NIR Calibration Model Research Articles

Rapid single flax (Linum usitatissimum) seed phenotyping of oil and other quality traits using single kernel near infrared spectroscopy

AbstractThe growing interest in the rapid measurement of seed ingredients using single‐kernel NIR (SKNIR) spectroscopy as a nondestructive measurement technique allows fast analysis of sample seed variance that can have effects on breeding and end‐use processing. Flax (Linum usitatissimum), an oilseed crop grown in the Northwest United States and worldwide, is highly beneficial for human health, food, and fiber. Its health benefits include its high protein and omega‐3 fatty acids content. Therefore, seed composition profiles are an important aspect of breeding. The goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds. In this study, SKNIR spectroscopy was used on a diverse set of flax accessions comprising of 306 samples to create prediction models on a custom built SKNIR instrument. Spectra data and reference protein, oil, and weight were used to build partial least squares (PLS) models. Calibration models provided reasonable prediction of these traits and could be used for screening purposes. PLS statistics were oil (R2 = 0.82, SEP = 1.72), weight (R2 = 0.74, SEP = 0.71), and protein (R2 = 0.62, SEP = 0.96) for validation data sets comprising of one‐third of the total samples. In conclusion, prediction models showed that SKNIR spectroscopy could be a very beneficial nondestructive technique to determine oil and weight as well as rapid screening of protein in single flax seeds while not requiring extensive preparation as compared to traditional techniques.

Development of comprehensive calibration models for pulp traits by near infrared spectroscopy (NIRS) for pure species and hybrids of Corymbia and Eucalyptus

Corymbia hybrid has emerged as a complimentary pulpwood in India along with Eucalyptus camaldulensis and eucalypt hybrids for enhancing the profitability of the industry and the income of farmers. Near infrared spectroscopy (NIRS) offers a rapid non-destructive method for estimating wood and pulp traits of short rotation trees. Development of NIR calibration models in Corymbia is essential for quick selection and rapid advancement of pulp wood traits. A study was carried out to develop a combined calibration curve for pure E. camaldulensis and Corymbia species and their interspecific hybrids. Wood samples were extracted from four-year-old 300 trees of Corymbia torelliana (CT), C. citriodora (CC), C. variegata (CV) and E. camaldulensis (EC) and their interspecific hybrids from progeny evaluation trials at three different locations in south India. The wood logs and powdered core samples were analyzed for kraft pulp yield (KPY), lignin and S|G ratio by wet chemistry and NIRS method. The KPY values of CC and CV ranged from 44 to 52%, whereas CT, a maternal parent in CT x CC|CV hybrids, had a narrow range of 38-46.5%. However, the interspecific hybrids of CT x CC|CV exhibited a range with higher values from 48.5 to 50.5%, indicating a narrow scope for predicting hybrids from that of parent’s standard calibration curves. The estimated values in pure species of Corymbia for lignin content ranged from 22.7 to 29.2 % and 1.9 to 3.1 for S|G ratio, while in hybrids it was 25.6 to 29.4 for lignin. The correlation coefficients for cross-validation of KPY in CC, CV, CT and their hybrids were 0.8, 0.81, 0.89 and 0.72, respectively, and root mean square errors (RMS) were 0.8, 0.55, 0.73 and 0.31, respectively. When CC and CV samples were pooled to generate a common prediction model, the correlation coefficients were 0.8 for KPY, 0.75 for lignin and 0.83 for S|G ratio, with RMS of 0.68 for both KPY and lignin, and 0.12 for S|G ratio. Corymbia hybrids were evaluated with 99% accuracy using a combined prediction model of pure Corymbia species and hybrids. The method was validated with 80 samples of pure species and hybrids with a RMS of less than 1.0. We also attempted in developing a combined prediction model for different species of Corymbia, Eucalyptus, and their hybrids with minimum prediction errors.

