Glucoregulatory Properties of a Protein Hydrolysate from Atlantic Salmon (Salmo salar): Preliminary Characterization and Evaluation of DPP-IV Inhibition and Direct Glucose Uptake In Vitro.

Metabolic disorders are increasingly prevalent conditions that manifest pathophysiologically along a continuum. Among reported metabolic risk factors, elevated fasting serum glucose (FSG) levels have shown the most substantial increase in risk exposure. Ultimately leading to insulin resistance (IR), this condition is associated with notable deteriorations in the prognostic outlook for major diseases, including neurodegenerative diseases, cancer risk, and mortality related to cardiovascular disease. Tackling metabolic dysfunction, with a focus on prevention, is a critically important aspect for human health. In this study, an investigation into the potential antidiabetic properties of a salmon protein hydrolysate (SPH) was conducted, focusing on its potential dipeptidyl peptidase-IV (DPP-IV) inhibition and direct glucose uptake in vitro. Characterization of the SPH utilized a bioassay-guided fractionation approach to identify potent glucoregulatory peptide fractions. Low-molecular-weight (MW) fractions prepared by membrane filtration (MWCO = 3 kDa) showed significant DPP-IV inhibition (IC50 = 1.01 ± 0.12 mg/mL) and glucose uptake in vitro (p ≤ 0.0001 at 1 mg/mL). Further fractionation of the lowest MW fractions (<3 kDa) derived from the permeate resulted in three peptide subfractions. The subfraction with the lowest molecular weight demonstrated the most significant glucose uptake activity (p ≤ 0.0001), maintaining its potency even at a dilution of 1:500 (p ≤ 0.01).

Diagnosing the cage of covariance to increase understanding and robustness of spectroscopic calibration models.

When vibrational spectroscopy is used for quantification purposes, multivariate analysis is often used to extract information from covariances between the spectra and any given reference values. In complex samples, there is a high risk that the constituents covary with each other. In such scenarios many methods may confuse the analytes and use signal from several analytes, rather than just the analyte of interest. While this allows the method to use more signal, and thus have a better effective signal-to-noise ratio, it also makes them less robust to changes to the chemical composition in the samples. This effect has been termed the cage of covariance. In order to avoid cage of covariance to affect predictive performances, it is highly important to have simple diagnostic tools to analyze and review this effect. Therefore, in the present paper, a systematic overview of tools for diagnosing and quantifying the cage of covariance in spectroscopic calibration models is provided. A collection of previously published methods with some expansions is provided, as well as two completely new tools: covariance ratio and virtual spiking. Practical applications of the tools on three different datasets are also shown.

The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry.

It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.

Genetic analyses of nutrient digestibility measured by fecal near-infrared spectroscopy in pigs.

Improved nutrient digestibility is an important trait in genetic improvement in pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. Further, poor nutrient digestibility represents a direct nutrient loss, which affects the profit of the farmer. The aim of this study was to estimate genetic parameters for apparent total tract digestibility of nitrogen (ATTDn), crude fat (ATTDCfat), dry matter (ATTDdm), and organic matter (ATTDom) and to investigate their genetic relationship to other relevant production traits in pigs. Near-infrared spectroscopy was used for prediction of total nitrogen content and crude fat content in feces. The predicted content was used to estimate apparent total tract digestibility of the different nutrients by using an indicator method, where acid insoluble ash was used as an indigestible marker. Average ATTDdm, ATTDom, ATTDn, and ATTDCfat ranged from 61% to 75.3%. Moderate heritabilities was found for all digestibility traits and ranged from 0.15 to 0.22. The genetic correlations among the digestibility traits were high (>0.8), except for ATTDCfat, which had no significant genetic correlation to the other digestibility traits. Significant genetic correlations were found between ATTDn and feed consumption between 40 and 120 kg live weight (F40120) (-0.54 ± 0.11) and ATTDdm and F40120 (-0.35 ± 0.12) and ATTDom and F40120 (-0.28 ± 0.13). No significant genetic correlations were found between digestibility traits and loin depth at 100 kg, nor backfat thickness at 100 kg (BF), except between BF and ATTDn (-0.31 ± 0.14). These results suggested that selection for improved feed efficiency through reduced feed intake within a weight interval, also has led to improved ATTDdm, ATTDom, and ATTDn. Further, the digestibility traits are heritable, but mainly related to feed intake and general function of the intestines, as opposed to allocation of feed resources to different tissues in the body.

Improved nutrient digestibility is an important trait in genetic improvement of pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. The main aim of this study was to investigate whether nutrient digestibility traits in pigs are heritable, and if they are genetically linked to other production traits. The results showed that digestibility of dry matter, organic matter, nitrogen, and crude fat are heritable, and can be selected for in a pig breeding program. The traits are genetically linked to other relevant production traits, such as feed intake, but not to carcass traits, such as loin depth. The results suggest that nutrient digestibility are traits that can be selected for, and that the traits are under indirect selection through other traits in the pig breeding program. The results also indicate that the nutrient digestibility traits express how well the animal utilizes consumed feed, rather than allocating feed to different tissue deposition.

