Effect of diet composition on the determination of ash and moisture content in solid cattle manure using visible and near-infrared spectroscopy

Visible and near-infrared (Vis-NIR, 350-2500 nm) diffuse reflection spectroscopy (DRS) models built from "as-collected" samples of solid cattle manure accurately predict concentrations of moisture and crude ash. Because different organic molecules emit different spectral signatures, variations in livestock diet composition may affect the predictive accuracy of these models. This study investigates how differences in livestock diet composition affect Vis-NIR DRS prediction of moisture and crude ash. Spectral signatures of solid manure samples (n = 216) from eighteen groups of cattle on six different diets were used to calibrate and validate partial least squares (PLS) regression models. Seven groups of PLS models were created and validated. In the first group, two-thirds of all samples were randomly selected as the calibration set and the remaining one-third were used for the validation set. In the remaining six groups, samples were grouped by livestock diet (ration). Each ration in turn was held out of calibrations and then used as a validation set. When predicting crude ash, the fully random calibration model produced a root mean square deviation (RMSD) of 2.5% on a dry basis (db), ratio of standard error of prediction to the root mean squared deviation (RPD) of 3.1, bias of 0.14% (db), and correlation coefficient r(2) of 0.90., When predicting moisture, an RMSD of 1.5% on a wet basis (wb), RPD of 4.3, bias of -0.09% (wb), and r(2) of 0.95 was achieved. Model accuracy and precision were not impaired by exclusion of any single ration from model calibration.     

Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: comparison of a new calibration transfer method with a slope/bias correction method

This paper reports the prediction of the ethylene content (C2 content) in random polypropylene (RPP) and block polypropylene (BPP) in the melt state by near-infrared (NIR) spectroscopy and chemometrics. NIR spectra of RPP and BPP in the melt states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system. The NIR spectra of RPP and BPP were compared. Partial least-squares (PLS) regression calibration models predicting the ethylene (C2) content that were developed by using each RPP or BPP spectra set separately yielded good results (SECV (standard error of cross validation): RPP, 0.16%; BPP, 0.31%; correlation coefficient: RPP, 0.998; BPP, 0.996). We also built a common PLS calibration model by using both the RPP and the BPP spectra set. The results showed that the common calibration model has larger SECV values than the models based on the RPP or the BPP spectra sets individually and is not practical for the prediction of the C2 content. We further investigated whether a calibration model developed by using the BPP spectra set can predict the C2 contents in the RPP sample set. If this is possible, it can save a significant amount of work and cost. The results showed that the use of the BPP model for the RPP sample set is difficult, and vice versa, because there are some differences in the molar absorption coefficients between the RPP and BPP spectra. To solve this problem, a transfer method from one sample spectra (BPP) set to the other spectra (RPP) set was studied. A difference spectrum between an RPP spectrum and a BPP spectrum was used to transfer from the BPP calibration set to the RPP calibration set. The prediction result (SEP (standard error of prediction), 0.23%, correlation coefficient, 0.994) of RPP samples by the transferred calibration set and model showed that it is possible to transfer from the BPP calibration set to the RPP calibration set. We also studied the transfer from the RPP calibration set (the range of C2 content: 0-4.3%) to the BPP calibration set. The prediction result of C2 content (the range of C2 contents: 0-7.7%) in BPP by use of the calibration model based on the transferred BPP spectra from the RPP spectra showed that the transfer method is only effective for the interpolation of the C2 content range by the nonlinear change in the peak intensities with the C2 content.     

The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures

The effect of the presence of metabolism-induced concentration correlations in the calibration samples on the prediction performance of partial least-squares regression (PLSR) models and mid-infrared spectra from Chinese hamster ovary cell cultures was investigated. Samples collected from batch cultures contained highly correlated metabolite concentrations as a result of metabolic relations. Calibrations based on such samples could only be used to predict concentrations in new samples if a similar correlation structure was present and failed when the new samples were randomly spiked with the analytes. On the other hand, such models were able to predict glucose correctly even if they were based on a spectral range in which glucose does not absorb, provided that the correlations in the calibration and in the new samples were similar. If however, samples from a calibration culture were randomly spiked with the main analytes, much more robust PLSR models resulted. It was possible to predict analyte concentrations in new samples irrespective of whether the correlation structure was maintained or not. Validity of all established models for any given use could be predicted a priori by computing the space inclusion and observer conditions. Predictions from these computations agreed in all cases with the experimental test of model validity.     

