Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 1. Gluten powder

Calibration transfer between NIR instruments using optimally predictive calibration subsets

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Amyloid-like Aggregation of Wheat Gluten and Its Components during Cooking: Mechanisms and Structural Characterization

Amyloid-like aggregation widely occurs during the processing and production of natural proteins, with evidence indicating its presence following the thermal processing of wheat gluten. However, significant gaps remain in understanding the underlying fibrillation mechanisms and structural polymorphisms. In this study, the amyloid-like aggregation behavior of wheat gluten and its components (glutenin and gliadin) during cooking was systematically analyzed through physicochemical assessment and structural characterization. The presence of amyloid-like fibrils (AFs) was confirmed using X-ray diffraction and Congo red staining, while Thioflavin T fluorescence revealed different patterns and rates of AFs growth among wheat gluten, glutenin, and gliadin. AFs in gliadin exhibited linear growth curves, while those in gluten and glutenin showed S-shaped curves, with the shortest lag phase and fastest growth rate (t1/2 = 2.11 min) observed in glutenin. Molecular weight analyses revealed AFs primarily in the 10-15 kDa range, shifting to higher weights over time. Glutenin-derived AFs had the smallest ζ-potential value (-19.5 mV) and the most significant size increase post cooking (approximately 400 nm). AFs in gluten involve interchain reorganization, hydrophobic interactions, and conformational transitions, leading to additional cross β-sheets. Atomic force microscopy depicted varying fibril structures during cooking, notably longer, taller, and stiffer AFs from glutenin.

Calibration set reduction by the selection of a subset containing the best fitting samples showing optimally predictive ability

Near-infrared (NIR) spectroscopy is a rapid, non-invasive and cost-effective technique, for which sample pre-treatment is often not required. It is applied for both qualitative and quantitative analyses in various application fields. Often, large calibration sets are used, from which informative subsets can be selected without a loss of meaningful information. In this study, a new approach for sample subset selection is proposed and evaluated. The global PLS model, obtained with the original large global calibration set after FCAM-SIG variable selection, is used for the selection of the best fitting subset of calibration samples with optimally predictive ability. This best fitting calibration subset is called the optimally predictive calibration subset (OPCS). After ranking the global calibration samples according to increasing residuals, different enlarging fractions of the ranked calibration set are selected. For each fraction, the optimal predictive ability and the corresponding optimal PLS complexity are determined by cross model validation (CMV). After performing CMV with all fractions, the fraction with the best fitting samples and optimally predictive ability, i.e. the OPCS, is determined. The use of the best fitting samples from the global PLS model results in an OPCS-based model which is similar to the global PLS model and has a similar predictive ability. Because the best fitting samples do not need to be representative for the global calibration set, but only need to support the OPCS-based model, the number of samples in the OPCS model is mostly smaller than that selected by a traditional representative sample subset selection method. The new OPCS approach is tested on three real life NIR data sets with twelve X-y combinations to model. The results show that the number of selected samples obtained by the OPCS approach is statistically significantly lower than (i) that of the most suitable and widely used representative sample selection method of Kennard and Stone, and (ii) that suggested by the guideline that the optimal sample size N for reduced calibration sets should surpass the PLS model complexity A by a factor 12. An additional advantage of the OPCS approach is that no outliers are included in the subset because only the best fitting calibration samples are selected. In the new OPCS approach, two additional innovations are built in: (i) CMV is for the first time applied for sample selection and (ii) in CMV, the "one standard error rule", adopted from "Repeated Double Cross Validation", is for the first time used for the determination of the optimal PLS complexity of the OPCS-based models.

Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study

High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques.

