Determination of total protein in hyperimmune serum samples by near-infrared spectrometry and multivariate calibration

In the present work, the determination of the total protein concentration in hyperimmune serum samples was performed through a partial least-squares near-infrared (NIR-PLS) method. The method was based on the chemometric treatment of the NIR spectra of samples. The influences of spectra preprocessing and spectral window utilized in the construction of PLS model were studied. Models were built using reference data of 19 samples selected through the use of hierarchical cluster analysis (HCA) of NIR spectra of samples and another 24 samples were employed for external validation of the method. A model with better prediction capacity was obtained after whole spectra preprocessing by multiplicative scattering correction (MSC) algorithm and using data in the spectral range of 2158-2209 nm. Under optimized conditions a RMSEP of 0.21 g dl⁻¹ and a quality coefficient value (QC) of only 5.8% were obtained for the prediction of total protein content in the samples used for external validation. Also, a determination coefficient, r², of 0.97 was obtained in the correlation of predicted and reference data of samples situated in the validation set.     

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

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

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm^?1, 7515.24 to 7108.33 cm^?1, and 5257.00 to 5098.87 cm^?1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm^?1was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.     

FT-NIRS技术应用于稻米直链淀粉含量分析研究

运用近红外光谱快速分析技术,使用偏最小二乘法建立了近红外光谱和水稻糙米直链淀粉含量的数学模型,并进行糙米直链淀粉含量预测.结果表明糙米近红外光谱与其直链淀粉含量具有良好的相关性,决定系数r2=0.8429,最大绝对误差4.82%,平均误差2.30%.该方法在不破坏样品的前提下快速分析水稻直链淀粉含量,可用于稻种资源的快速鉴定,对于水稻优质育种及其相关研究具有重要意义.     

Process analytical technology case study: Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

近红外光谱仪快速检测油菜硫苷、芥酸及油份含量数学模型的建立

近红外反射光谱技术是一种高效、快速的现代分析技术,应用过程中需要建立相应的数学模型.收集油菜籽样品600余份,采用国际(家)标准方法对芥酸、硫苷、含油率进行测试,根据各模型的要求,选择不同的具有代表性的样品,建立数学模型.内部交叉证实法对模型验证结果显示:高芥酸、低芥酸、硫苷、含油率模型复相关系数分别为0.9677、0.9318、0.9820、0.9626,内部验证均方差(RMSECV)分别为4.41、2.24、4.62、0.543.外部样品检测法分别用已建模型和国际(家)标准方法对油菜籽样品芥酸、硫苷、含油率进行检测,两者检测结果基本一致.综合内部和外部验证结果表明:建立的模型能够满足油菜籽硫甘、芥酸及油份含量快速检测的要求.另对油菜自身含水率对数学模型的影响做了初步探索.     

Aerobic digestion of Ca-alginate gels studied as a model system of seaweed tissue degradation

The Ca-crosslinked alginate matrix of brown seaweeds may present a limiting factor when microbes decompose algal tissue. Ca-alginate gels made from Ascophyllum nodosum and Laminaria hyperborea stipe alginates were digested in aerated batch reactors at 35 °C and pH 7 using an alginate decomposing inoculum harvested during aerobic degradation of L. hyperborea stipe. The mineralisation of Ca-alginate gels was independent of the substrate source, with consumption rates of alginate similar to those of algal alginates in L. hyperborea stipe. Despite a high guluronate lyase activity, the fractional content of guluronate in the remaining Ca-alginate gels increased during digestion as observed earlier for algal tissue. Thus, the Ca-crosslinked guluronate residues were the most recalcitrant material in both gels and algal tissue.Since the access for enzymes to the Ca-crosslinked guluronate residues probably is restricted, ionic washout may represent an important factor for the degradation process. In total, the alginate in algal tissue and Ca-alginate gels behaved similarly during biodegradation. This revised version was published online in August 2006 with corrections to the Cover Date.     

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

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

食用调和油中花生油含量的近红外光谱分析

采用偏最小二乘法(PLS)等方法建立了食用调和油中花生油含量定量分析的近红外光谱定标模型。采集食用调和油样品在4 000 cm-1~10 000 cm-1范围内的近红外漫反射光谱,光谱经一阶导数处理后,采用偏最小二乘法建立样品中花生油含量的定标模型,并用Leave-one-out内部交叉验证法对模型进行验证。模型相关系数为0.99961,校正均方根RMSEC为0.830%。比较不同光谱预处理方法对定标模型的影响,结果表明一阶导数Corr.coeff最好。采用不同的化学计量学方法建立的定标模型中以偏最小二乘回归法最理想。     

Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy

Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800–1800 cm⁻¹) of 21 maize samples that were contaminated with 300 – 2600 μg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported. Presented at the 25^th Mykotoxin Workshop in Giessen, Germany, May 19–21, 2003     

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.     