Determination of protein and fatty acid composition of shell-intact upland cottonseed using near-infrared reflectance spectroscopy

Upland cotton is a leading natural fiber crop; and an important source of vegetable oil and protein. The protein content and fatty acid composition are important nutritional indicators of cottonseed. In order to establish rapid, high throughout, nondestructive and low-cost method for cottonseed nutrition evaluation, phenotypic data of seed kernel protein, oil and fatty acid content were combined with preprocessed near-infrared spectra of shell-intact cottonseed from 179 upland cotton genotypes to establish calibration models for these cottonseed nutrients. A total of 17 kinds of fatty acids were stably detected and quantitatively determined using Gas Chromatography-Mass Spectrometry (GC-MS) technology, including 7 kinds of fatty acids which were detected in cottonseed, to the best of our knowledge, for the first time. Finally, 19 NIR calibration models were established through partial least squares regression analysis, and evaluated by cross-validation and external verification. Results indicated that 16 models for seed protein, oil, and 14 fatty acids (capric acid, lauric acid, myristic acid, pentadecanoic acid, palmitic acid, heptadecanoic acid, linoleic acid, arachidic acid, cis-11-eicosenoic acid, linolenic acid, heneicosanoic acid, docosanoic acid, tridecanoic acid, and lignoceric acid) achieved acceptable or satisfactory prediction ability. Three calibration models for palmitoleic acid, stearic acid and oleic acid obtained moderate prediction ability with R2cal above 0.7 which could be used in rough screening. Nine fatty acids in cottonseed were modeled for the first time. These results broadened our understanding of the fatty acid profile of cottonseed oil and laid a foundation for fast, nondestructive evaluation of cottonseed nutrient quality for cotton breeding, processing and marketing.

A Comparative Study of Benchtop and Portable NIR and Raman Spectroscopic Methods for the Quantitative Determination of Curcuminoids in Turmeric Powder

Turmeric consumption is continually increasing worldwide. Curcuminoids are major active constituents in turmeric and are associated with numerous health benefits. A combination of spectroscopic methods and chemometrics shows the suitability of turmeric for food quality control due to advantages such as speed, versatility, portability, and no need for sample preparation. Five calibration models to quantify curcuminoids in turmeric were proposed using benchtop and portable devices. The most remarkable results showed that Raman and NIR calibration models present an excellent performance reporting RMSEP of 0.44% w/w and 0.41% w/w, respectively. In addition, the five proposed methods (FT-IR, Raman, and NIR) were compared in terms of precision and accuracy. The results showed that benchtop and portable methods were in good agreement and that there are no significant differences between them. This study aims to foster the use of portable devices for food quality control in situ by demonstrating their suitability for the purpose.

Detection of Chilling Injury Symptoms of Salak Pondoh Fruit during Cold Storage with Near Infrared Spectroscopy (NIRS)

One of the treatment methods that is suitable for maintaining the quality of salak pondoh fruit is cold storage. However, cold storage of salak pondoh fruit below its optimum storage temperature (15°C) can cause chilling injury/CI. This research aims to develop a non-destructive method of determining the CI symptoms of salak pondoh fruit during storage at 5°C with NIRS. Measurements were carried out for 14 days by measuring the CI parameters (pH and IL/ion leakage) and the NIR reflectance spectra. The best PLS method NIR calibration model for estimating pH was generated using the Savitzky-Golay Smoothing pretreatment with rk= 0.81, rv =0.76, SEC= 0.12%, SEP =0.12%, CV= 2.97%, RPD= 1.66, and consistency =100.06%. The IL slope of salak pondoh fruit correlated with the pH in a linear pattern relationship with the equation y = 0.1735x - 0.4305. The highest IL slope value was obtained on the 4th day of storage which means that IL has reached its peak, and CI was predicted to have occurred on the 4th day of storage.

