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).

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.

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 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.

Assessment of the scalability of a microtiter plate system for screening of oleaginous microorganisms.

Recent developments in molecular biology and metabolic engineering have resulted in a large increase in the number of strains that need to be tested, positioning high-throughput screening of microorganisms as an important step in bioprocess development. Scalability is crucial for performing reliable screening of microorganisms. Most of the scalability studies from microplate screening systems to controlled stirred-tank bioreactors have been performed so far with unicellular microorganisms. We have compared cultivation of industrially relevant oleaginous filamentous fungi and microalga in a Duetz-microtiter plate system to benchtop and pre-pilot bioreactors. Maximal glucose consumption rate, biomass concentration, lipid content of the biomass, biomass, and lipid yield values showed good scalability for Mucor circinelloides (less than 20% differences) and Mortierella alpina (less than 30% differences) filamentous fungi. Maximal glucose consumption and biomass production rates were identical for Crypthecodinium cohnii in microtiter plate and benchtop bioreactor. Most likely due to shear stress sensitivity of this microalga in stirred bioreactor, biomass concentration and lipid content of biomass were significantly higher in the microtiter plate system than in the benchtop bioreactor. Still, fermentation results obtained in the Duetz-microtiter plate system for Crypthecodinium cohnii are encouraging compared to what has been reported in literature. Good reproducibility (coefficient of variation less than 15% for biomass growth, glucose consumption, lipid content, and pH) were achieved in the Duetz-microtiter plate system for Mucor circinelloides and Crypthecodinium cohnii. Mortierella alpina cultivation reproducibility might be improved with inoculation optimization. In conclusion, we have presented suitability of the Duetz-microtiter plate system for the reproducible, scalable, and cost-efficient high-throughput screening of oleaginous microorganisms.

Microtiter plate cultivation of oleaginous fungi and monitoring of lipogenesis by high-throughput FTIR spectroscopy.

BACKGROUND: Oleaginous fungi can accumulate lipids by utilizing a wide range of waste substrates. They are an important source for the industrial production of omega-6 polyunsaturated fatty acids (gamma-linolenic and arachidonic acid) and have been suggested as an alternative route for biodiesel production. Initial research steps for various applications include the screening of fungi in order to find efficient fungal producers with desired fatty acid composition. Traditional cultivation methods (shake flask) and lipid analysis (extraction-gas chromatography) are not applicable for large-scale screening due to their low throughput and time-consuming analysis. Here we present a microcultivation system combined with high-throughput Fourier transform infrared (FTIR) spectroscopy for efficient screening of oleaginous fungi. RESULTS: The microcultivation system enables highly reproducible fungal fermentations throughout 12 days of cultivation. Reproducibility was validated by FTIR and HPLC data. Analysis of FTIR spectral ester carbonyl peaks of fungal biomass offered a reliable high-throughput at-line method to monitor lipid accumulation. Partial least square regression between gas chromatography fatty acid data and corresponding FTIR spectral data was used to set up calibration models for the prediction of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, unsaturation index, total lipid content and main individual fatty acids. High coefficients of determination (R2 = 0.86-0.96) and satisfactory residual predictive deviation of cross-validation (RPDCV = 2.6-5.1) values demonstrated the goodness of these models. CONCLUSIONS: We have demonstrated in this study, that the presented microcultivation system combined with rapid, high-throughput FTIR spectroscopy is a suitable screening platform for oleaginous fungi. Sample preparation for FTIR measurements can be automated to further increase throughput of the system.

Fourier-transform infrared spectroscopy for characterization of protein chain reductions in enzymatic reactions.

The potential of dry-film Fourier-transform infrared (FTIR) measurements as a monitoring tool for enzymatic hydrolysis of protein-based substrates is explored in this study. As a proof-of-concept, the enzymatic digestion of bovine serum albumin using Alcalase was monitored. To evaluate the analytical approach on complex substrates with industrial relevance, salmon- and chicken-based substrates were digested for 80 minutes using Alcalase and a total of 12 FTIR spectra were acquired during the course of the hydrolysis. The observed changes in the IR spectral features as a function of hydrolysis time were found to be in agreement with the breakdown of the amide backbone and formation of amino and carboxylate terminals. Some of the most consistent markers for hydrolysis time were the bands at 1516 cm-1 (-NH3+) and ∼1400 cm-1 (-COO-). Moreover, principal component analysis (PCA) of the FTIR spectra was used to demonstrate the systematic relationship of the hydrolysis time with key variables (wavelengths) in the protein backbone region (800-1800 cm-1). Scores in the first principal component versus the hydrolysis time have been shown to provide an overview of the process dynamics related to protein structural changes. The herein presented results suggest that dry-film FTIR measurements have potential as a rapid tool for monitoring industrial protein hydrolysis processes.