FTIR-based prediction of collagen content in hydrolyzed protein samples.

Fourier transform infrared spectroscopy (FTIR) is a powerful analytical tool that has been used for protein and peptide characterization for decades. In the present study, the objective was to investigate if FTIR can be used to predict collagen content in hydrolyzed protein samples. All samples were obtained from enzymatic protein hydrolysis (EPH) of poultry by-products providing a span in collagen content from 0.3% to 37.9% (dry weight), and the FTIR analysis was performed using dry film FTIR. Since nonlinear effects were revealed by calibration using standard partial least squares (PLS) regression, Hierarchical Cluster-based PLS (HC-PLS) calibration models were constructed. The HC-PLS model provided a low prediction error when validated using an independent test set (RMSE = 3.3% collagen), while validation using real industrial samples also showed satisfying results (RMSE = 3.2%). The results corresponded well with previously published FTIR-based studies of collagen, and characteristic spectral features for collagen were well identified in the regression models. Covariance between collagen content and other EPH related processing parameters could also be ruled out in the regression models. To the authors' knowledge, this is the first time that collagen content has been systematically studied in solutions of hydrolysed proteins using FTIR. This is also one of few examples where FTIR is successfully used to quantify protein composition. The dry-film FTIR approach presented in the study is expected to be an important tool in the growing industrial segment that is based on sustainable utilization of collagen-rich biomass.

Raman spectroscopy and NIR hyperspectral imaging for in-line estimation of fatty acid features in salmon fillets.

Raman spectroscopy was compared with near infrared (NIR) hyperspectral imaging for determination of fat composition (%EPA + DHA) in salmon fillets at short exposure times. Fillets were measured in movement for both methods. Salmon were acquired from several different farming locations in Norway with different feeding regimes, representing a realistic variation of salmon in the market. For Raman, we investigated three manual scanning strategies; i) line scan of loin, ii) line scan of belly and iii) sinusoidal scan of belly at exposure times of 2s and 4s. NIR images were acquired while the fillets moved on a conveyor belt at 40 cm/s, which corresponds to an acquisition time of 1s for a 40 cm long fillet. For NIR images, three different regions of interest (ROI) were investigated including the i) whole fillet, ii) belly segment, and iii) loin segment. For both Raman and NIR measurements, we investigated an untrimmed and trimmed version of the fillets, both relevant for industrial in-line evaluation. For the trimmed fillets, a fat rich deposition layer in the belly was removed. The %EPA + DHA models were validated by cross validation (N = 51) and using an independent test set (N = 20) which was acquired in a different season. Both Raman and NIR showed promising results and high performances in the cross validation, with R2CV = 0.96 for Raman at 2s exposure and R2CV = 0.97 for NIR. High performances were obtained also for the test set, but while Raman had low and stable biases for the test set, the biases were high and varied for the NIR measurements. Analysis of variance on the squared test set residuals showed that performance for Raman measurements were significantly higher than NIR at 1% significance level (p = 0.000013) when slope-and-bias errors were not corrected, but not significant when residuals were slope-and-bias corrected (p = 0.28). This indicated that NIR was more sensitive to matrix effects. For Raman, signal-to-noise ratio was the main limitation and there were indications that Raman was close to a critical sample exposure time at the 2s signal accumulation.

Encoder-decoder neural networks for predicting future FTIR spectra - application to enzymatic protein hydrolysis.

In the process of converting food-processing by-products to value-added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lack good characterization and contain high batch variation, online or at-line monitoring of the enzymatic reactions would be beneficial. We investigate the potential of deep neural networks in predicting the future state of enzymatic hydrolysis as described by Fourier-transform infrared spectra of the hydrolysates. Combined with predictions of average molecular weight, this provides a flexible and transparent tool for process monitoring and control, enabling proactive adaption of process parameters.

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue-A Salmon Case Study.

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

Post-enzymatic hydrolysis heat treatment as an essential unit operation for collagen solubilization from poultry by-products.

Enzymatic protein hydrolysis (EPH) is an invaluable process to increase the value of food processing by-products. In the current work the aim was to study the role of standard thermal inactivation in collagen solubilization during EPH of poultry by-products. Hundred and eighty hydrolysates were produced using two proteases (stem Bromelain and Endocut-02) and two collagen-rich poultry by-products (turkey tendons and carcasses). Thermal inactivation was performed with and without the sediment to study the effect of heat on collagen solubilization. A large difference in molecular weight distribution profiles was observed when comparing hydrolysate time series of the two proteases. In addition, it was shown that 15 min heat treatment, conventionally used for inactivating proteases, is essential in solubilizing collagen fragments, which significantly contributes to increasing the protein yield of the entire process. The study thus demonstrated the possibility of producing tailored products of different quality by exploiting standard heat inactivation in EPH.

