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Highlights • Plum tomato oil-in-water emulsions reduce pancreatic lipase activity. • Release of carotenoids into oil droplets is affected by oil content and HPH pressure. • Raman spectroscopy is a rapid method for determination of carotenoids in oil droplets. Abstract Carotenoids are lipophilic compounds that are digested and absorbed along with lipids. Emulsions based on a mixture of plum tomato and red sweet pepper, with 5% or 10% rapeseed oil, were obtained by high pressure homogenization, and the concentration of carotenoids in the emulsion oil droplets was quantified. The fraction of lycopene and beta-carotene released from the plant matrix into the oil droplets was highest in the 10% emulsion, which had larger oil droplets than the 5% emulsion. Xanthophylls were easily released into oil droplets in both 5% and 10% emulsions. The results suggest that the release of carotenoids made available for intestinal absorption depends on carotenoid type and can be significantly improved by increasing the homogenization pressure and oil content. However, in vitro gastrointestinal digestion indicated the presence of constituents or structures in the emulsions, originating from tomato, that reduced pancreatic activity, which may delay micellarization and uptake of carotenoids. [ABSTRACT FROM AUTHOR]

Non-destructive methods for assessing the chemical composition of fruit and berries are being developed for use in e.g., sorting, storage management or as part of decision making in autonomous harvesting systems. In this study, Raman spectroscopy (RS) was used to estimate a selection of chemical components using partial least squares regression (PLSR) from early (June) and late (September) in the Norwegian strawberry harvesting season. Satisfactory PLSR models were made for total soluble solids (TSS), fructose, glucose, sum of sugars (SS), citric acid and sum of acids (SA) with coefficient of determination (R2) ranging from 0.81 to 0.92 when evaluated by cross validation. PLSR models for total acid content, sucrose and malic acid did not perform as well, with R2 ranging from 0.42 to 0.68, when evaluated by cross validation. Strawberries harvested in September showed significant difference between samples in sweet, sour, and acidic taste (p < 0.001). Results from RS demonstrated that the method can be used to determine sensory properties, where e.g., the correlation between predicted values of TSS and SS/SA with sensory sweet taste were 0.80 and 0.87, respectively. In conclusion, RS performed very well for characterization of both chemical and sensory properties in fresh strawberries. • Raman spectroscopy was used to assess the chemical composition of strawberries. • PLSR models for TSS, fructose, glucose and sum of sugars had R2 > 0.87. • PLSR models for citric acid and sum of acids had R2 > 0.80. • High correlation between Raman results and sweet and sour taste of strawberries. [ABSTRACT FROM AUTHOR]

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful surface-sensitive technique to study the vibrational properties of analytes at very low levels of concentration. In particular, detection of bioactive molecules, specifically antioxidants, is an area of interest to gain insights into the reproducible and quantitative SERS-determination. In this study, SERS measurements were systematically evaluated for ferulic acid, p -coumaric acid, caffeic acid and sinapic acid. The study objective in this research was to: 1) prepare and characterize SERS-active silver colloids; 2) cluster the as-obtained colloids through Principal Component Analysis on the basis of concentration and nanoparticle size; and 3) develop a highly sensitive SERS-based method for phenolic antioxidant detection. The reliability of the proposed method was demonstrated through detection of the phenolic antioxidants evaluated at low levels of concentration. In particular, sinapic acid was evaluated for the first time, with a limit of detection of 2.5 × 10 −9 M. [ABSTRACT FROM AUTHOR]

[Display omitted] • The role of thermal inactivation in collagen solubilization during EPH was studied. • Two protases with very different mode of action towards collagen were included. • 180 hydrolysates from different poultry by-products were produced. • Heat assisted solubilization of partly hydrolyzed collagen was assessed. • Sediment removal prior to thermal inactivation is a strategy to tailor hydrolysates. 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. [ABSTRACT FROM AUTHOR]