Metabolic profiling and predicting the free radical scavenging activity of guava (Psidium guajava L.) leaves according to harvest time by 1H-nuclear magnetic resonance spectroscopy

Guava leaves were classified and the free radical scavenging activity (FRSA) evaluated according to different harvest times by using the (1)H-NMR-based metabolomic technique. A principal component analysis (PCA) of (1)H-NMR data from the guava leaves provided clear clusters according to the harvesting time. A partial least squares (PLS) analysis indicated a correlation between the metabolic profile and FRSA. FRSA levels of the guava leaves harvested during May and August were high, and those leaves contained higher amounts of 3-hydroxybutyric acid, acetic acid, glutamic acid, asparagine, citric acid, malonic acid, trans-aconitic acid, ascorbic acid, maleic acid, cis-aconitic acid, epicatechin, protocatechuic acid, and xanthine than the leaves harvested during October and December. Epicatechin and protocatechuic acid among those compounds seem to have enhanced FRSA of the guava leaf samples harvested in May and August. A PLS regression model was established to predict guava leaf FRSA at different harvesting times by using a (1)H-NMR data set. The predictability of the PLS model was then tested by internal and external validation. The results of this study indicate that (1)H-NMR-based metabolomic data could usefully characterize guava leaves according to their time of harvesting.     

应用近红外光谱估测小麦叶片氮含量

研究利用近红外光谱(near-infrared, NIR)和化学计量学方法估测小麦(Triticum aestivum)新鲜叶片和粉末状干叶中全氮含量的可行性, 并建立小麦叶片氮含量估测模型, 以期为小麦氮素营养的精确管理提供理论依据。以3个小麦田间试验观测资料为基础, 分别运用偏最小二乘法(partial least squares, PLS)、反向传播神经网络(back-propagation neural network, BPNN)和小波神经网络(wavelet neural network, WNN), 建立小麦叶片氮含量的鲜叶和粉末状干叶近红外光谱估测模型, 用随机选择的样品集对所建模型进行测试和检验。结果显示, 利用PLS、BPNN和WNN 3种方法构建的近红外光谱模型均能准确地估测小麦叶片氮含量, 其中基于BPNN和WNN的模型优于基于PLS的模型, 且以基于WNN的模型表现最好。对模型进行检验的结果显示, 粉末状干叶模型的预测均方根误差(RMSEP)分别为0.147、0.101和0.094, 鲜叶模型的RMSEP分别为0.216、0.175和0.169, 模型的相关系数均在0.84以上。因此, 利用近红外光谱估算小麦叶片氮素营养精确可行, 对其他作物的氮素营养估测提供了借鉴和参考。     

Design of Experiments-Based Monitoring of Critical Quality Attributes for the Spray-Drying Process of Insulin by NIR Spectroscopy

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q² of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.KEY WORDS: moisture content, multivariate analysis, NIR, particle size, spray-drying     

Applying near-infrared spectroscopy in downstream processing: one calibration for multiple clarification processes of fermentation media

The use of near-infrared spectroscopy (NIRS) is demonstrated in the first downstream processing (DSP) steps of an active pharmaceutical ingredient (API) manufacturing process. The first method developed was designed to assess the API content in the filtrate stream (aqueous) of a rotary drum vacuum filter. The PLS method, built after spectral preprocessing and variable selection, had an accuracy of 0.01% (w/w) for an API operational range between 0.20 and 0.45% (w/w). The robustness and extrapolation ability of the calibration was proved when samples from ultrafiltration and nanofiltration processes, ranging from 0 to 2% (w/w), were linearly predicted (R2=0.99). The development of a robust calibration model is generally a very time-consuming task, and once established it is imperative that it can be useful for a long period of time. This work demonstrates that NIR procedures, when carefully developed, can be used in different process conditions and even in different process steps of similar unit operations.     

基于NIRS法快速检测荞麦蛋白质与淀粉含量的研究

为建立近红外光谱技术测定荞麦蛋白质与淀粉含量的方法,本研究以217份荞麦样品为试验材料,采用最小二乘回归预测和交叉验证构建近红外预测模型。分析表明:前处理采用多元散射校正法(MSC),维数(Rank)分别为5和5,光谱区间6803.9~6094.2/cm所建立的荞麦蛋白质与淀粉含量模型的预测效果较好,其决定系数(R~2)分别为0.9481和0.9167,交叉验证均方根(RMSECV)分别为0.68和2.08,相对分析误差(RPD)分别为4.39和3.46,均大于3.0,外部验证相关系数均大于0.96。本试验所建立的蛋白质与淀粉含量近红外预测模型具有较高的准确度和稳健性,可用于荞麦品质的快速测定。     

Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n = 149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 μm. The results show the NIR technique to have promise as a semi-quantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows.     

