Near-infrared Predictions Research Articles

Nondestructive Determination of Tocopherol and Tocotrienol in Vitamin E Powder Using Near- and Mid-Infrared Spectroscopy.

Vitamin E is an essential nutrient, but its poor water solubility limits food and pharmaceutical applications. The usability of vitamin E can be enhanced via modification methods such as encapsulation, which transforms the physical state of vitamin E from a liquid to a powder. This study examined the efficacy of near-infrared (NIR) and mid-infrared (MIR) spectroscopy in identifying and predicting various vitamin E derivatives in vitamin E-encapsulated powder (VEP). An MIR analysis revealed the fundamental C-H vibrations of vitamin E in the range of 2700-3250 cm-1, whereas an NIR analysis provided information about the corresponding combination, first, and second overtones in the range of 4000-9000 cm-1. The MIR and NIR data were analyzed using a principal component analysis to characterize the VEP. Partial least squares (PLS) regression was applied to predict the content of individual vitamin E derivatives. PLS cross-validation revealed that NIR analysis provides more reliable predictive accuracy and precision for the contents of vitamin E derivatives, achieving a higher coefficient of determination for prediction (Q2) (0.92-0.99) than MIR analysis (0.20-0.85). For test set validation, the NIR predictions exhibited a significant level of accuracy, as indicated by a high ratio of prediction to deviation (RPD) and Q2. Furthermore, the PLS models developed using the NIR data had statistically significant predictive performance, with a high RPD (1.54-3.92) and Q2 (0.66-0.94). Thus, NIR spectroscopy is a valuable nondestructive technique for analyzing vitamin E samples, while MIR spectroscopy serves as a useful method for confirming its presence.

New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9-tetrahydrocannabinol

The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ9-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable.The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes.Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.

In-line monitoring of low drug concentration of flowing powders in a new sampler device.

A novel sampler device for flowing powders was tested to quantify drug concentrations as low as 0.76% w/w in pharmaceutical powder blends. The sampler device was developed based on the powder flow behavior within a tablet press feed frame, following the principles laid down in the Theory of Sampling. Two Near-Infrared (NIR) spectroscopic calibration models were developed with powder blends that varied from 0.52 to 2.52% w/w and 1.51-4.52% w/w. The calibration models were able to determine caffeine concentration in test set blends with root mean square error of predictions and bias below 0.1% w/w. Samples were collected from the sampler device and analyzed by ultraviolet-visible (UV-Vis) to determine the caffeine concentration. A high agreement between the in-line NIR predictions and the sampled UV-Vis results was found. The paddle wheel speed in the sampler can be varied up to ±10% without affecting NIR predictions; however, the models did not respond adequately to a 25% increase in this speed. Variographic analysis showed that the sampler device may quantify low drug concentrations with nugget effects below 0.0050 (%w/w)2. This study demonstrate that the sampler device may handle throughputs up to 45kg/h, without significantly affecting the physical properties of powder blends.

In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

As unsafe components in herbal medicine (HM), saccharides can affect not only the drug appearance and stabilization, but also the drug efficacy and safety. The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using near-infrared (NIR) spectroscopy. NIR spectra in the 4000–10,000-cm[Formula: see text] wavelength range are acquired in situ using a transflectance probe. These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation. Calibration models based on partial least squares (PLS) regression have been developed for the three saccharide impurities, namely glucose, fructose, and sucrose. Model errors are estimated as the root-mean-square errors of cross-validation (RMSECVs) of internal validation and root-mean-square errors of prediction (RMSEPs) of external validation. The RMSECV values of glucose, fructose, and sucrose were 1.150, 1.535, and 3.067[Formula: see text]mg[Formula: see text]mL[Formula: see text], and the RMSEP values were 0.711, 1.547, and 3.740[Formula: see text][Formula: see text], respectively. The correlation coefficients [Formula: see text] between the NIR predictive and the reference measurement values were all above 0.94. Furthermore, NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation. The results demonstrate that, as an alternative process analytical technology (PAT) tool for monitoring batch alcohol precipitation processes, NIR spectroscopy is advantageous for both efficient determination of quality characteristics (fast, in situ, and requiring no toxic reagents) and process stability, and evaluating the repeatability.

Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy

Spatially-resolved spectroscopy (SRS) enables better interrogation of tissue properties at different depths, and it thus has the potential for enhancing quality assessment of horticultural products like tomato, which are heterogeneous in structure and chemical composition. This research was aimed at assessing quality of tomato fruit by using a newly developed SRS system with 30 detection optic fibers covering the wavelength range of 550–1650 nm and comparing its performance with two conventional single-point (SP) spectroscopic instruments covering the visible and shortwave near-infrared (Vis/SWNIR) (400–1100 nm) and near-infrared (NIR) (900–1300 nm) regions, respectively. Spatially-resolved (SR) spectra and SP interactance spectra were acquired for 600 ‘Sun Bright’ tomato fruit. Partial least squares (PLS) models for individual SR spectra and their combinations and for SP Vis/SWNIR and NIR spectra were developed for prediction of soluble solids content (SSC) and pH. Results showed that SSC and pH predictions by SRS varied depending on the light source-detector distance, with the correlation coefficient of prediction (rp) ranging 0.608–0.791 and 0.688–0.800, respectively. Combinations of two or more SR spectra resulted in better, more consistent SSC and pH predictions. SR predictions of pH (rp = 0.819) were better than for SP Vis/SWNIR (rp = 0.743) and NIR (rp = 0.741) predictions, whereas SR predictions of SSC (rp = 0.800) were comparable to SP NIR predictions (rp = 0.810) but better than SP Vis/SWNIR predictions (rp = 0.729). This research showed that owning to its ability of acquiring spatially-resolved spectral information, the SRS technique has advantages over conventional SP spectroscopy for enhancing quality assessment of tomatoes.

In line monitoring of the powder flow behavior and drug content in a Fette 3090 feed frame at different operating conditions using Near Infrared spectroscopy

Near infrared (NIR) spectroscopy was used to determine the drug concentration in 3% (w/w) acetaminophen blends within the complex flow regime of the tablet press feed frame just before tablet compaction. NIR spectra also provided valuable information on the powder flow behavior within the feed frame and were used to track when a process enters or leaves the steady state. A partial least squares regression calibration model was developed with powder mixtures that varied from 1.5 to 4.5% (w/w) by obtaining 135 spectra after steady state for each concentration while the feed frame and die disc operated at 30.5 revolutions per minute (rpm). The calibration model determined drug concentration in validation blends with a root mean square error of prediction and bias below 0.1% (w/w). The robustness of the NIR calibration model was evaluated by determining the effect of variation on the operating conditions (paddle wheel speed and die disc speed) on NIR predictions. This work found that the paddle wheel speed can be increased up to 30% and the die disc speed decrease 10% without affecting NIR predictions. The results demonstrated that paddle wheel speed has a significant effect on the wave powder behavior (frequency and amplitude) but does not have significant effect on the mass hold-up within feed frame. The die disc speed does not affect wave powder behavior but affects significantly the mass hold-up inside the feed frame. This information can be used to reduce the tablet weight variability and ensure that this critical attribute is met.

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

Evaluation of Analytical and Sampling Errors in the Prediction of the Active Pharmaceutical Ingredient Concentration in Blends From a Continuous Manufacturing Process

A near-infrared (NIR) spectroscopic method was developed for real time analysis of the active pharmaceutical ingredient (API) in blends from a continuous manufacturing process. The sampling and analytical errors of these determinations were estimated through variographic analysis. Thirty-three calibration blends were prepared in laboratory scale equipment with a concentration range spanning from 70 to 130% of API target concentration. The NIR calibration model was validated using three independent validation sets (prepared in laboratory and pilot plant facilities and in the CM equipment). Real-time NIR spectra were obtained with an interface where three NIR spectrometers monitored the CM process. A variographic study was performed with the NIR predictions of drug concentration in blends. A total of 1800 NIR spectra were obtained throughout a CM run that lasted 2.5 h. Two NIR spectrometers (M1 and M2) monitored the CM run while located in positions b-1 and b-3 of the sensing interface. These two positions yielded very similar results. The average NIR predictions for blends were 101.67% LC for the first run using spectrometer M1 and 103.60% LC with M2. The second run provided an average NIR prediction of 101.19% LC with M1 and 103.16% LC with M2. The average drug concentration in tablets was 100.63% LC for the first run and 100.42% LC for the second run. Variograms showed a low sill and a flat, stable variogram demonstrating good mixing of the blend. The CM process provided tablets with excellent content uniformity. The sampling and analytical errors and the true process variation were easily discerned through variographic analysis.