In the present study, the possibility of employing spatially offset Raman spectroscopy (SORS) in the qualitative and quantitative characterization of quality parameters of salmon through the skin has been explored. A laboratory-based SORS setup comprising an 830 nm laser was employed, and intact samples and model samples made of salmon tissue constituents were used to investigate the penetration of Raman signals through the dark and light part of salmon skin. Intact salmon samples with both dark and light skin were measured at different spatial offsets. When using spatial offsets in the range of 5-6 mm, the results clearly show that information regarding fatty acid composition and carotenoid content could be obtained from both dark and light parts of the skin. Similar information could not be obtained using conventional backscattering Raman spectroscopy. Model samples of ground salmon spiked with either solutions of carotenoids or a range of vegetable oils were also measured, and at a spatial offset of 5 mm, a clear relationship between Raman carotenoid band intensities and carotenoid concentrations in the model samples was revealed. In addition, high correlations for the estimation of iodine values (i.e., fatty acid unsaturation) could be obtained for SORS measurements through light and dark parts of the salmon skin. A crude estimate suggested that information from around 5 mm beneath the surface area of the salmon skin could be obtained. The choice of a laser line in the near-infrared region is a major prerequisite for successful through-skin analysis of salmon. This feasibility study could pave the way for future Raman analysis of intact salmon. [ABSTRACT FROM AUTHOR]

Highlights: [•] Resolution and resolving power are often defined in opposing ways. [•] Resolution is the theoretical ability of the detector. [•] Distinguishability is the ability of the detector achieved in practice. [•] The distinguishability of a detector cannot be better than its resolution. [Copyright &y& Elsevier]

Abstract: In this investigation, the effect of NaCl, KCl and MgSO4 on bovine meat was studied, where the salts were used in standard marinades in 5.5% concentration. The effect of salts on secondary structure of the myofibrillar proteins, protein–water interactions, WHC, and sensory properties of the meat was followed by carrying out FTIR and NIR measurements, cooking loss and sensory analysis. The information obtained by spectroscopic analysis was integrated by using CPCA. This revealed that MgSO4 increased ratio of α-helices and C O and NH groups (followed by FTIR) that are involved in H-bonding with surrounding water molecules (followed by NIR). This was also supported by increased WHC. Conversely, KCl reduced WHC of meat and was correlated to non-hydrogenated C O and NH groups. Furthermore, the sensory analysis confirmed that MgSO4 was acceptable only when the share of this salt in the mixture was one third. [Copyright &y& Elsevier]

Abstract: In this paper a combination of NIR spectroscopy and FTIR and Raman microspectroscopy was used to elucidate the effects of different salts (NaCl, KCl and MgSO4) on structural proteins and their hydration in muscle tissue. Multivariate multi-block technique Consensus Principal Component Analysis enabled integration of different vibrational spectroscopic techniques: macroscopic information obtained by NIR spectroscopy is directly related to microscopic information obtained by FTIR and Raman microspectroscopy. Changes in protein secondary structure observed at different concentrations of salts were linked to changes in protein hydration affinity. The evidence for this was given by connecting the underlying FTIR bands of the amide I region (1700–1600cm−1) and the water region (3500–3000cm−1) with water vibrations obtained by NIR spectroscopy. In addition, Raman microspectroscopy demonstrated that different cations affected structures of aromatic amino acid residues differently, which indicates that cation–π interactions play an important role in determination of the final structure of protein molecules. [Copyright &y& Elsevier]

Abstract: Extended multiplicative signal correction (EMSC) is a model-based preprocessing technique that has been frequently used in vibrational spectroscopy. The framework of EMSC allows, apart from basic preprocessing as baseline correction and normalization, to separate and quantify different types of chemical and physical variations in vibrational spectra. EMSC thus enables the user to study these different effects separately. The aim of this tutorial is to give a comprehensive description of EMSC and its use in vibrational spectroscopy. Examples from FT-IR and Raman spectroscopy of biological materials will be used, and different extensions of EMSC and their properties will be discussed. [Copyright &y& Elsevier]

• New SEC -based method was applied for estimation of protein digestibility. • SEC enables quantitative comparisons of protein digestibility in different foods. • The method gives insight into protein solubilization and breakdown during digestion. • The proposed SEC based methodology is reproducible and easy to perform. 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 (D SEC). Values for D SEC 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 NH 2 -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. [ABSTRACT FROM AUTHOR]