Identification of gallbladder cancer by direct near-infrared measurement of raw bile combined with two-trace two-dimensional correlation analysis

We demonstrated the utility of direct near-infrared (NIR) bile analysis for the identification of gallbladder (GB) cancer by employing two-trace two-dimensional (2T2D) correlation analysis to recognize dissimilar spectral features among diverse bile samples for potential improvement of discrimination accuracy. To represent more diverse clinical cases for reliable assessment, bile samples obtained from five normal, 44 gallstone, 25 GB polyp, six hepatocellular cancer (HCC), and eight GB cancer subjects were analyzed. Due to the altered metabolic pathways by carcinogenesis, the NIR spectral features of GB cancer samples, including intensity ratios of main peaks, were different from those of other sample groups. The differentiation of GB cancer in the principal component (PC) score domain was mediocre and subsequent discrimination accuracy based on linear discriminant analysis (LDA) was 88.5%. When 2T2D slice spectra were obtained using a reference spectrum constructed by the linear combination of the spectra of five pure representative bile metabolites and employed, the accuracy was improved to 95.6%. The sensitive recognition of dissimilar spectral features in GB cancer by 2T2D correlation analysis was responsible for the enhanced discrimination.

Conformational Changes in the Structure of Dough and Bread Enriched with Pumpkin Seed Flour

Pumpkin seed flour is a promising raw material for use in the technology of various bakery products. It has a high biological value and valuable amino acid profile. During the technological process of making bread, there are conformational changes in the protein structure. The purpose of the study was to determine the effect of pumpkin seed flour on conformational changes in the structure of protein substances of dough and bread from wheat flour by near-infrared reflection spectroscopy. The protein profile changed to complete when replacing 10% or more of wheat flour because the score for all amino acids was higher than 100%. The utilitarian coefficient indicates the same balance of amino acids in proteins of all samples. As the percentage of substitution increases, the number of amino acids used for anabolic purposes decreases, and these are more fully utilized by the body.

Gold-based nanoplatform for a rapid lateral flow immunochromatographic test assay for gluten detection

Background

Gluten, a food allergen, is available in foods derived from wheat, rye and barley. It damages the small intestine and causes celiac disease. Herein, we designed a rapid immunochromatographic lateral flow test assay for detecting the gluten contents of raw materials. In this rapid test, the presence of gluten was screened through the capturing of gliadin (a toxic component of gluten) by two identical gliadin monoclonal antibodies. One of the antibodies was immobilized on the membrane in the test zone as a capture reagent. The other antibody was labeled with gold nanoparticles (AuNPs) as a detector reagent.

Results

Gold nanoparticles with a size of about 20 nm were synthesized and conjugated to the gliadin monoclonal antibodies. The detection limit of the experimental assay was 20 ppm and positive results were visualized after 15 min using only 40 μL of the extracted sample for each test. Analysis of different flour samples identified the best sensitivity and specificity of the lateral flow test strip (LFTS).

Conclusion

The experimental LFTS is an easy-to-use and rapid method for the screening of gluten level in raw materials. The LFTS may be employed to ensure the safety of foods.

Rapid spectroscopic method for quantifying gluten concentration as a potential biomarker to test adulteration of green banana flour

The demand for gluten-free banana flour has led manufactures to enforce strict measures for quality control. A need has arisen for the development of more sensitive and reliable methods to test the quality of green banana flour (GBF). The objective of this study was to develop rapid visible to near-infrared (Vis-NIR) based spectroscopic models to detect gluten concentration, as a biomarker to detect wheat flour adulteration in green banana flour (GBF). Spectroscopic data were acquired using a desktop (FOSS®) Vis-NIR spectroscopy ranging from 400 to 2500 nm of the electromagnetic spectrum. The spectral and reference data were submitted to principal component analysis (PCA) and partial least squares regression (PLSR) for the development of gluten adulteration detection models. Calibration models were constructed based on a full cross-validation approach, consisting of 51 samples for the calibration set and 21 samples for the test set. PCA scores plot discriminated gluten adulterated and unadulterated GBF samples with 100% accuracy for the first two principal components (PCs). The optimal prediction model was obtained after a combination of baseline (offset and baseline linear correlation) and standard normal variate (SNV) pre-processing technique. This model showed a 94% coefficient of determination of cross-validation (R²_cv) and prediction (R²_p); root mean square error of cross-validation (RMSECV) of 3.7 mg/kg, root mean square error of prediction (RMSEP) of 3.9 mg/kg; and RPD value of 4. This work has demonstrated that Vis-NIRS method is a robust and feasible technology that may be used to ensure the safety of banana flour and that this product stays gluten-free by providing good and reliable gluten detection and quantification prediction models.