Determination of naphtha composition by near infrared spectroscopy and multivariate regression to control steam cracker processes

Naphtha is an important feedstock for the petrochemical industry and accurate measurement of its detailed composition is essential for the optimization of pyrolysis processes. In the present work, the suitability of using near infrared (NIR) for the determination of 38 parameters used to characterize naphtha was evaluated: pooled composition (PIONA); specific composition groups (C3 paraffins; C4–C12 n-paraffins; C4–C12 iso-paraffins; C5–C7+ naphthenics; C8–C10 aromatics; C4–C6 olefins) and individual components (toluene, benzene, iso-pentane, cyclopentane, methyl cyclopentane; cyclohexane, xylenes, 1,3 butadiene and isoprene). Besides the usual comparison against reference methods, NIR predictions were also evaluated for their ultimate use in determining the coil outlet temperature (COT) for process control. 28 models for the naphtha parameters presented root mean square error (RMSEP) values lower or close to the reproducibility of the reference method and exhibited satisfactory correlation values (r2) when compared with r2max. Among the ten remaining parameters, two (1,3 butadiene and isoprene) presented very low concentrations and it was not possible to obtain a suitable model. Eight parameters had RMSEP values higher than the intralaboratory reproducibility and/or r2 values lower than expected. Despite this, the complete naphtha composition obtained from NIR prediction was demonstrated to be quite suitable for COT simulation, providing accurate COT values compared to the values estimated from the reference method.

Online monitoring of P(3HB) produced from used cooking oil with near-infrared spectroscopy

Online monitoring process for the production of polyhydroxyalkanoates (PHA), using cooking oil (UCO) as the sole carbon source and Cupriavidus necator, was developed. A batch reactor was operated and hydroxybutyrate homopolymer was obtained. The biomass reached a maximum concentration of 11.6±1.7gL−1 with a polymer content of 63±10.7% (w/w). The yield of product on substrate was 0.77±0.04gg−1. Near-infrared (NIR) spectroscopy was used for online monitoring of the fermentation, using a transflectance probe. Partial least squares regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18, 2.37 and 1.58gL−1 for biomass, UCO and PHA, respectively, which indicate the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control.

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models.

Near Infrared Method Development for a Continuous Manufacturing Blending Process

This study describes the validation of a near infrared spectroscopic method for monitoring a continuous manufacturing system. The achievement of this goal requires determining the near infrared (NIR) method’s accuracy, precision, obtaining an estimate of the sample volume analyzed, and the frequency with which measurements should be obtained. Five calibration blends were prepared spanning a concentration range from 7 to 13 % w/w ibuprofen to prepare a partial least squares (PLS) calibration model for a five component formulation. NIR spectra were obtained after powders passed through feeders and a custom-made tumble mixer. The calibration model’s precision and accuracy were determined with the prediction of three validation blends. NIR concentration predictions obtained from spectra collected during the validation runs showed relative standard errors of predictions of 2.8 % at target concentration with standard deviations NMT 0.2 % w/w. At certain time points, blend samples coming out of the blender were collected for UV analysis. Results from the NIR and UV analysis were comparable with the largest difference being 0.36 % w/w between the methods. A continuous blending process was monitored for 3 min after steady-state conditions were achieved. All individual predictions were within 3 standard deviations of the average reference value. The standard deviation for the NIR concentration predictions was 0.49 % w/w. The use of the standard normal variate (SNV) transform significantly reduced the effect of differences in powder flow on the NIR predictions. The use of variograms provided valuable insight into the frequency of measurements needed for the continuous manufacturing system. Additional research is needed to investigate the differences observed in the NIR and UV results for the continuous manufacturing run.

Quality Prediction of Kiwifruit Based on Near Infrared Spectroscopy

To establish the standard of ripe kiwifruit sorting, near infrared (NIR) spectroscopy was performed on kiwifruit sampled from three farms. Destructive measurements of flesh firmness, soluble solids content (SSC), and acidity were performed and compared to measurement using NIR reflectance spectrums from 408 to 2,492 nm. NIR predictions of those quality factors were calculated using the modified partial least square regression method. Flesh firmness was predicted with a standard error of prediction (SEP) of 3.32 N and with a correlation coefficient (R 2 ) of 0.88. SSC was predicted with SEP of 0.49 ˚Brix and with R 2 of 0.98. Acidity was predicted with SEP of 0.28% and with R 2 of 0.91. Kiwifruit ripened at 20℃ for 15 days showed uneven qualities with normal distribution. Considering the SEP of each parameter, kiwifruit after ripening treatment could be non-destructively predicted their qualities and sorted by flesh firmness or soluble solids content through NIR prediction.