• Non-destructive determination of SSC and sugars in apples by Raman spectroscopy. • Apple analysis at three steps: intact apples, apples without skins and juices. • Measurements thought skin were possible at least 1.0 cm in deep. • Regression coefficients highlighted identifiable Raman bands related to the target sugars. This study reports the feasibility of using Raman spectroscopy for quantification of soluble solids and individual sugars in apples. Six different commercial apple varieties were measured by Raman spectroscopy at three different steps: 1) Intact apples with skin, 2) apples without skin and 3) juices obtained from apples. Results indicated that it is possible to measure Raman signals to a depth of 8 mm into the apple with a wide area Raman probe. Multivariate calibration models were established to evaluate how well Raman spectra can be used to estimate the quality parameters SSC (%), total sugars, glucose, fructose and sucrose. Estimation accuracy for SSC was comparable with what is achievable with near-infrared spectroscopy: Root mean square error of cross-validation (RMSECV) = 0.66, 0.46 and 0.72 % and coefficients of determination (R2) = 0.70, 0.85 and 0.63 for intact apples, apples without skin and juices, respectively. Sucrose and glucose were well estimated with RMSECV of 2.8, 1.9, 2.1 mg/mL for glucose and 5.8, 3.9 and 3.7 mg/mL for sucrose, for the three sample cases, respectively. Coefficient of determination was higher than 0.82 for all models. Regression coefficients for all calibration models highlighted identifiable Raman bands that could be related to the target sugars. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we extend the concept of cross model validation (CMV) to multiple X and Y variables where different spectroscopic techniques serve as X and Y data in a regression context. For the first dataset on marzipan samples the main objective was to find significant regions in the spectral data, and to discuss the issue of false discovery, i.e. combinations of variables that erroneously are found to be significant. A permutation test within the framework of CMV showed that no regression coefficients in the partial least squares regression (PLSR) model between FT-IR and VIS/NIR spectra show significance at the 5% level. We believe the reason is that the CMV acts as strong filter towards spurious correlations. Corresponding CH- and OH-bands between FT-IR and NIR spectra gave significant regions. For the second dataset, the results from CMV are interpreted more in detail with chemical background knowledge in mind. Most of the significant regions found between the Raman and NIR spectra could be interpreted from the chemical composition of the oil mixtures. Some regions were more difficult to interpret, which could be due to systematic baseline effects in the NIR data. [Copyright &y& Elsevier]

Abstract: Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8–170.0gI2/100gfat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5gI2/100gfat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples. [Copyright &y& Elsevier]

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 r cv 2=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 r cv 2=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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• DH% and M w were monitored during enzymatic protein hydrolysis of dairy proteins. • Intercorrelation of DH% vs M w were studied for 60 milk protein hydrolysates. • A PLSR model was developed for prediction of DH% based on molecular weight distribution profiles. • A multivariate model based on dry-film FTIR was developed for dual prediction of DH% and M w. Fourier-transform infrared (FTIR) spectroscopy was applied to predict the degree of hydrolysis (DH%) and weight-average molecular weight (M w) in milk protein hydrolysates. Both DH% and M w are important quality parameters of protein hydrolysates. Measuring these parameters and following their development during proteolytic reactions is therefore essential for process control and optimization in industry. In the present study the intercorrelation and the complimentary nature of these parameters were investigated and a partial least squares regression (PLSR) model was developed for the prediction of DH% from molecular weight distributions. Finally, we developed PLSR models based on dry-film FTIR spectroscopy for the prediction of both DH% and M w. Here spectral changes in the amide region were found to be important for the two calibration models, underlining the advantage of dry-film FTIR measurement. This shows that dry-film infrared spectroscopy is a promising tool for dual prediction of DH% and M w. [ABSTRACT FROM AUTHOR]

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. Graphical Abstract [ABSTRACT FROM AUTHOR]

In this work, we evaluated the feasibility of Raman spectroscopy as an in-line raw material characterization tool for industrial process control of the hydrolysis of poultry rest raw material. We established calibrations (N = 59) for fat, protein, ash (proxy for bone) and hydroxyproline (proxy for collagen) in ground poultry rest raw material. Calibrations were established in the laboratory using poultry samples with high compositional variation. Samples were measured using a wide area illumination Raman probe at varying working distance (6 cm, 9 cm, 12 cm) and probe tilt angle (0°, 30°) to mimic expected in-line variations in the measurement situation. These moderate variations did not significantly affect performance for any analytes. The obtained calibrations were tested in-line with continuous measurements of the ground poultry by-product stream at a commercial hydrolysis facility over the course of two days. Measurements were acquired under demanding conditions, e.g. large variations in working distance. Reasonable estimates of compositional trends were obtained. Validation samples (N = 19) were also reasonably well predicted, with RMSEP corr = [0.14, 1.37, 2.36, 1.51]% for hydroxyproline, protein, fat and ash, respectively. However, there were indications that further calibration development and robustification of pre-processing would be advantageous, particularly with respect to hydroxyproline and protein models. It is the authors' impression that with such efforts, potentially in combination with development of practical measurement setup, the use of Raman spectroscopy as a process control tool for the hydrolysis of poultry rest raw materials is within reach. [Display omitted] • First test of in-line characterization of a poultry rest raw material stream in a real hydrolysis facility, using a Wide Area Illumination Raman probe. • Calibrations made in laboratory using poultry samples with high variation in chemistry. • On-site validation in a real poultry processing facility following variations in the raw material stream over 2 days. • Indication that moderate variations in working distances was not problematic for predictive performance. • Reasonable prediction trends for compositional variations were obtained by in-line Raman measurements, in spite of challenging measurement conditions. [ABSTRACT FROM AUTHOR]