In situ monitoring of grape seed protein hydrolysis by Raman spectroscopy

Raman spectroscopy was used to monitor the enzymatic hydrolysis process of grape seed protein. The degree of hydrolysis (DH), IC₅₀ of the ACE inhibitory activity, and peptide content of the digestive products of grape seed protein were analyzed offline. The partial least squares (PLS), interval partial least squares (IPLS), and joint interval partial least squares (Si-PLS) models of DH, IC₅₀ , and peptide content were established and the optimal pretreatment method was selected. In the optimal model, the corrected model r of the grape seed protein hydrolysis degree is 0.997, the Root Mean Square Error of Cross Validation (RMSECV) is 0.507%. The predicted model r value is 0.9932, the Root Mean Square Error of Prediction (RMSEP) is 1.15%. The corrected model r value of the IC₅₀ is 0.9965, the RMSECV is 11.9%. The r value and RMSEP of predicted model are 0.9978 and 9.64%. The corrected model r value of the peptide content is 0.9955, the RMSECV is 12.7%, the predicted model r value is 0.9953, and the RMSEP is 15.4%. These results showed that in situ real-time monitoring of grape seed protein hydrolysis process can be achieved by Raman spectroscopy. PRACTICAL APPLICATIONS: This study uses Raman spectroscopy method to establish the quantification of proteolysis, IC50, and peptide content of the simulated digestive products during grape seed proteolysis. Analyze the model to monitor and evaluate the target parameters during the entire grape seed proteolysis process. In situ real-time monitoring of grape seed proteolysis is of great significance to the entire grape seed active peptide industry.

Effects of Physical and Chemical Factors on the Structure of Gluten, Gliadins and Glutenins as Studied with Spectroscopic Methods

This review presents applications of spectroscopic methods, infrared and Raman spectroscopies in the studies of the structure of gluten network and gluten proteins (gliadins and glutenins). Both methods provide complimentary information on the secondary and tertiary structure of the proteins including analysis of amide I and III bands, conformation of disulphide bridges, behaviour of tyrosine and tryptophan residues, and water populations. Changes in the gluten structure can be studied as an effect of dough mixing in different conditions (e.g., hydration level, temperature), dough freezing and frozen storage as well as addition of different compounds to the dough (e.g., dough improvers, dietary fibre preparations, polysaccharides and polyphenols). Additionally, effect of above mentioned factors can be determined in a common wheat dough, model dough (prepared from reconstituted flour containing only wheat starch and wheat gluten), gluten dough (lack of starch), and in gliadins and glutenins. The samples were studied in the hydrated state, in the form of powder, film or in solution. Analysis of the studies presented in this review indicates that an adequate amount of water is a critical factor affecting gluten structure.

In-situ analysis of the water distribution and protein structure of dough during ultrasonic-assisted freezing based on miniature Raman spectroscopy

The effect of ultrasonic-assisted freezing (UAF) on the water distribution of dough and molecular structure of gluten was in-situ monitored by low field nuclear magnetic resonance (LF-NMR) and micro-miniature Raman spectroscopy in this research. The results showed that UAF treatment increased the bound water content between 5 min and 30 min, and weakened the signal intensity of hydrogen protons due to the ultrasound enhanced heat and mass transfer during the freezing process. In-situ Raman spectra analysis indicated that UAF delayed the transition from embedded to exposure of tyrosine and tryptophan residues during the freezing process. Meanwhile, UAF reduced the number of hydrogen bonds, gauche-gauche-gauche (g-g-g) conformation breakage, the degree of α-helix to random coil conversion and damage to the gluten network during the freezing process. UAF treatment reduced the water mobility and breakage of non-covalent bonds, which prevented a dramatic shift in the protein advanced conformation during the freezing process, thereby improving the quality of frozen dough.

Characterisation of the quality alterations in model fat-filled milk powders under inclement conditions and the prediction of the storage time using near infrared spectroscopy

Fat-filled milk powders (FMP) are exported to tropical developing markets as inexpensive milk alternatives. Consequently, FMP are exposed to high temperature and humidity over long distribution and storage times, presenting challenges in preserving product quality and stability. Efficient and cost-effective methods for quality assurance under such conditions are needed. We utilised the changes in profile of the fatty acids, amino acids and near infrared spectra to investigate the quality alterations in 4 types of FMP produced onsite with 4 different vegetable oils (i.e., coconut, palm, soya-bean and sunflower) and stored for 7 weeks at 40 °C. Stearic acid decreased while the leucine content increased upon storage, but palm oil FMP appeared to be the most stable. Multiclass analyses offered substantive separation between fresh and aged samples. The models based on interval-PLS efficiently (NSE ≥ 0.90) predicted storage time with low errors (RSR ≤ 0.28), indicative of FMP freshness and stability.