Development of a Process Analytical Technology (PAT) for in-line monitoring of film thickness and mass of coating materials during a pan coating operation

The aim of this study was to perform in-line Near Infrared (NIR) measurements inside a pan coater to monitor a coating operation in real-time, by predicting the increases in mass of coating materials and coating thickness. A polymer combination of ethylcellulose/poly(vinyl-alcohol)–poly(ethylene-glycol) graft copolymer was used as functional aqueous coating. Coated tablets were sampled at regular intervals during the coating operation, then subjected to either simple and fast weighing ( n = 50) or accurate and non-destructive Terahertz Pulsed Imaging (TPI) measurements ( n = 3). Off-line NIR spectra analysis revealed that the coating operation could efficiently be controlled by focusing on two distinct NIR regions, related to absorption bands of ethylcellulose. Principal component analysis of in-line NIR spectra gave a clear classification of the collected coated tablets. Real-time quantitative monitoring of the coating operation was successfully performed from partial least square calibration models built using either TPI or weighing as reference method. Coating thicknesses as well as mass of coating materials used as primary values provided accurate NIR predictions. A comparison study demonstrated that both reference methods led to reliable and accurate real-time monitoring of the coating operation. This work demonstrated that in-line NIR measurements associated with multivariate analyses can be implemented to monitor in real-time a pan coating operation in order to fulfil the expectations of ICH Q8 guideline on pharmaceutical development, especially in terms of PAT control strategy and reduced end-product testing.

Construction of predictive models for gentamicin contents in aqueous solution using specific near infrared spectral regions

Component control is a key step of the quality control process in gentamicin (GM) production. The near infrared (NIR) method allows the rapid analysis of various components for the on-line control in the production process. In this study, we selected specific NIR spectral regions and eliminated solvent interference in constructing the predictive models of GM content in aqueous solution. We found that two factors could lead to better NIR predictions: (i) the combined use of specific NIR spectral regions related to both structural characteristics and content; and (ii) the analysis of sample spectra based on solvent reference spectra to identify and extract spectral information of testing components. We constructed predictive models for total GM C components, GM C1a (C1a), micronomicin (MCR) and sisomicin (SISO) in aqueous solution, respectively. These models can be used for the quick content analysis of different components in various types of GM injection samples.

Developing and evaluating a multisite and multispecies NIR calibration for the prediction of Kraft pulp yield in eucalypts

Over recent years the application of near infra-red (NIR) spectroscopy to the prediction of wood properties has been demonstrated in many proof-of-concept studies. Previous work has demonstrated that NIR measurements can be used to predict basic density from woodmeal, chainsaw dust and solid wood, as well as microfibril angle and modulus of elasticity in solid samples. For over a decade, the prediction of Kraft pulp yield (KPY) has been a constant research focus, and numerous small studies have demonstrated this potential. However, because of the cost of obtaining calibration samples with known KPY, sample numbers are typically less than 100. While the potential for NIR prediction of KPY is well recognised, the shift to routine commercial use has not occurred. There still remains considerable scepticism in the research and industry communities about the use of NIR. Concern is typically expressed in two areas: (1) the consistency, accuracy and precision of predictions and (2) the need to prepare a separate calibration for each site and/or species group. To elevate NIR from proof-of-concept to a pilot scale, a large multisite, multispecies calibration was developed over iterative cycles to: (1) determine whether KPY in eucalypts can be predicted from a single calibration independent of site and species, and (2) identify the potential limits of accuracy and precision. This paper reports the results of the first seven testing cycles. The NIR calibration was expanded from an initial sample set of 104 mixed eucalypt samples to over 720 samples covering more than 40 species from predominantly temperate sites across Australia. The performance of the final calibration using two independent and contrasting data sets showed that a multisite and multispecies calibration is feasible. The expected potential accuracy and precision that can be expected from NIR predictions is discussed.