Highlights • A rapid method for color quality measurement in paprika was developed. • Raman spectroscopy with a 785 nm laser excitation was applied in paprika powder. • Non-adulterated samples were discriminated from Sudan I adulterated samples by PLS-DA. Abstract The aim of this study was developing a non-destructive method for the determination of color in paprika powder as well as for detecting possible adulteration with Sudan I. Non-destructive Raman spectroscopy was applied directly to paprika powder employing a laser excitation of 785 nm for the first time. The fluorescence background was estimated, by fitting a polynomial to each spectrum, and then subtracted. After preprocessing the spectra, some peaks were clearly identified as characteristic from pigments present in paprika. The preprocessed Raman spectra were correlated with the ASTA color values of paprika by partial least squares regression (PLSR). Twenty-five paprika samples were adulterated with Sudan I at different levels and the PLSR model was also obtained. The coefficients of determination (R2) were 0.945 and 0.982 for ASTA and Sudan I concentration, respectively, and the root mean square errors of prediction (RMSEP) were 8.8 ASTA values and 0.91 mg/g, respectively. Finally, different approaches were applied to discriminate between adulterated and non-adulterated samples. Best results were obtained for partial least squares – discriminant analysis (PLS-DA), allowing a good discrimination when the adulteration with Sudan I was higher than 0.5%. [ABSTRACT FROM AUTHOR]

Enzymatic protein hydrolysis (EPH) is one of the industrial bioprocesses used to recover valuable constituents from food processing by-products. Extensive heterogeneity of by-products from, for example, meat processing is a major challenge in production of protein hydrolysates with stable and desirable quality attributes. Therefore, there is a need for process control tools for production of hydrolysates with defined qualities from such heterogeneous raw materials. In the present study, we are reporting a new feed-forward process control strategy for enzymatic protein hydrolysis of poultry by-products. Four different spectroscopic techniques, i.e., NIR imaging scanner, a miniature NIR (microNIR) instrument, fluorescence and Raman, were evaluated as tools for characterization of the raw material composition. Partial least squares (PLS) models for ash, protein, and fat content were developed based on Raman, fluorescence, and microNIR measurements, respectively. In an effort to establish feed-forward process control tools, we developed statistical models that enabled prediction of end-product characteristics, i.e., protein yield and average molecular weight of peptides (Mw), as a function of raw material quality and hydrolysis time. A multiblock sequential orthogonalised-PLS (SO-PLS) model, where spectra from one or more techniques and hydrolysis time were used as predictor variables, was fitted for the feed-forward prediction of product qualities. The best model was obtained for protein yield based on combined use of microNIR and fluorescence (R2 = 0.88 and RMSE = 4.8). A Raman-based model gave a relatively moderate prediction model for Mw (R2 = 0.56 and RMSE = 150). Such statistical models based on spectroscopic measurements of the raw material can be vital process control tools for EPH. To our knowledge, the present work is the first example of a spectroscopic feed-forward process control for an industrially relevant bioprocess. [ABSTRACT FROM AUTHOR]

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 R2 CV = 0.96 for Raman at 2s exposure and R2 CV = 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. [Display omitted] • Feasibility of in-line estimation of fatty acid features (%EPA + DHA) in salmon fillets. • Raman spectroscopy was compared with near infrared hyperspectral imaging (NIR). • Promising results for both methods. • Indications that shorter exposure times than 2s might be challenging for Raman. • Raman gave low and stable biases (test set). NIR gave high and varied biases. [ABSTRACT FROM AUTHOR]