Application of NIR Spectral Standardization Based on Principal Component Score Evaluation in Wheat Flour Crude Protein Model Sharing

In order to explore spectral standardization methods for spectra collected by different NIR spectrometers, to reduce spectral differences, and to realize model sharing among different instruments, the crude protein content of 154 wheat flour samples was measured using one grating and three Fabry-Perot tunable filter NIR spectrometers in wavelength. At the same wavelength range and wavelength interval, three algorithms, namely, direct standardization (DS), piecewise direct standardization (PDS), and simple linear regression direct standardization (SLRDS), were used to standardize spectra collected by different instruments from the same samples. Spectral standardization error rate (SSER), principal component score error rate (PCSER), and other indicators were employed to analyze the spectral differences between the master and the target spectra, and the effect of model sharing was evaluated using parameters including prediction correlation coefficient (Rp), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD). The results show the following: (1) The difference between spectra can be quantitatively evaluated through analyzing SSER and PCSER. (2) After standardization by the three algorithms, the spectral difference between the three target and the master spectrometers is significantly reduced and the prediction effect of the master model is greatly improved. (3) Among the three algorithms, DS algorithm had the smallest error rate in standardizing spectra from three target spectrometers. After standardization by the DS algorithm, the master model had the best effect. Its prediction accuracy was greatly improved compared with that before standardization. (4) The standard model established based on the S450 spectrometer can be applied to the same spectrometer as the N500 spectrometer with the same resolution and different wavelength ranges, so as to achieve model sharing. Therefore, DS, PDS, and SLRDS algorithms can effectively reduce the spectral differences between different instruments and realize the sharing of NIR calibration models for wheat flour crude protein measurement.

Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device.

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

Rapid selection of Andrographis paniculata medicinal plant materials based on major bioactive using near-infrared spectroscopy.

The quantitative analysis of andrographolides in Andrographis paniculata plant materials is essential for pharmaceutical factories. This analysis cannot be done for all samples due to the conventional process using the extraction and HPLC methods requires a long analysis time and sample destruction. Therefore, near-infrared spectroscopy (NIRS) was employed to classify the class of A. paniculata and to determine the content of two active ingredients, andrographolide (AP1) and dehydroandrographolide (AP3) in A. paniculata, rapidly and non-destructively. One hundred twenty dried powder samples were obtained from aerial parts, branches, leaves, and branches mixed with leaves. The NIR absorption scans were collected from a broad spectral region (1000–2500 nm). Then, the scanned samples were extracted and analyzed for their AP1 and AP3 contents using an HPLC reference method. The success classification model based on AP1 level was developed using the second derivative pretreated NIR spectra of the entire wavelength region using the Partial Least Squares-Discriminant Analysis (PLS-DA) method. The NIR calibration models were developed and tested for quantitative analysis with 50 independent samples. The models were identified for the analysis of the AP1 content with excellent performance (correlation coefficient (R) = 0.98; standard error of validation (SEV) = 0.24%) and for the analysis of the AP3 content at a good level of efficiency (R = 0.93; SEV = 0.15%). This study showed that NIR spectroscopic method offers rapid analysis for the selection of A. paniculata that meets the requirement in bioactive amount.Graphic Supplementary InformationThe online version contains supplementary material available at 10.1007/s11696-021-01746-0.

An innovative sampling interface for monitoring flowing pharmaceutical powder mixtures

A chute was designed following the principles of the Theory of Sampling to minimize the variations in powder flow and provide all particles in the flowing blends with the same opportunity of being selected as a sample. The design also reduces the thickness of the chute to allow the analysis of a higher portion of the flowing blends by a near infrared spectrometer. The blends that flowed through the chute had Carr’s index values that fluctuated between 23 and 25 percent, indicating passable flowability. A powder fowling evaluation demonstrated that there was no powder accumulation at the inspection window of the chute. The mass flow rate profiles indicated that the system achieves mass steady-state in approximately 30 s and a throughput of 30 kg/h which makes it suitable for continuous manufacturing operations. An in-line NIR calibration model was developed to quantify caffeine concentrations between 1.51 and 4.52 % w/w. The spectra obtained from each experiment had minimal baseline variation. The developed NIR method was robust to throughput changes up to approximately ±7 %. The test blends in the caffeine concentration range between 2.02 % w/w and 4.02 % w/w met the dose uniformity requirements of the Ph.Eur. 9.0, chapter 2.9.47. Variographic analysis was done to estimate the analytical and sampling errors which yielded values below 0.01 (%w/w)2. The obtained results showed that this chute could also be used in a continuous manufacturing line or other applications with flowing powders.