Feasibility study for rapid near-infrared spectroscopic identification of different gallbladder diseases by direct analysis of bile juice

A whole-sample-covering near-infrared (NIR) spectroscopy scheme has been adopted for the simple drop-and-dry measurement of raw bile juice for the identification of gallbladder (GB) diseases of stone, polyp, and cancer. For reproducible measurement, a non-NIR absorbing polytetrafluoroethylene (PTFE) providing a hydrophobic surface was chosen as a substrate to form bile juice droplets of a consistent shape. To ensure representative spectroscopic sampling, NIR radiation illuminated the whole area of the dried sample for spectral acquisition. The NIR band shapes and relative band intensities of GB cancer differed moderately from those of GB stone and GB polyp. The composition of GB cancer samples was presumed to be dissimilar from other sample compositions. Differentiation between GB polyp and GB stone, however, was less facile; nevertheless, in the case of GB polyp samples, the obtained NIR features were informative in the identification of various pathological conditions such as adenomyomatosis (abnormal growth of epidermal tissue) and hepatitis B. To elucidate the NIR features of bile juice samples, separate NIR spectra of major bile constituents such as conjugated bile salts, lecithin, cholesterol, and albumin were analyzed. The demonstrated NIR spectroscopy scheme requiring no sample pretreatment or separation of bile juice could be useful for fast bile juice-based screening of GB diseases, especially the identification of early GB cancer.

Conditions Governing Food Protein Amyloid Fibril Formation-Part I: Egg and Cereal Proteins

Conditions including heating mode, time, temperature, pH, moisture and protein concentration, shear, and the presence of alcohols, chaotropic/reducing agents, enzymes, and/or salt influence amyloid fibril (AF) formation as they can affect the accessibility of amino acid sequences prone to aggregate. As some conditions applied on model protein resemble conditions in food processing unit operations, we here hypothesize that food processing can lead to formation of protein AFs with a compact cross β-sheet structure. This paper reviews conditions and food constituents that affect amyloid fibrillation of egg and cereal proteins. While egg and cereal proteins often coexist in food products, their impact on each other's fibrillation remains unknown. Hen egg ovalbumin and lysozyme form AFs when subjected to moderate heating at acidic pH separately. AFs can also be formed at higher pH, especially in the presence of alcohols or chaotropic/reducing agents. Tryptic wheat gluten digests can form fibrillar structures at neutral pH and maize and rice proteins do so in aqueous ethanol or at acidic pH, respectively.

In Situ Monitoring of the Effect of Ultrasound on the Sulfhydryl Groups and Disulfide Bonds of Wheat Gluten

Ultrasound treatment can improve enzymolysis efficiency by changing the amounts of sulfhydryl groups (SH) and disulfide bonds (SS) in protein. This paper proposes an in-situ and real-time monitoring method for SH and SS during ultrasound application processes using a miniature near-infrared (NIR) optical fiber spectrometer and a chemometrics model to determine the endpoint of ultrasonic treatment. The results show that SH and SS contents fluctuated greatly with the extension of ultrasonic time. The optimal spectral intervals for SH content were 869–947, 1207–1284, 1458–1536 and 2205–2274 nm, the optimal spectral intervals of SS content were 933–992, 1388–1446, 2091–2148 and 2217–2274 nm. According to the optimal spectral intervals, the synergy interval partial least squares (Si-PLS) and error back propagation neural network (BP-ANN) for SH, SS contents were established. The BP-ANN model was better than the Si-PLS model. The correlation coefficient of the prediction set (R_p) and the root mean square error of prediction (RMSEP) for the BP-ANN model of SH were 0.9113 and 0.38 μmol/g, respectively, the R_p² and residual prediction deviation of SH were 0.8305 and 2.91, respectively. For the BP-ANN model of SS, the R_p and the RMSEP were 0.7523 and 6.56 μmol/g, respectively. The R_p² and residual prediction deviation (RPD) of SS were 0.8305 and 2.91, respectively. However, the R_p² and RPD of SS was 0.5660 and 1.64, respectively. This work demonstrated that the miniature NIR combined with BP-ANN algorithms has high potential for in-situ monitoring of SH during the ultrasonic treatment process, while the spectral prediction model of SS needs to be further developed.