Model-based pre-processing has become wide spread in spectroscopy and is the standard procedure in Fourier-transform infrared spectroscopy. It has also been shown to give valuable contributions in Raman spectroscopy. Extended multiplicative signal correction is flexible enough to handle varying fluorescence background and take into account individual variations in baselines while still keeping enough rigidity through reference spectra and model fitting to avoid degenerate solutions and overfitting, when used correctly. We demonstrate the basic extended multiplicative signal correction method and some extensions, including a novel shift correction, on real Raman data to demonstrate effects on visual appearance, replicate variation and prediction. Comparisons with other standard correction methods are also shown and discussed. © 2016 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper we present a method for transferring calibrations between different spectrometers based on assigning wavelength correspondence. It has been tested for near-infrared (NIR) and Raman spectroscopic instruments, and three examples are included in the paper. The calibration transfer is done in three steps: first wavelength correspondence is established. Second, PLS models are built and tuned for the new spectrometer. Third, the PLS models are slope and bias corrected. The advantages with this approach are that it does not require transfer samples and that there is only one parameter to tune: the number of PLS components. While a few samples with reference values are required for the tuning, it is fewer than methods with multiple parameters that need to be tuned. • Calibration transfer between different spectrometers using wavelength correspondence. • Calibration transfer without transfer samples. • Calibration transfer applied on NIR and Raman spectrometers. [ABSTRACT FROM AUTHOR]

Industries are continuously looking for dedicated sensor solutions for evaluating the chemical composition of products that can provide valuable insights into process control, process optimization, and product quality. A rapid and robust analytical method, Fourier-Transform Infrared (FTIR) spectroscopy, holds significant potential in this regard as it provides detailed compositional values of food nutrients. The main aim of the present study was to use dry film FTIR spectroscopy as an analytical tool to characterize products from an industrial enzymatic protein hydrolysis process and link this to understand industrial process variations. For this purpose, 463 protein hydrolysate samples were obtained from an industrial enzymatic protein hydrolysis plant. In the same period, systematic variations in process parameters such as raw material composition, enzyme type, and water addition, were performed. All samples were analyzed using dry film FTIR spectroscopy. Two subsets containing 200 and 68 hydrolysate samples were chosen for measuring average molecular weight (AMW) and collagen content respectively, providing reference data for constructing calibration models based on partial least squares regression (PLSR). The percentage of low molecular weight constituents was derived from the molecular weight distribution of size exclusion chromatograms of protein hydrolysates and also used in the modeling. Calibration models for the prediction of AMW, low molecular weight constituents, and collagen content were obtained with a good fit and lower estimation errors. Principal component analysis (PCA) of protein hydrolysates' FTIR spectra effectively differentiated process variations (enzyme types and raw materials) without extensive reference analysis. One-factor analysis of variance (ANOVA) tests was used to observe the impact of process variation on product quality. FTIR proved to be a sensitive method for liquid protein analysis and process control with a significant potential in process optimization approaches. To the authors' knowledge, this is the first time dry film FTIR spectroscopy has been used to evaluate an industrial bioprocess with designed process variations. [Display omitted] • Dry film FTIR was used to evaluate an industrial bioprocess with process variations. • Dry film FTIR can be used to characterize industrial protein hydrolysate quality. • FTIR calibration models were built with low estimation errors for three parameters. • FTIR spectroscopy was successfully used to comprehend industrial process variations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Methods for diagnosing the cage of covariance. • Testing if covariance structure reduces robustness in spectroscopic models. • Spectroscopic model sensitivity to interfering analytes. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Raw material and protease have major effects on product composition and quality, but impact vary with product quality studied. • Protein yield, proximate composition and bioactivity are more affected by protease than by raw material variations. • Raw material variations affect peptide size distribution and amino acid composition in protein fractions, exemplified by EAA values. The aim of this study was to investigate the role of compositional variations in side-streams from the chicken and turkey industry on product characteristics after enzymatic protein hydrolysis (EPH). Variation was obtained using four raw materials, namely chicken and turkey carcasses, and mechanically deboned chicken and turkey residues. Alcalase 2.4 L, Corolase 2TS and Flavourzyme, three commercial proteases with different specificities, were used to investigate impact of protease choice on product characteristics. Besides protein yield, effects on product characteristics such as nutrient and amino acid composition, size distribution of peptides, degree of hydrolysis (DH%), rheological properties, and angiotensin-I-converting enzyme (ACE) inhibition, were investigated. Results show that choice of protease and industrially relevant variations in poultry raw material composition both have major effect on product composition and the other quality parameters studied. Protein yield, proximate composition, and bioactivity were more affected by protease than raw material variation. Raw material variation had a bigger impact on quality parameters such as peptide size distribution and amino acid composition, exemplified by the sum of essential amino acids. This means that raw material variation should be handled and accounted for to realize the true potential that exists in tailor-making EPH products with specified properties for targeted applications. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews the basic ideas underlying two new multi-block techniques, the SO-PLS and the PO-PLS regression methods. A discussion is given about how the two methods are related to each other and to standard regression and analysis of variance (ANOVA). In particular the relation between SO-PLS and Type I ANOVA is underlined. It is shown how the sums of squares can be split according to blocks introduced and also how two-way ANOVA applied to cross-validated residuals, can be utilised for testing significance of the blocks. Different ways of interpreting the results for both methods are considered and illustrated by examples. Relations to other proposed methods and ideas are discussed. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. • Spectroscopy and chemometrics are crucial tools for understanding bioprocesses. • Raw material properties and process settings both affect the end-product quality. • NIR and Raman spectroscopy may be used for in-line characterisation of raw materials. • FTIR, SEC and NMR provide detailed characterisation of protein hydrolysates. • Transition of technology and insights from lab to industry is a remaining barrier. [ABSTRACT FROM AUTHOR]