Residence time distribution modelling and in line monitoring of drug concentration in a tablet press feed frame containing dead zones

The presence of a ‘significant dead zone’ in any continuous manufacturing equipment may affect the product quality and need to be investigated systematically. Dead zone will affect the residence time distribution (RTD) of continuous manufacturing and thus the mixing and product quality. Tablet press (feed frame) is one of unit operations that directly influence the critical quality attributes (CQA’s). However, currently no systematic methods and tools are available to characterize and model the feed frame dead zone. In this manuscript, the RTD of the tablet press feed frame containing dead zone is investigated. Step-change experiments revealed that the feed frame could be expressed as a traditional continuous stirred tank model. The volume fractions of the dead zones are determined experimentally as well as using RTD model. In addition, an in-line NIR method for drug concentration monitoring inside the feed frame is also developed. The developed NIR calibration model enables to monitor the drug concentration precisely and detect the variation immediately with the probe positioned right above the left paddle. It is also found that the feed frame paddle speed slightly affects the predictive accuracy of NIR, while the die disc speed has no significant effect.

Evaluation of temperature compensation methods for a near‐infrared calibration to predict the viscosity of micellar liquids

AbstractNear‐infrared (NIR) spectroscopy is a popular technique for the measurement of chemical and physical properties in‐line using predictive models. The success of these models in industrial settings, in terms of accuracy and precision, often relies on the removal or avoidance of non‐linear spectral changes associated with fluctuating process parameters like temperature. In this work, a NIR calibration model developed to predict the viscosity of micellar liquids in‐line is used to evaluate various methods designed to account for temperature fluctuations. The viscosity of these liquids can vary on average by ±0.5 Pa s with a 1° change in temperature. The methods trialled include global linear techniques, a multivariate filter (generalised least squares weighting [GLSW]) and direct standardisation. The performances of these techniques were compared against one another based on root mean square error of prediction (RMSEP), prediction bias and rank. The best method was found to be GLSW, which was the least complex (five latent variables) and showed the lowest RMSEP (0.429 Pa s). This study provides insight into the use of recognised methods to remove temperature‐induced spectral variation in a PLS model developed to predict viscosity, where both NIR spectra and the property of viscosity itself are sensitive to temperature.

Procjena kemijskog sastava voluminozne krme NIR spektroskopijom

Near-infrared spectroscopy (NIR spectroscopy) has been used in analytics for more than 50 years. The aim of this review is to present statistical indicators of the developed calibration models for predicting forage chemical composition by NIR spectroscopy, which have been published over the last 15 years. This paper presents statistics for predicting of forage dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), ash, and pH value of forage at different pre-scan processing level (fresh, dried / ground forage) and different forage types such as grass monocultures, legumes, grass-clover mixtures (GCM), semi-natural pasture, straw, maize, hay, silage and haylage. Due to wider applicability of NIR calibration model for prediction of chemical composition of forage, the development of calibration includes forage originating from various agricultural production technologies, cultivation climates, varieties and vegetation seasons, etc. In order to develop more reliable calibration models for prediction of forage chemical composition, calibrations are developed for individual plant species, cultivars, harvest during the vegetation season, as well as for individual microclimates of cultivation. NIR spectroscopy has high potential for predicting the content of DM, CP, NDF, ADF, ash and pH value in forage.