Protein enriched pasta: structure and digestibility of its protein network

Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

Optimizing Savitzky-Golay parameters for improving spectral resolution and quantification in infrared spectroscopy

Calculating derivatives of spectral data by the Savitzky-Golay (SG) numerical algorithm is often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress unwanted spectral features that arise due to nonideal instrument and sample properties. Addressing these issues, a study of the simulated and measured infrared data by partial least-squares regression has been conducted. The simulated data sets were modeled by considering a range of undesired chemical and physical spectral anomalies and variations that can occur in a measured spectrum, such as baseline variations, noise, and scattering effects. The study has demonstrated the importance of the optimization of the SG parameters during the conversion of spectra into derivative form, specifically window size and polynomial order of the fitting curve. A specific optimal window size is associated with an exact component of the system being estimated, and this window size does not necessarily apply for some other component present in the system. Since the optimization procedure can be time-consuming, as a rough guideline spectral noise level can be used for assessment of window size. Moreover, it has been demonstrated that, when the extended multiplicative signal correction (EMSC) is used alongside the SG procedure, the derivative treatment of data by the SG algorithm must precede the EMSC normalization.

Predictive-property-ranked variable reduction with final complexity adapted models in partial least squares modeling for multiple responses

For partial least-squares regression with one response (PLS1), many variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative variables. Many variable-reduction methods are based on various PLS-model-related parameters, called predictor-variable properties. Recently, an important adaptation, in which the model complexity is optimized, was introduced in these methods. This method was called Predictive-Property-Ranked Variable Reduction with Final Complexity Adapted Models, denoted as PPRVR-FCAM or simply FCAM. In this study, variable reduction for PLS2 models, using an adapted FCAM method, FCAM-PLS2, is investigated. The utility and effectiveness of four new predictor-variable properties, derived from the multiple response PLS2 regression coefficients, are studied for six data sets consisting of ultraviolet-visible (UV-vis) spectra, near-infrared (NIR) spectra, NMR spectra, and two simulated sets, one with correlated and one with uncorrelated responses. The four properties include the mean of the absolute values as well as the norm of the PLS2 regression coefficients and their significances. The four properties were found to be applicable by the FCAM-PLS2 method for variable reduction. The predictive abilities of models resulting from the four properties are similar. The norm of the PLS2 regression coefficients has the best selective abilities, low numbers of variables with an informative meaning to the responses are retained. The significance of the mean of the PLS2 regression coefficients is found to be the least-selective property.

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

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 Q (2) 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.

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.

Determination of secondary structural changes in gluten proteins during mixing using Fourier transform horizontal attenuated total reflectance spectroscopy

Fourier transform horizontal attenuated total reflectance (FT-HATR) was used to examine changes in the secondary structure of gluten proteins in a flour-water dough system during mixing. Midinfrared spectra of mixed dough revealed changes in four bands in the amide III region associated with secondary structure in proteins: 1317 (alpha-helix), 1285 (beta-turn), 1265 (random coil), and 1242 cm (-1) (beta-sheet). The largest band, which also showed the greatest change in second derivative band area (SDBA) during mixing, was located at 1242 cm (-1). The bands at 1317 and 1285 cm (-1) also showed an increase in SDBA over time. Conversely, the band at 1265 cm (-1) showed a corresponding decrease over time as the doughs were mixed. All bands reached an optimum corresponding to the minimum mobility of the dough as determined by the mixograph. Increases in alpha-helix, beta-turn, and beta-sheet secondary structures during mixing suggest that the dough proteins assume a more ordered conformation. These results demonstrate that it is possible, using infrared spectroscopic techniques, to relate the rheological behavior of developing dough in a mixograph directly to changes in the structure of the gluten protein system.