[Display omitted] • FTIR is a potential tool for quantifying collagen in hydrolyzed proteins. • Results were validated both with laboratory and industrial test sets. • With broad variation in collagen, the FTIR response is non-linear. • The non-linear behavior was improved using hierarchical cluster-based regression. • This is one of few examples where FTIR is used to quantify protein composition. 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. [ABSTRACT FROM AUTHOR]

The interaction between near infrared (NIR) light and the sample is a key factor to take into consideration when developing applications using NIR spectroscopy. The aim of this study was to assess the penetration depth of the NIR light through both intact watermelons and the red flesh of the cut fruit, using an interactance NIR system. The results showed a maximum penetration of between 9 and 11 mm in the assays carried out on intact watermelons and 11 mm in the watermelon flesh. The prediction performance of the different NIR system configurations used were compared by quantifying the SSC in intact watermelons in a non-destructive way. The results obtained for the assay in which the illuminated regions were separated by 28 mm (Distance 4) reported the best results for determination of SSC (%) obtaining an R2 cv = 0.73 and RMSECV = 0.39 % and an R2 p = 0.81 and RMSEP = 0.30 % when the set of data for that distance was split into calibration and internal validation sets. • The penetration depth of NIR light into different watermelons tissues was studied. • The penetration depth of NIR light was assessed using MCR-ALS. • The optimum optical measurement geometry of the NIR system was identified. • Penetration depth through the rind of the watermelon was about 9–11 mm. • NIR interactance spectroscopy was used to measure the SSC in intact watermelons. [ABSTRACT FROM AUTHOR]