Prediction of potential mushroom yield by visible and near-infrared spectroscopy using fresh phase II compost

Potential mushroom (Agaricus bisporus) yield of phase II compost is determined by interactions of key quality parameters including dry matter, nitrogen dry matter, ammonia, pH, conductivity, thermophilic microorganisms, C : N ratio, fiber fractions, ash, and certain minerals. This study was aimed at generating robust visible and near-infrared (Vis-NIR) calibrations for predicting potential yield, using spectra from fresh phase II compost. Four compost comparative trials were carried out during the winter and summer months of 2001-2003, under controlled experimental conditions employing six commercially prepared composts, with eight replicate (8 bag) plots per treatment (48 x 8 = 384). The substrates were prepared by windrow or bunker phase I, followed by phase II production. The fresh samples were scanned for Vis-NIR (400-2498 nm) spectra, averaged, transformed, and regressed against the recorded yield by employing a modified partial least squares algorithm. The best calibration model generated from the database explained 84% of yield variation within the data set with a standard error of calibration of 13.75 kg/tonne of fresh compost. The model was successfully tested for robustness with yield results obtained from a validation trial, carried out under similar experimental conditions in early 2004, and the standard error of prediction was 18.21 kg/tonne, which was slightly higher than the mean experimental error (17.94 kg/tonne) of the trial. The accuracy of the model is acceptable for estimating potential yield by classifying phase II substrate as poor (180-220 kg), medium (220-260 kg), and high (260-300 kg) yielding compost. The yield prediction model is being transferred to a new instrument based at Loughgall for routine evaluation of commercial phase II samples.     

Determination of lycopene and beta-carotene content in tomato fruits and related products: Comparison of FT-Raman, ATR-IR, and NIR spectroscopy

Tomatoes and various products derived from thermally processed tomatoes are major sources of lycopene, but apart from this micronutrient, other carotenoids such as beta-carotene also are present in the fruit. They occur in tomato fruits and various tomato products in amounts of 2.62-629.00 (lycopene) and 0.23-2.83 mg/100 g (beta-carotene). Standard methods for determining the carotenoid content require the extraction of the analyte as well as other cleanup steps. In this work, FT-Raman, ATR-IR, and NIR spectroscopy are applied in order to establish new, fast, and nondestructive calibration methods for quantification of lycopene and beta-carotene content in tomato fruits and related products. The best prediction quality was achieved using a model based on IR spectroscopy (R2 = 0.98 and 0.97, SECV = 33.20 and 0.16 for lycopene and beta-carotene, respectively). In spite of the fact that Raman spectra of tomato products show characteristic key bands of the investigated carotenoids, this method gives slightly lower reliability (R2 = 0.91 and 0.89, SECV = 74.34 and 0.34 for lycopene and beta-carotene, respectively). NIR spectroscopy, which has been used for quantification purposes in the agricultural sector for several decades, in this study shows the worse prediction quality (R2 = 0.85 and 0.80, SECV = 91.19 and 0.41 for lycopene and beta-carotene, respectively).     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

Optimization of informative spectral variables for the quantification of EGCG in green tea using Fourier transform near-infrared (FT-NIR) spectroscopy and multivariate calibration

Epigallocatechin-3-gallate (EGCG) is credited with the majority of the health benefits associated with green tea consumption. It has a high economic and medicinal value. The feasibility of using different variable selection approaches in Fourier transform near-infrared (FT-NIR) spectroscopy for a rapid and conclusive quantitative determination of EGCG in green tea was investigated. Graphically oriented multivariate calibration modeling procedures such as interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and genetic algorithm optimization combined with siPLS (siPLS-GA) were applied to select the most efficient spectral variables that provided the lowest prediction error. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and coefficient of determination (R(2)) for the prediction set. Experimental results showed that the siPLS-GA model obtained the best results in comparison to other models. The optimal models were achieved with R(2)(p) = 0.97 and RMSEP = 0.32. The model can be obtained with only 36 variables retained and it provides a robust model with good estimation accuracy. This demonstrates the potential of NIR spectroscopy with multivariate calibration methods to quickly detect the bioactive component in green tea.     

Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database

Large soil spectral libraries compiling thousands of NIR (Near Infrared) reflectance spectra have been created encompassing a wide diversity and heterogeneity of spectra. Among the many chemometric approaches to the calibration of chemical and physical properties from these large libraries, local calibrations have the advantage of being able to select the most similar spectra to the spectrum of a target sample. This is particularly relevant when dealing with highly heterogeneous media such as soils, where the mineral matrix has a strong influence on spectral features. A crucial step in the implementation of local calibration procedures is the construction of local neighbourhoods. In this study, we investigate the influence of index computation and neighbour selection on calibration results using local PLSR models on a large soil spectral database. Our indices combine two spectral compression methods (Principal Component Analysis or Fast Fourier Transform) with two distinct distance metrics (Mahalanobis distance or correlation coefficient). Based on a large collection of soil samples provided by the French National Soil Quality Monitoring programme, we constructed calibration models to estimate two chemical (organic carbon and cationic exchange capacity) and two physical (clay and sand content) factors. After neighbour selection, local Partial Least Squares regressions were applied to the selected spectra. Our results highlight the utility of the Fourier transformation of the spectra compared to the classical PCA compression method in achieving a more appropriate neighbourhood selection. We propose an index based on the coefficient correlation with FFT compression that led to a neighbourhood selection giving the best prediction results for the four considered soil constituents.     

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path Fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

The use of multiple calibration sets in partial least squares (PLS) regression was proposed to improve the quantitative determination of NH(3) over wide concentration ranges from open-path Fourier transform infrared (OP/FT-IR) spectra. The spectra were measured near animal farms, where the path-integrated concentration of NH(3) can fluctuate from nearly zero to as high as approximately 1000 ppm-m. PLS regression with a single calibration set did not cover such a large concentration range effectively, and the quantitative accuracy was degraded due to the nonlinear relationship between concentration and absorbance for spectra measured at low resolution (1 cm(-1) and poorer.) In PLS regression with multiple calibration sets, each calibration set covers a part of the entire concentration range, which significantly decreases the serious nonlinearity problem in PLS regression occurring when only a single calibration set is used. The relative error was reduced from approximately 6% to below 2%, and the best results were obtained with four calibration sets, each covering one quarter of the entire concentration range. It was also found that it was possible to build the multiple calibration sets easily and efficiently without extra measurements.     

绿茶汤中茶多酚近红外定量分析的光程选择

研究了光程对近红外定量分析绿茶汤中茶多酚的影响.以不同光程(1 mm,2 mm,5 mm)的样品池采集50个绿茶汤样品的近红外透射光谱.采用偏最小二乘法(PLS)建立茶汤中茶多酚的定量分析模型,并验证模型的准确度和精密度.结果表明,选择1 mm光程光谱建立的茶多酚定量分析模型最优,模型的校正集相关系数R2和内部交叉验证均方根RMSECV分别为91.08%和0.009 3%;检验集相关系数R2和预测标准差SEP为91.53%和0.008 5%;实际值与预测值配对t检验值为0.224 9,差异不显著.10次重复测量相对标准偏差RSD为0.008 7%,表明方法检测重复性好.     

Quantitative measurements of loss on ignition in iron ore using laser-induced breakdown spectroscopy and partial least squares regression analysis

Laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR) have been applied to perform quantitative measurements of a multiple-species parameter known as loss on ignition (LOI), in a combined set of run-of-mine (ROM) iron ore samples originating from five different iron ore deposits. Global calibration models based on 65 samples and their duplicates from all the deposits with LOI ranging from 0.5 to 10 wt% are shown to be successful for prediction of LOI content in pressed pellets as well as bulk ore samples. A global independent dataset comprising a further 60 samples was used to validate the model resulting in the best validation R(2) of 0.87 and root mean square error of prediction (RMSEP) of 1.1 wt% for bulk samples. A validation R(2) of 0.90 and an RMSEP of 1.0 wt% were demonstrated for pressed pellets. Data preprocessing is shown to improve the quality of the analysis. Spectra normalization options, automatic outlier removal and automatic continuum background correction, which were used to improve the performance of the PLSR method, are discussed in detail.     

The effect of flow rate, accelerometer location and temperature in acoustic chemometrics on liquid flow: Spectral changes and robustness of the prediction models

Prediction of chemical composition of flowing liquids using passive acoustic measurements and multivariate regression (acoustic chemometrics) has been reported as a promising in-line measurement method. However, the passive acoustic measurement results are also affected directly or indirectly by other factors than composition of the liquid, i.e. physical conditions of the flow and equipment/pipe properties. The present study focuses on the effects of flow rate, accelerometer location and temperature on the acoustic spectra and prediction of composition of liquids. The studied liquids were two-component mixtures of sucrose and water, and three-component mixtures of ethanol, sucrose and water. Multivariate models were estimated using both local and global calibration on full spectra, and augmented frequency and amplitude matrices derived from full spectra. Flow rate and accelerometer location had the most pronounced effect on acoustic spectra and prediction results from recalibrated local models. Temperature had a minor effect on the acoustic spectra and prediction results. The prediction error for determination of ethanol, sucrose and water increased with increasing flow rate. Changes in flow rate resulted in considerable spectral variations, causing the resultant local calibration model to perform poorly predicting the new samples taken at other flow conditions. Global models performed well on prediction of liquid composition at all studied flow and temperature levels. The global models, however, needed higher number of PLS factors and led to higher prediction errors compared to local models. Using the augmented frequency and amplitude matrices in PLS/PPLS global regression models led to higher prediction errors compared to full spectra models. However, the augmented frequency and amplitude models were more parsimonious (4–6 PLS factors) compared to the full spectra models (10–12 PLS factors).     

Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models

Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.     

In vivo noninvasive measurement of blood glucose by near-infrared diffuse-reflectance spectroscopy

This paper reports in situ noninvasive blood glucose monitoring by use of near-infrared (NIR) diffuse-reflectance spectroscopy. The NIR spectra of the human forearm were measured in vivo by using a pair of source and detector optical fibers separated by a distance of 0.65 mm on the skin surface. This optical geometry enables the selective measurement of dermis tissue spectra due to the skin's optical properties and reduces the interference noise arising from the stratum corneum. Oral glucose intake experiments were performed with six subjects (including a single subject with type I diabetes) whose NIR skin spectra were measured at the forearm. Partial least-squares regression (PLSR) analysis was carried out and calibration equations were obtained with each subject individually. Without exception among the six subjects, the regression coefficient vectors of their calibration models were similar to each other and had a positive peak at around 1600 nm, corresponding to the characteristic absorption peak of glucose. This result indicates that there is every possibility of glucose detection in skin tissue using our measurement system. We also found that there was a good correlation between the optically predicted values and the directly measured values of blood samples with individual subjects. The potential of noninvasive blood glucose monitoring using our methodology was demonstrated by the present study.     

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Spectral simulation methodology for calibration transfer of near-infrared spectra

A spectrum simulation method is described for use in the development and transfer of multivariate calibration models from near-infrared spectra. By use of previously measured molar absorptivities and solvent displacement factors, synthetic calibration spectra are computed using only background spectra collected with the spectrometer for which a calibration model is desired. The resulting synthetic calibration set is used with partial least squares regression to form the calibration model. This methodology is demonstrated for use in the analysis of physiological levels of glucose (1-30 mM) in an aqueous matrix containing variable levels of alanine, ascorbate, lactate, urea, and triacetin. Experimentally measured data from two different Fourier transform spectrometers with different noise levels and stabilities are used to evaluate the simulation method. With the more stable instrument (A), well-performing calibration models are obtained, producing a standard error of prediction (SEP) of 0.70 mM. With the less stable instrument (B), the calibration based solely on synthetic spectra is less successful, producing an SEP value of 1.58 mM. For cases in which the synthetic spectra do not describe enough spectral variance, an augmentation protocol is evaluated in which the synthetic calibration spectra are augmented with the spectra of a small number of experimentally measured calibration samples. For instruments A and B, respectively, augmentation with measured spectra of nine samples lowers the SEP values to 0.64 and 0.85 mM.     

Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using Fourier transform Raman spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.     

Multivariate calibration and instrument standardization for the rapid detection of diethylene glycol in glycerin by Raman spectroscopy

The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.     

Advanced process design and understanding of continuous twin-screw granulation via implementation of in-line process analytical technologies

Process analytical technologies (PAT) are identified as an essential element in the Quality by Design framework, providing the cornerstone to implement continuous pharmaceutical manufacturing. This study is concerned with employing three in-line PATs: Eyecon? 3D imaging system, Near-infrared spectroscopy (NIRS) and Raman spectroscopy (RS), in a continuous twin-screw granulation process to enable real-time monitoring and prediction of critical quality attributes of granules. The Thermo Scientific? Pharma 11 twin-screw granulator was used to manufacture granules from a low-dose formulation with caffeine anhydrous as the model drug. A 30-run full factorial design including three critical process parameters (liquid to solid ratio, barrel temperature and throughput) was conducted to evaluate the performance of each analytical tool. Eyecon? successfully captured the granule size and shape variation from different experimental conditions and demonstrated sufficient sensitivity to the fluctuation of size parameter D10 in the presence of process perturbations. The partial least square regression (PLSR) models developed using NIRS showed small relative standard error of prediction values (less than 5%) for most granule physical properties. In contrast, the RS-based PLSR models revealed higher prediction errors towards granule drug concentration, potentially due to the inhomogeneous premixing of raw materials during calibration model development.