Real-time on-line blend uniformity monitoring using near-infrared reflectance spectrometry: A noninvasive off-line calibration approach

A robust, noninvasive, real-time, on-line near-infrared (NIR) quantitative method is described for blend uniformity monitoring of a pharmaceutical solid dosage form containing 29.4% (w/w) drug load with three major excipients (crospovidone, lactose, and microcrystalline cellulose). A set of 21 off-line, static calibration samples were used to develop a multivariate partial least-squares (PLS) calibration model for on-line prediction of the API content during the blending process. The concentrations of the API and the three major excipients were varied randomly to minimize correlations between the components. A micro electrical-mechanical system (MEMS) based portable, battery operated NIR spectrometer was used for this study. To minimize spectral differences between the static and dynamic measurement modes, the acquired NIR spectra were preprocessed using standard normal variate (SNV) followed by second derivative Savitzky-Golay using 21 points. The performance of the off-line PLS calibration model were evaluated in real-time on 16 laboratory scale (30 L bin size) blend experiments conducted over 3 months. To challenge the robustness of the off-line calibration model, several blend experiments were conducted using a different bin size, faster revolution speed and variations in the potency of the API. Employing the PLS calibration model developed using the off-line calibration approach, the real-time API NIR (%) predictions for all experiments were all within 90–110%. These results were confirmed using the conventional thief sampling of the final blend followed by high performance liquid chromatography (HPLC) analysis. Further confirmation was established through content uniformity by HPLC of manufactured tablets. Finally, the optimized off-line PLS method was successfully transferred to a production site which involved using a secondary NIR instrument with a 15-fold scale-up in bin size from development.

Near Infrared Spectroscopy for Control of the Compound-Feed Manufacturing Process: Mixing Stage

Years of research have demonstrated the ability of near infrared (NIR) spectroscopy to predict the chemical composition and percentage of included ingredients in intact finished compound feeds. However, before reaching the end product, the mixing stage—where a large number of highly-varied ingredients are mixed together to obtain the desired product—is a critical point in feed manufacture. Detection of errors at this stage is crucial, since the process can still be corrected. This study sought to demonstrate the ability of NIR equations developed for finished compound feeds to be used at the mixing stage and ensure that the product meets specifications for chemical and ingredient labelling. When equations developed with intact compound feeds for prediction of crude protein (CP), crude fibre (CF), percentage of sunflower meal (SFM) and percentage of soybean meal (SBM) were applied to samples taken at the mixing stage, the results for CP and CF showed that NIR predictions at this stage provide information very similar to that obtained for the final product (CPmixer = 15.65% CPfinal = 15.87%; CFmixer = 7.69% CFfinal = 8.02%). The minor differences observed for the prediction of SFM (%) and SBM (%) at the two stages (SFMmixer = 6.94% SFMfinal = 7.99%; SBMmixer = 11.04% SBMfinal = 9.31%) may be due to the special features of the validation samples used. Although a number of technical and scientific problems remain to be solved, results confirm NIR spectroscopy as a viable tool at the mixing stage. This technique would enable manufacturers to make any corrections required at this point of the process.

Determination of Total Carbon and Nitrogen Content in a Range of Tropical Soils Using near Infrared Spectroscopy: Influence of Replication and Sample Grinding and Drying

Near infrared (NIR) reflectance spectroscopy has been receiving increased attention for the rapid and inexpensive determination of soil properties and of total carbon (Ct) and nitrogen content (Nt) in particular. However, methodological aspects such as sample grinding and drying or replication have not been addressed extensively. The objectives of the paper were, thus, to assess how NIR predictions of Ct and Nt were affected by sample grinding (2 mm sieving vs. 0.2 mm grinding), drying (air-drying vs oven-drying at 40°C during 24 h) and replication (use of one to six sub-samples to determine average spectra). This was performed on a range of tropical soils that differed widely in mineralogy (low and high activity clay soils, allophanic soils) and texture (sandy to clayey). The accuracy of the NIR predictions of Ct and Nt was higher with oven-dried compared to air-dried samples and, more markedly, with 0.2 mm ground compared to 2 mm sieved samples. Replication had a positive effect on NIR predictions when 2 mm sieved samples were used, especially for air-dried samples, but this effect was not clear with 0.2 mm ground samples. Thus, the most accurate predictions of Ct and Nt were obtained with oven-dried finely ground samples, with limited response to sample replication. Accurate predictions were, however, also obtained with four replicates on oven-dried 2 mm sieved samples. Acceptable and less tedious results could, thus, be achieved when replacing fine grinding by replication. Even with this procedure, the r2 between predicted (NIR) and measured (reference) values was 0.9 and the ratio of standard error of prediction to mean ( CV%) was 20% which can be considered satisfactory for the heterogeneous sample set under study.

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.