Prediction Carbohydrate Content of Sweet Passion Fruit Seeds (140 Days After Flowering) Using NIR Spectra with Artificial Neural Network

This study purposed to develop estimation methods using NIR spectroscopy in determining carbohydrate content of sweet passion fruit seeds at the level maturity at 140 days after flowering and developing an NIR calibration model using an ANN (Neural Network) chemo metric technique. The research method used several stages, labeling the material preparation stage, the measurement stage using NIR, the chemical analysis stage and the data processing stage with artificial neural networks. At the reflectance input the highest R-Total value which the number of neurons in the hidden layer is 7 (R-Total is 0.870) and R-SNV pretreatment as the input of the highest R-Total value is found in the number of neurons with hidden layers which is 9 (R- Total is 0.949). Based on these results the development model is suitable as a predictive value, this can be seen from the regression value approaching 1.

Vibrational spectroscopy as a green technology for predicting nutraceutical properties and antiradical potential of early-to-late apricot genotypes

Vibrational spectroscopy is considered a green alternative to the traditional analytical methods for quantitative determination of antioxidants in agricultural products. In this study, the potential of NIR and MIR spectroscopy for the prediction of antioxidant compounds in seven early-to-late apricot genotypes from different locations was reported. NIR and MIR calibration models were developed using Partial Least Squares Regression (PLS) with cross-validation, and the performance of final models was evaluated using Rp2, root mean square error of prediction (RMSEP) and relative predicted determinant (RPD). Five independent PLS models were developed, using both NIR and MIR spectra from 300 samples per year and their corresponding total phenols (TPs), total flavans (FLs), total carotenoids (TCs), antiradical capacity (AC) reference values, yielding coefficient on average always > 0.95. Very good models were obtained for TPs (Rp2 = 0.99, RMSEP = 0.73 g kg−1 GAE, RPD = 12.0; Rp2 = 0.99, RMSEP = 0.95 g kg−1 GAE, RPD = 9.9 for MIR and NIR models, respectively), and FLs (Rp2 = 0.99, RMSEP = 0.99 g kg−1 CAT, RPD = 8.5; Rp2 = 0.98, RMSEP = 1.10 g kg−1 CAT, RPD = 6.4, for MIR and NIR models, respectively). Besides, both MIR and NIR models for total carotenoids (TCs) quantification showed a satisfactorily predictive capability (Rp2 = 0.98, RMSEP = 0.01 g kg-1 β-carotene, RPD = 6.5; Rp2 = 0.95, RMSEP = 0.01 g kg-1 β-carotene, RPD = 4.5, for MIR and NIR models, respectively). Good model performances were also obtained for the prediction of antiradical potential of samples, developed herein for the first time on freeze-dried apricot samples. In general, ATR-MIR demonstrated advantages over NIR in predicting all nutraceutical parameters, whereas similar model performances were obtained for the antiradical potential tested versus ABTS+• using MIR and NIR spectral regions. Results reported in the present study suggest that vibrational spectroscopy, both in MIR and NIR range, is adequate to predict the nutraceutical properties of apricots for screening purposes quickly and sustainably.

Log‐ratio transformation is the key to determining soil organic carbon fractions with near‐infrared spectroscopy

SummaryInformation about soil organic carbon fractions is important in understanding the vulnerability of soil carbon to climate change and land management. Soil organic carbon can be divided into fractions that are labile and others that are more stable. All existing methods to fractionate soil organic carbon are time consuming and complex. Near‐infrared reflectance spectroscopy (NIRS) is a rapid analytical technique. In this study we evaluated and optimized the use of NIRS to predict soil organic carbon fractions with the constraint that the carbon fractions (labile and stabilized) should add up to 100% (total organic carbon content). We used samples from two datasets from agricultural and forest sites in Germany (dataset A) and Europe (dataset B). Samples were fractionated by two different methods (density and physical‐chemical fractionation) as reference methods. Soil samples were scanned in the NIR range and calibration models were developed using partial least squares regression. The key to improving model performance was the log‐ratio transformation proposed by Aitchison for compositional data that enabled us to model the fractions as dependent variables. Traditional methods for the optimization of NIRS models, such as selection of wavelength range and pretreatment of spectra, showed no effective reduction in error. With the constraint that both fractions add to 100% (log‐ratio transformation), the ratio of performance to deviation (RPD) increased from 1.6 to 2.8 for the labile C fraction and from 1.5 to 3.2 for the stabilized C fraction. Root mean square error of cross‐validation of the labile fraction was 4.3 g C kg−1for dataset A and 2.7 g C kg−1for dataset B, which corresponds to anR2of 0.88–0.80 and RPD of 2.9–2.2, respectively. The models performed equally well for different soil textures and land‐use types. With log‐ratio transformation, more precise calibrations of NIRS for C fractions could be obtained.HighlightsNear‐infrared spectroscopy can predict soil carbon fractions at different spatial extentsLog‐ratio transformation satisfies the constraint that fractions should add up to 100%Log‐ratio transformation is the key to improving calibrations for soil C fractions.The NIR models were tested with two fractionation schemes comprising different land uses and soil texture

