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-addedingredients, fine grained control of the rawmaterials, enzymes and process conditionsensures the best possible yield and eco-nomic return. However, when raw mate-rial batches lack good characterization andcontain high batch variation, online or at-line monitoring of the enzymatic reac-tions would be beneficial. We investigate the potential of deep neural networks inpredicting the future state of enzymatic hydrolysis as described by Fourier-trans-form infrared spectra of the hydrolysates. Combined with predictions of averagemolecular weight, this provides a flexible and transparent tool for process moni-toring and control, enabling proactive adaption of process parameters.publishedVersio

In-line Raman spectroscopy for characterization of an industrial poultry raw material stream

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Calanus finmarchicus as a novel source of health-promoting bioactive peptides: Enzymatic protein hydrolysis, characterization, and in vitro bioactivity

Calanus finmarchicus is a crustacean currently used as a source of marine lipid. The lipids are extracted by enzymatic protein hydrolysis, while the remaining peptide fraction is regarded as a byproduct. In the present work, ten different commercial proteases and endogenous C. finmarchicus proteases were used to produce a set of 63 protein hydrolysates. Protease concentration and hydrolysis time were varied. Hydrolysates were characterized using size-exclusion chromatography and 1H nuclear magnetic resonance spectroscopy. Addition of commercial proteases had unremarkable effect on the yield and molecular weight distribution. This was attributed to the strong impact of endogenous enzymes dominating the hydrolysis process. However, multivariate classification based on 1H NMR spectra revealed subtle variations in composition of hydrolysates produced using different enzymes. The hydrolysates were further evaluated for DPP-IV inhibition and antioxidant activity. The hydrolysates showed significantly higher bioactivity than the unhydrolyzed control. A representative hydrolysate (CaFi55) was fractionated using semipreparative size-exclusion chromatography. A fraction consisting of short peptides with an average chain length of five amino acids (F2), was identified as a major contributor to the DPP-IV inhibitory activity (IC50 = 0.70 ± 0.07 mg/mL)

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.publishedVersio

Fourier-transform infrared spectroscopy for characterization of liquid protein solutions: A comparison of two sampling techniques

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Low-cost, mass-producible nanostructured surface on flexible substrate with ultra-thin gold or silver film for SERS applications

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Prediction of chemical and sensory properties in strawberries using Raman spectroscopy

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FTIR-based prediction of collagen content in hydrolyzed protein samples

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

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.publishedVersio