Abstract According to European food safety authorities, one of the major control parameters for mechanically separated meat is calcium content, which is an indicator of residual bone. Residual bone in mechanically separated meat can also be measured as total ash content. Despite the need to measure both ash and/or calcium content of mechanically separated meat, there is no rapid analytical technique that can be used in an industrial environment. In the current study, we are presenting the first application of Raman spectroscopy as a rapid tool for estimating calcium and ash contents in bone and meat mixtures from mechanical deboning of chicken meat. Raman-based partial least squares regression models were developed for prediction of both ash and calcium content in 79 samples gathered from four different production days. Two different data pre-processing methods, i.e., polynomial background fitting and extended multiplicative scatter correction with polynomial extension, were applied to the raw Raman data and the prediction models were compared. The prediction model based on EMSC treated data afforded the lowest root mean square error of cross-validation (RMSECV = 0.333 g/100 g for calcium and RMSECV = 0.634 g/100 g for ash) and the highest coefficient of determination (R2 = 0.775 for calcium and R2 = 0.894 for ash). Highlights • Raman study of residual bone minerals in mechanically deboned chicken meat. • Calcium and ash content of samples from mechanical chicken deboning were studied. • Raman-based PLSR models were developed for prediction of calcium and ash content. • Prediction models were compared for two pre-processing strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Whereas industrial laboratory systems for Fourier-Transform Infrared (FTIR) spectroscopic characterization have been available for decades, dedicated FTIR solutions for in-line process control or for industrial at-line analysis of protein quality are still scarce. Thus, the present study aimed to compare two industrially viable sampling techniques, namely attenuated total reflectance (ATR) and dry film FTIR spectroscopy, for qualitative and quantitative analysis of liquid protein solutions. For this purpose, two sample sets were acquired: set 1 consisted of 95 protein hydrolysate samples produced in the laboratory from salmon processing by-products, and set 2 consisted of 133 protein hydrolysate samples obtained from an industrial processing plant of poultry by-products. Average molecular weights (AMW) and Brix measurements of protein hydrolysates were used as reference values for obtaining regression models. AMW was predicted with higher accuracy and lower estimation errors using dry film FTIR compared to ATR measurements for both sample types. The difference in predictive performance was higher in poultry hydrolysates because the protein and tissue complexities are higher than in salmon hydrolysates, and information from the amide I region in the FTIR spectra is needed to provide good calibration results. ATR, on the other hand, was more reliable for the prediction of Brix values of protein hydrolysates. The study also showed that FTIR spectroscopy can be used to predict protein quality features of industrially produced protein hydrolysates with a sensitivity of high industrial relevance. Therefore, developing industrially viable portable instruments for at-line process analysis in the food, feed, and biotech industries is a natural next development stage. [Display omitted] • This is the first study to compare ATR and dry film FTIR spectroscopy for quantitative analysis of liquid protein solutions. • Dryfilm FTIR showed higher accuracy for prediction of average molecular weight (AMW) in liquid protein hydrolysates. • FTIR can be used to predict the AMW of industrial protein hydrolysates with a sensitivity of high industrial relevance. • ATR proved to be a more reliable method for the prediction of Brix values. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Enzymatic protein hydrolysis can be used industrially for refining peptides from poultry by-products. The aim is to introduce these peptides for human consumption, but today the majority is used for feed or pet food. One main barrier for entering the human market is unknown and unstable peptide product qualities. Reliable analytical methods for characterising the peptide fraction is therefore vital for optimizing and controlling the product quality. In this work, we investigate the potential of using a large database of dry-film Fourier-transform Infrared (FTIR) fingerprints to gain new insight in quality variations of peptide products. The database cover >1300 samples from laboratory experiments performed over several years. We found that the FTIR fingerprints contain patterns that are stable across experiments and time, and that variations in the fingerprint can be related to raw material composition and processing factors. We also show that the database can be used for evaluating industrial samples. By comparing the FTIR fingerprints of industrial samples with those in the database, we could see systematic changes in product quality over time and make hypotheses about the underlying causes for change. These results lay the foundation for using dry-film FTIR spectroscopy as an industrial tool for product and process optimisation and control. • FTIR fingerprints of protein hydrolysates contain reproducible patterns. • The fingerprints can be related to raw material composition and process parameters. • A database of FTIR fingerprints can be used to benchmark industry samples. • Dry-film FTIR is a promising technology for optimizing industrial hydrolysis processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In analytical chemistry, multivariate calibration is applied when substituting a time-consuming reference measurement (based on e.g. chromatography) with a high-throughput measurement (based on e.g. vibrational spectroscopy). An average error term, of the response variable, is often used to evaluate the performance of a calibration model. However, indirect relationships, between the response and explanatory variables, may be used for calibration. In such cases, model validity cannot necessarily be determined solely by the average error term. One should also consider the use of the models, as well as the validity of the indirect relationships in future samples. If the analyte of interest is partly quantified from signals of interfering compounds, then these interfering compounds will play a hidden role in the calibration. This hidden role may affect future use of the calibration model as strong covariance relationships between analyte estimates and interfering compounds may be imposed. Hence, such model cannot detect changes in the relationship between the analyte and interfering compounds. The problem is called the cage of covariance. This paper discusses the concept cage of covariance and possible consequences of applying models exposed to this issue. • The concept of Cage of Covariance is explained and visualized. • Estimates of several response variables may depend on each other. • This is problematic if estimates are used in e.g. breeding programs. [ABSTRACT FROM AUTHOR]