Detecting and quantifying meat meal or meat and bone meal contamination in fishmeal by visible and near infrared reflectance spectra

The use of animal protein feeds such as meat meal or meat and bone meal (MMBM) play an important role in the feed manufacturing industry, but their safe and healthy use in animal feeds is of public concern in order to prevent the spread of bovine spongiform encephalopathy (BSE). The objective of the present work was to develop a technique using near infrared reflectance spectroscopy (NIRS) that would be suitable for detecting and quantifying contaminating levels of MMBM in fishmeal. To this end, a partial least squares (PLS) discriminant analysis and a modified partial least squares (MPLS) quantitative analysis, using visible and NIRS, were developed using a calibration set of 186 samples including 90 samples of pure fishmeal and 96 samples adulterated with MMBM at levels ranging from 10 to 320 g/kg. An external validation set, comprised of 39 pure samples and 54 adulterated samples, was used to validate the calibration model. A PLS discriminant analysis model developed with mathematic pretreatment 1,4,4,1, successfully detected fishmeal adulterated with MMBM. External validation indicated that all samples were discriminated correctly. A MPLS quantitative model, developed with mathematic pretreatment 1,4,4,1, also successfully predicted the MMBM in fishmeal with standard error of cross-validation (SECV) of 27.89 g/kg and ratio of the standard deviation of the validation set to the standard error of prediction (RPD) of 3.37. The calibration and validation results confirm that NIRS could provide the feed industry and inspection bodies with a rapid, non-destructive and non-invasive technique for the detection and quantification of MMBM in fishmeal.     

Hybrid modeling framework for process analytical technology: application to Bordetella pertussis cultures

In the process analytical technology (PAT) initiative, the application of sensors technology and modeling methods is promoted. The emphasis is on Quality by Design, online monitoring, and closed-loop control with the general aim of building in product quality into manufacturing operations. As a result, online high-throughput process analyzers find increasing application and therewith high amounts of highly correlated data become available online. In this study, an hybrid chemometric/mathematical modeling method is adopted for data analysis, which is shown to be advantageous over the commonly used chemometric techniques in PAT applications. This methodology was applied to the analysis of process data of Bordetella pertussis cultivations, namely online data of near-infrared, (NIR), pH, temperature and dissolved oxygen, and off-line data of biomass, glutamate, and lactate concentrations. The hybrid model structure consisted of macroscopic material balance equations in which the specific reactions rates are modeled by nonlinear partial least square (PLS). This methodology revealed a significant higher statistical confidence in comparison to PLSs, translated in a reduction of mean squared prediction errors (e.g., individual root mean squared prediction errors calibration/validation obtained through the hybrid model for the concentrations of lactate: 0.8699/0.7190 mmol/L; glutamate: 0.6057/0.2917 mmol/L; and biomass: 0.0520/0.0283 OD; and obtained through the PLS model for the concentrations of lactate: 1.3549/1.0087 mmol/L; glutamate: 0.7628/0.3504 mmol/L; and biomass: 0.0949/0.0412 OD). Moreover, the analysis of loadings and scores in the hybrid approach revealed that process features can, as for PLS, be extracted by the hybrid method.     

External versus internal source of calcium during the gelation of alginate beads for DNA encapsulation

Alginate gels produced by an external or internal gelation technique were studied so as to determine the optimal bead matrix within which DNA can be immobilized for in vivo application. Alginates were characterized for guluronic/mannuronic acid (G/M) content and average molecular weight using 1H-NMR and LALLS analysis, respectively. Nonhomogeneous calcium, alginate, and DNA distributions were found within gels made by the external gelation method because of the external calcium source used. In contrast, the internal gelation method produces more uniform gels. Sodium was determined to exchange for calcium ions at a ratio of 2:1 and the levels of calcium complexation with alginate appears related to bead strength and integrity. The encapsulation yield of double-stranded DNA was over 97% and 80%, respectively, for beads formed using external and internal calcium gelation methods, regardless of the composition of alginate. Homogeneous gels formed by internal gelation absorbed half as much DNAse as compared with heterogeneous gels formed by external gelation. Testing of bead weight changes during formation, storage, and simulated gastrointestinal (GI) conditions (pH 1.2 and 7.0) showed that high alginate concentration, high G content, and homogeneous gels (internal gelation) result in the lowest bead shrinkage and alginate leakage. These characteristics appear best suited for stabilizing DNA during GI transit.