Monitoring of polypeptide content in the solid‐state fermentation process of rapeseed meal using NIRS and chemometrics

AbstractThis study combined Fourier transform‐near infrared (NIR) spectroscopy and chemometrics, to monitor changes in peptide content during the solid‐state fermentation of rapeseed meal. A NIR calibration model was established by performing spectral scanning on 81 samples and using interval partial least squares. The results showed that coefficient of determination (R2) and root mean square error of cross‐validation could achieve 0.9441 and 0.654 g/L for polypeptide content. Furthermore, the predicted and experimental values of the two parameters in an external validation set showed similar changes throughout the fermentation.Practical applicationsThe results show that near‐infrared spectroscopy is a promising method to monitor the chemical parameters of rapeseed meal during solid fermentation.

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS2regression method

NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and non-civet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R2) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R2pred values. The prediction also resulted in low bias and RMSEP values.

Use of NIRS technology for on-vine measurement of nitrate content and other internal quality parameters in intact summer squash for baby food production

This study sought to assess the feasibility of using NIR spectroscopy to predict the physico-chemical composition of summer squash during on-vine ripening, with a view to deciding on its possible use in baby food production depending on nitrate content at harvesting. NIR calibration models were developed using a set of 157 samples scanned in situ in the 1600–2400nm region, using a portable handheld MEMS-NIR spectrophotometer working in reflectance mode. Modified partial least squares (MPLS) regression was used to interpret spectra and develop calibrations for summer squash composition. Results (R2cv=0.83; SECV=112.44mgL−1) showed that NIRS technology has great potential for measuring nitrate content and also other quality parameters in intact summer squashes during on-vine ripening. In addition, suitable wavelengths for nitrate content determination were identified by x-loading weights and regression coefficients. These findings suggest that NIRS may be a valuable tool for the rapid, accurate and non-destructive measurement of nitrate content, with a view to ascertaining the suitability of individual fruits for use in the production of baby foods.

The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability

We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product’s enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

Swellable floating tablet based on spray-dried casein nanoparticles: Near-infrared spectral characterization and floating matrix evaluation

In this study, spray-dried alfuzosin hydrochloride (ALF)-loaded casein (CAS) nanoparticles were successfully used for the preparation of a swellable floating matrix via direct compression. The developed NIR calibration model was able to assess ALF and CAS levels in five different batches of drug-loaded nanoparticles. The calibration and prediction plots exhibited good linearity with correlation coefficients of more than 0.9. The standard error of calibration and cross-validation was less than 5% of the measured values, confirming the accuracy of the model. A linear relationship was obtained correlating the actual drug entrapped and the predicted values obtained from the NIR partial least squares regression model. The un-crosslinked tablet demonstrated a substantial weight gain (317% after 2h) and completely disintegrated after 3–4h whereas both 10 and 40% w/w genipin-crosslinked tablets showed lower weight gain (114 and 42% after 2h, respectively). A rapid floating of the tablets within 5–15min (compared to 45min for the marketed tablet) was observed, with maintained floating for 24h. Marketed and prepared tablets succeeded to prolong ALF release for 24h. The development of drug-loaded CAS nanoparticles using spray-drying represents a new alternative for the preparation of swellable floating tablets for prolonged drug release.