Feasibility of In-Line Raman Spectroscopy for Quality Assessment in Food Industry: How Fast Can We Go?

Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry.

Exploring Effects of Protease Choice and Protease Combinations in Enzymatic Protein Hydrolysis of Poultry By-Products.

A study of the effects of single and combined protease hydrolysis on myofibrillar versus collagenous proteins of poultry by-products has been conducted. The aim was to contribute with knowledge for increased value creation of all constituents of these complex by-products. A rational approach was implemented for selecting proteases exhibiting the most different activity towards the major protein-rich constituents of mechanically deboned chicken residue (MDCR). An initial activity screening of 18 proteases on chicken meat, turkey tendons and MDCR was conducted. Based on weight yield, size exclusion chromatography (SEC) and SDS-PAGE, stem Bromelain and Endocut-02 were selected. Studies on hydrolysis of four different poultry by-products at 40 °C, evaluated by protein yield, SEC, and SDS-PAGE, indicate that the proteases' selectivity difference can be utilized in tailor-making hydrolysates, enriched in either meat- and collagen-derived peptides or gelatin. Three modes of stem Bromelain and Endocut-02 combinations during hydrolysis of MDCR were performed and compared with single protease hydrolysis. All modes of the protease combinations resulted in a similar approximately 15% increase in product yield, with products exhibiting similar SEC and SDS-PAGE profiles. This shows that irrespective of the modes of combination, the use of more than one enzyme in hydrolysis of collagen-rich material can provide means to increase the total protein yield and ultimately contribute to increased value creation of poultry by-products.

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow's Lactation.

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows' lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

Improved estimation of in vitro protein digestibility of different foods using size exclusion chromatography.

While the harmonized INFOGEST model provides a physiologically relevant platform for simulated digestion, it needs to be combined with adequate analytical methods to enable quantification and comparison of protein digestibility in different food matrices. We have shown that size exclusion chromatography (SEC) can be used to estimate the proportion of small peptides potentially available for uptake. Combined with determination of total dissolved protein, the % of small peptides per total protein was calculated as a physiologically relevant estimate of protein digestibility (DSEC). Values for DSEC differed for casein (87.6%), chicken mince (72.6%), heated pea protein concentrate (67.8%), bread (63%), beef entrecote (57.7%) and pea protein concentrate (57.8%). In contrast to existing methods (TCA soluble protein, free NH2-groups), the proposed SEC based method gives separate insight into the two fundamental processes during protein digestion (solubilization and break-down), while maintaining the ability to rank digestibility of very different food proteins.

Characterization of Collagen Structure in Normal, Wooden Breast and Spaghetti Meat Chicken Fillets by FTIR Microspectroscopy and Histology.

Recently, two chicken breast fillet abnormalities, termed Wooden Breast (WB) and Spaghetti Meat (SM), have become a challenge for the chicken meat industry. The two abnormalities share some overlapping morphological features, including myofiber necrosis, intramuscular fat deposition, and collagen fibrosis, but display very different textural properties. WB has a hard, rigid surface, while the SM has a soft and stringy surface. Connective tissue is affected in both WB and SM, and accordingly, this study's objective was to investigate the major component of connective tissue, collagen. The collagen structure was compared with normal (NO) fillets using histological methods and Fourier transform infrared (FTIR) microspectroscopy and imaging. The histology analysis demonstrated an increase in the amount of connective tissue in the chicken abnormalities, particularly in the perimysium. The WB displayed a mixture of thin and thick collagen fibers, whereas the collagen fibers in SM were thinner, fewer, and shorter. For both, the collagen fibers were oriented in multiple directions. The FTIR data showed that WB contained more ß-sheets than the NO and the SM fillets, whereas SM fillets expressed the lowest mature collagen fibers. This insight into the molecular changes can help to explain the underlying causes of the abnormalities.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy.

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R2CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

Magnetic ligand fishing using immobilized DPP-IV for identification of antidiabetic ligands in lingonberry extract.

In this work, a new magnetic ligand fishing probe for discovery of DPP-IV inhibitory ligands was developed and it was tested as a proof of concept on the fruit extract of Vaccinium vitis-idaea (lingonberry). The ligands were shown to have appreciable dipeptidyl peptidase IV (DPP-IV) inhibitory activity (IC50: 31.8 µg mL-1).) Inhibition of DPP-IV is a well-known therapeutic approach for management of type 2 diabetes (T2D). DPP-IV was successfully immobilized onto magnetic beads and was shown to retain its catalytic activity and selectivity over a model mixture. A total of four ligands were successfully fished out and identified as cyanidin-3-galactoside (2), cyanidin-3-arabinoside (3), proanthocynidin A (4), and 10-carboxyl-pyranopeonidin 3-O-(6″-O-p-coumaroyl)-glucoside (5) using HPLC/HRMS.

Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy.

The main purpose of this study was to investigate if Raman spectra recorded at the exact same position as drip loss measurements could improve prediction of drip loss in pork. One ventral and one dorsal cylindrical plug, cut from a standardized slice from Longissimus lumborum, were used to determine drip loss by EZ-DripLoss method and to collect Raman spectra, while ultimate pH was measured at another location. Partial least squares regression models were developed using spectra from each plug individually or averaged spectra from both plugs. The best models used spectra from the ventral plug, resulting in rcv2=0.75, root mean square error of cross-validation (RMSECV) = 1.27% and ratio of prediction to deviation (RPD) =2.0 for EZ-DripLoss and rcv2=0.72, RMSECV = 0.05 and RPD = 2.0 for ultimate pH. Results indicate that Raman spectroscopy can be used for rough screening of drip loss and pH in pork, and that the location chosen for collection of spectra can be very important for successful predictions.

Fourier transform infrared spectroscopy of milk samples as a tool to estimate energy balance, energy- and dry matter intake in lactating dairy cows.

The objective of the study was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) analysis of milk samples to predict body energy status and related traits (energy balance (EB), dry matter intake (DMI) and efficient energy intake (EEI)) in lactating dairy cows. The data included 2371 milk samples from 63 Norwegian Red dairy cows collected during the first 105 days in milk (DIM). To predict the body energy status traits, calibration models were developed using Partial Least Squares Regression (PLSR). Calibration models were established using split-sample (leave-one cow-out) cross-validation approach and validated using an external test set. The PLSR method was implemented using just the FTIR spectra or using the FTIR together with milk yield (MY) or concentrate intake (CONCTR) as predictors of traits. Analyses were conducted for the entire first 105 DIM and separately for the two lactation periods: 5 ≤ DIM ≤ 55 and 55 < DIM ≤ 105. To test the models, an external validation using an independent test set was performed. Predictions depending on the parity (1st, 2nd and 3rd-to 6th parities) in early lactation were also investigated. Accuracy of prediction (r) for both cross-validation and external test set was defined as the correlation between the predicted and observed values for body energy status traits. Analyzing FTIR in combination with MY by PLSR, resulted in relatively high r-values to estimate EB (r = 0.63), DMI (r = 0.83), EEI (r = 0.84) using an external validation. Only moderate correlations between FTIR spectra and traits like EB, EEI and dry matter intake (DMI) have so far been published. Our hypothesis was that improvements in the FTIR predictions of EB, EEI and DMI can be obtained by (1) stratification into different stages of lactations and different parities, or (2) by adding additional information on milking and feeding traits. Stratification of the lactation stages improved predictions compared with the analyses including all data 5 ≤ DIM ≤105. The accuracy was improved if additional data (MY or CONCTR) were included in the prediction model. Furthermore, stratification into parity groups, improved the predictions of body energy status. Our results show that FTIR spectral data combined with MY or CONCTR can be used to obtain improved estimation of body energy status compared to only using the FTIR spectra in Norwegian Red dairy cattle. The best prediction results were achieved using FTIR spectra together with MY for early lactation. The results obtained in the study suggest that the modeling approach used in this paper can be considered as a viable method for predicting an individual cow's energy status.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Fourier-transform infrared spectroscopy for monitoring proteolytic reactions using dry-films treated with trifluoroacetic acid.

In this study we explore the potential of using Fourier-transform infrared (FTIR) spectra of trifluoroacetate-protein and peptide complexes for monitoring proteolytic reactions. The idea of treating dry-films of protein hydrolysates with trifluoroacetic acid (TFA) prior to FTIR analysis is based on the unique properties of TFA. By adding a large excess of TFA to protein hydrolysate samples, the possible protonation sites of the proteins and peptides will be saturated. In addition, TFA has a low boiling point when protonated as well as complex-forming abilities. When forming TFA-treated dry-films of protein hydrolysates, the excess TFA will evaporate and the deprotonated acid (CF3COO-) will interact as a counter ion with the positive charges on the sample materials. In the study, spectral changes in TFA-treated dry-films of protein hydrolysates from a pure protein and poultry by-products, were compared to the FTIR fingerprints of untreated dry-films. The results show that time-dependent information related to proteolytic reactions and, consequently, on the characteristics of the protein hydrolysates can be obtained. With additional developments, FTIR on dry-films treated with TFA may be regarded as a potential future tool for the analysis of all types of proteolytic reactions in the laboratory as well as in industry.