Noninvasive analysis of thin turbid layers using microscale spatially offset Raman spectroscopy.

Here, we demonstrate, for the first time, the extension of applicability of recently developed microscale spatially offset Raman spectroscopy (SORS), micro-SORS, from the area of cultural heritage to a wider range of analytical problems involving thin, tens of micrometers thick diffusely scattering turbid layers. The method can be applied in situations where a high turbidity of layers prevents the deployment of conventional confocal Raman microscopy with its depth resolving capability. The method was applied successfully to detect noninvasively the presence of thin, highly turbid layers within polymers, wheat seeds, and paper. An invasive, cross sectional analysis confirmed the micro-SORS findings. Micro-SORS represents a new Raman imaging modality expanding the portfolio of noninvasive, chemically specific analytical tools.

Fourier transform infrared spectroscopy for the prediction of fatty acid profiles in Mucor fungi grown in media with different carbon sources.

Fungal production of polyunsaturated fatty acids (PUFAs) is a highly potential approach in biotechnology. Currently the main focus is directed towards screening of hundreds strains in order to select of few potential ones. Thus, a reliable method for screening a high number of strains within a short period of time is needed. Here, we present a novel method for screening of PUFA-producing fungi by high-throughput microcultivation and FTIR spectroscopy. In the study selected Mucor fungi were grown in media with different carbon sources and fatty acid profiles were predicted on the basis of the obtained spectral data. FTIR spectra were calibrated against fatty acid analysis by GC-FD. The calibration models were cross-validated and correlation coefficients (R2) from 0.71 to 0.78 with RMSECV (root mean squared error) from 2.86% to 6.96% (percentage of total fat) were obtained. The FTIR results show a strong correlation to the results obtained by GC analysis, where high total contents of unsaturated fatty acids (both PUFA and MUFA) were achieved for Mucor plumbeus VI02019 cultivated in canola, olive and sunflower oil and Mucor hiemalis VI01993 cultivated in canola and olive oil.

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.

In vitro gastrointestinal stability and intestinal absorption of ACE-1 and DPP4 inhibitory peptides from poultry by-product hydrolysates.

Bioactive peptides derived from food are promising health-promoting ingredients that can be used in functional foods and nutraceutical formulations. In addition to the potency towards the selected therapeutic target, the bioavailability of bioactive peptides is a major factor regarding clinical efficacy. We have previously shown that a low molecular weight peptide fraction (LMWPF) from poultry by-product hydrolysates possesses angiotensin-1-converting enzyme (ACE-1) and dipeptidyl-peptidase 4 (DPP4) inhibitory activities. The present study aimed to investigate the bioavailability of the bioactive peptides in the LMWPF. Prior to the investigation of bioavailability, a dipeptide YA was identified from this fraction as a dual inhibitor of ACE-1 and DPP4. Gastrointestinal (GI) stability and intestinal absorption of the bioactive peptides (i.e., YA as well as two previously reported bioactive dipeptides (VL and IY)) in the LMWPF were evaluated using the INFOGEST static in vitro digestion model and intestinal Caco-2 cell monolayer, respectively. Analysis of peptides after in vitro digestion confirmed that the dipeptides were resistant to the simulated GI conditions. After 4 hours of incubation, the concentration of the peptide from the apical side of the Caco-2 cell monolayer showed a significant decrease. However, the corresponding absorbed peptides were not detected on the basolateral side, suggesting that the peptides were not transported across the intestinal monolayer but rather taken up or metabolized by the Caco2 cells. Furthermore, when analyzing the gene expression of the Caco-2 cells upon peptide stimulation, a down-regulation of peptide transporters, the transcription factor CDX2, and the tight junction protein-1 (TJP1) was observed, suggesting the specific effects of the peptides on the Caco-2 cells. The study demonstrated that bioactive dipeptides found in the LMWPF were stable through in vitro GI digestion; however, the overall bioavailability may be hindered by inadequate uptake across the intestinal barrier.

A portable dry film FTIR instrument for industrial food and bioprocess applications.

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

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.

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.

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.

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.

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.

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.

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.