the near infrared transmittance spectra of biomass, the influence was less pronounced than in the visible region, the spectral information at longer wavelengths (1600-2350 nm) showing greater stability to morphological variations. Long-wave NIR spectral information is therefore likely to be more useful in estimating biomass in mycelial bioprocesses. Furthermore, the NIR reflectance spectra of dried biomass were found to show correlations to the morphological variations introduced, suggesting that NIR spectra may be useful in obtaining morphology related information.     

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.     

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.     

Diffuse Reflectance, Near-Infrared Spectroscopic Estimation of Sugarcane Lignocellulose Components—Effect of Sample Preparation and Calibration Approach

Near-infrared spectroscopic calibrations for sugarcane lignocellulose composition (ash, lignin, and structural carbohydrates) have been developed on a total dry weight and extractives-free dry weight basis. Reference spectra were measured from crude preparations of bagasse and fibrated sugarcane stalks. Spectral interference attributed to extractives in the crude preparations adversely affected the fitting of calibrations derived from extractives-free reference data. Extractives content varied between 5 and 68 % of total dry weight and depended on the nature of the sample preparation process. Applying DOSC (direct orthogonal signal correction) to the spectra circumvented the need to physically remove these extractives from the samples prior to spectroscopic measurement. DOSC enhanced the correlation between the extractives-free reference data and the measured absorbances. Meanwhile, the correlation between the extractives reference data and the measured absorbances was attenuated. This indicated that DOSC removed the spectral contribution of the extractives. This mathematical treatment of the spectra improved the cross-validation performance of the calibrations for extractives-free lignin and some structural carbohydrates (arabinan, glucan, and xylan). The DOSC-based models explained greater than 90 % of the variance in the calibration data and led to relative standard errors of prediction under cross-validation and independent testing that were less than 10 %. Although DOSC also improved the cross-validation performance of ash and galactan calibrations, their relative standard errors under independent testing were high (>10 %). DOSC was also advantageous for total dry weight calibrations as it reduced the number of latent variables employed in the models.     

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.     

Near infrared spectroscopy as a tool for the determination of eumelanin in human hair

Eumelanins are brown-black pigments present in the hair and in the epidermis which are acknowledged as protection factors against cell damage caused by ultraviolet radiation. The quantity of eumelanin present in hair has recently been put forward as a means of identifying subjects with a higher risk of skin tumours. For epidemiological studies, chromatographic methods of determining pyrrole-2,3,5-tricarboxylic acid (PTCA; the principal marker of eumelanin) are long, laborious and unsuitable for screening large populations. We suggest near infrared (NIR) spectroscopy as an alternative method of analysing eumelanin in hair samples. PCTA was determined on 93 samples of hair by means of oxidizing with hydrogen peroxide in a basic environment followed by chromatographic separation. The same 93 samples were then subjected to NIR spectrophotometric analysis. The spectra were obtained in reflectance mode on hair samples which had not undergone any preliminary treatment, but had simply been pressed and placed on the measuring window of the spectrophotometer. The PTCA values obtained by means of HPLC were correlated with the near infrared spectrum of the respective samples. A correlation between the PTCA values obtained by means of HPLC and the PTCA values obtained from an analysis of the spectra was obtained using the principal component regression (PCR) algorithm. The correlation obtained has a coefficient of regression (R(2)) of 0.89 and a standard error of prediction (SEP) of 13.8 for a mean value of 108.6 ng PTCA/mg hair. Some considerations about the accuracy of the obtained correlation and the main sources of error are made and some validation results are shown.     

Measurement of hemoglobin in whole blood using a partial least squares regression model with selected second derivative near infrared transmission spectral signals

In this work, we propose a signal selection procedure for determination of hemoglobin (Hb) concentration in whole blood using near infrared (NIR) transmission spectral signals. A dataset of 190 whole blood NIR transmission spectra with reference Hb concentrations was used to evaluate the method. Spectral signals were selected based on the squared correlation coefficient (R(2)) between the signal and the Hb concentration. An improved uninformative variable elimination (UVE) procedure was performed to remove redundant signals from the primary selected signal set. A partial least squares (PLS) regression model was built with the final selected signals and the corresponding Hb concentrations. The results indicate that the proposed method is effective at increasing the predictive power of the NIR-PLS spectral model for determining Hb concentration in whole blood samples.     

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.     

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.     

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA×) and Limousin (LIM×) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA× and 106 LIM× were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P < 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA× meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM× meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R(2) (SE(CV), mg/100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM× samples (R(2) = 0.12 to 0.62; SECV = 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA× beef samples. In particular for LIM×, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.     

The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods

The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400–2500 nm) as such a method was investigated. Vis–NIR calibrations were performed for organic carbon (C_org) and total N (N_tot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r²=0.65) and 2.7 (r²=0.87) were achieved. Apparent differences in Vis–NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis–NIR analyses. Comparative predictions of C_org and N_tot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH₄-N (N_hotKCl) values as predictors. Likewise, the reference values of C_org and N_tot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis–NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis–NIR method provided promising predictions of variables important for the soil N supply.     

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.