Raman, near-infrared and fluorescence spectroscopy were evaluated for determination of collagen content in ground meat. Two sample sets were used (i.e. ground beef and ground poultry by-products), and collagen concentrations (measured as hydroxyproline) varied in the ranges 0.1–3.3% in the beef samples and 0.4–1.5% in the poultry samples. Similar validation results for hydroxyproline were obtained for NIRS (R2 = 0.82 and RMSECV = 0.11%) and Raman (R2 = 0.81 and RMSECV = 0.11%) for the poultry samples. For the beef samples, NIRS obtained slightly less accurate results (R2 = 0.89, RMSECV = 0.25%) compared to Raman (R2 = 0.94, RMSECV = 0.19%), most likely due to less representative sampling. Fluorescence spectroscopy gave higher prediction errors (RMSECV = 0.50% and 0.13% for beef and poultry, respectively). This shows that Raman spectroscopy employing a scanning approach for representative sampling is a potential tool for on-line determination of collagen in meat. • Non-destructive determination of collagen in meat products by Raman spectroscopy. • Regression models were compared for different spectroscopic techniques. • Specific models for collagen and proteins are possible with Raman spectroscopy. [ABSTRACT FROM AUTHOR]

In the presented study, Fourier-transform infrared (FTIR) spectroscopy is used to predict the average molecular weight of protein hydrolysates produced from protein-rich by-products from food industry using commercial enzymes. Enzymatic protein hydrolysis is a well-established method for production of protein-rich formulations, recognized for its potential to valorize food-processing by-products. The monitoring of such processes is still a significant challenge as the existing classical analytical methods are not easily applicable to industrial setups. In this study, we are reporting a generic FTIR-based approach for monitoring the average molecular weights of proteins during enzymatic hydrolysis of by-products from the food industry. A total of 885 hydrolysate samples from enzymatic protein hydrolysis reactions of poultry and fish by-products using different enzymes were studied. FTIR spectra acquired from dry-films of the hydrolysates were used to build partial least squares regression (PLSR) models. The most accurate predictions were obtained using a hierarchical PLSR approach involving supervised classification of the FTIR spectra according to raw material quality and enzyme used in the hydrolysis process, and subsequent local regression models tuned to specific enzyme-raw material combinations. The results clearly underline the potential of using FTIR for monitoring protein sizes during enzymatic protein hydrolysis in industrial settings, while also paving the way for measurements of protein sizes in other applications. Image 1 • FTIR was used to predict the average molecular weight of protein hydrolysates. • Improved prediction of M w was achieved using hierarchical modeling. • The results underline the potential of using FTIR for monitoring protein sizes. [ABSTRACT FROM AUTHOR]

Background: Mucoromycota fungi are important producers of low- and high-value lipids. Mortierella alpina is used for arachidonic acid production at industrial scale. In addition, oleaginous Mucoromycota fungi are promising candidates for biodiesel production. A critical step in the development of such biotechnological applications is the selection of suitable strains for lipid production. The aim of the present study was to use the Duetz-microtiter plate system combined with Fourier transform infrared (FTIR) spectroscopy for high-throughput screening of the potential of 100 Mucoromycota strains to produce low- and high-value lipids. Results: With this reproducible, high-throughput method, we found several promising strains for high-value omega-6 polyunsaturated fatty acid (PUFA) and biodiesel production purposes. Gamma-linolenic acid content was the highest in Mucor fragilis UBOCC-A-109196 (24.5% of total fatty acids), and Cunninghamella echinulata VKM F-470 (24.0%). For the first time, we observed concomitant gamma-linolenic acid and alpha-linolenic acid (up to 13.0%) production in psychrophilic Mucor flavus strains. Arachidonic acid was present the highest amount in M. alpina ATCC 32222 (41.1% of total fatty acids). Low cultivation temperature (15 °C) activated the temperature sensitive ∆17 desaturase enzyme in Mortierella spp., resulting in eicosapentaenoic acid production with up to 11.0% of total fatty acids in M. humilis VKM F-1494. Cunninghamella blakesleeana CCM-705, Umbelopsis vinacea CCM F-539 and UBOCC-A-101347 showed very good growth (23–26 g/L) and lipid production (7.0–8.3 g/L) with high palmitic and oleic acid, and low PUFA content, which makes them attractive candidates for biodiesel production. Absidia glauca CCM 451 had the highest total lipid content (47.2% of biomass) of all tested strains. We also demonstrated the potential of FTIR spectroscopy for high-throughput screening of total lipid content of oleaginous fungi. Conclusions: The use of Duetz-microtiter plate system combined with FTIR spectroscopy and multivariate analysis, is a feasible approach for high-throughput screening of lipid production in Mucoromycota fungi. Several promising strains have been identified by this method for the production of high-value PUFA and biodiesel. [ABSTRACT FROM AUTHOR]