Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aspevik, T., Oterhals, A., Ronning, S. B., Altintzoglou, T., Wubshet, S. G., et al. (2017). Valorization of proteins from co- and by-products from the fish and meat industry. Topics in Current Chemistry (Cham), 375(3), 53.
Bernd, H., Ralph, H., Andreas, N., Daryoush, S., Jens, T., & Jarka, G. (2015). Process analytical technologies in food industry – challenges and benefits: a status report and recommendations. Biotechnology Journal, 10(8), 1095–1100.
Bocker, U., Wubshet, S. G., Lindberg, D., & Afseth, N. K. (2017). Fourier-transform infrared spectroscopy for characterization of protein chain reductions in enzymatic reactions. The Analyst, 142(15), 2812–2818.
Burns, D. A., & Ciurczak, E. W. (2008). Handbook of near-infrared analysis. Boca Raton: CRC Press.
Fountoulakis, M., & Lahm, H.-W. (1998). Hydrolysis and amino acid composition analysis of proteins. Journal of Chromatography A, 826(2), 109–134.
Gilmartin, L., & Jervis, L. (2002). Production of cod (Gadus morhua) muscle hydrolysates. Influence of combinations of commercial enzyme preparations on hydrolysate peptide size range. Journal of Agricultural and Food Chemistry, 50(19), 5417–5423.
Hinz, D. C. (2006). Process analytical technologies in the pharmaceutical industry: the FDA’s PAT initiative. Analytical and Bioanalytical Chemistry, 384(5), 1036–1042.
Jørgensen, K., & Næs, T. (2004). A design and analysis strategy for situations with uncontrolled raw material variation. Journal of Chemometrics, 18(2), 45–52.
Kummu, M., de Moel, H., Porkka, M., Siebert, S., Varis, O., & Ward, P. J. (2012). Lost food, wasted resources: Global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Science of The Total Environment, 438, 477–489.
Li-jun, L., Chuan-he, Z., & Zheng, Z. (2008). Analyzing molecular weight distribution of whey protein hydrolysates. Food and Bioproducts Processing., 86(1), 1–6.
Liland, K. H., Kohler, A., & Afseth, N. K. (2016). Model-based pre-processing in Raman spectroscopy of biological samples. Journal of Raman Spectroscopy, 47(6), 643–650.
Mandair, G. S. & Morris, M. D. (2015). Contributions of Raman spectroscopy to the understanding of bone strength. BoneKEy Report, 4.
Martens, H., & Næs, T. (1989). Multivariate calibration. New York: Wiley.
Morris, M. D., & Mandair, G. S. (2011). Raman assessment of bone quality. Clinical Orthopaedics and Related Research, 469(8), 2160–2169.
Muteki, K., Swaminathan, V., Sekulic, S. S., & Reid, G. L. (2012). Feed-forward process control strategy for pharmaceutical tablet manufacture using latent variable modeling and optimization technologies. IFAC Proceedings, 45(15), 51–56.
Næs, T., Tomic, O., Mevik, B. H., & Martens, H. (2011). Path modelling by sequential PLS regression. Journal of Chemometrics, 25(1), 28–40.
Næs, T., Tomic, O., Afseth, N. K., Segtnan, V., & Måge, I. (2013). Multi-block regression based on combinations of orthogonalisation, PLS-regression and canonical correlation analysis. Chemometrics and Intelligent Laboratory Systems, 124, 32–42.
Nasirpour, A., Scher, J., & Desobry, S. (2006). Baby foods: Formulations and interactions (a review). Critical Reviews in Food Science and Nutrition, 46(8), 665–681.
NMKL (1989). Fat, determination according to SBR (Schmid-Bondzynski-Ratslaff) in meat and meat products. NMKL method no 131, Nordic committee on food analysis, Oslo, Norway.
NMKL (2003). Nitrogen, Determination in foods and feeds according to Kjeldahl. NMKL method no 6, 4th Ed. Nordic Committee on Food Analysis, Oslo.
NMKL (2005). Ash, Gravimetric determination in foods. NMKL method no 173, 2nd Ed. Nordic Committee on Food Analysis, Oslo, Norway.
Parfitt, J., Barthel, M., & Macnaughton, S. (2010). Food waste within food supply chains: quantification and potential for change to 2050. Philosophical Transactions of the Royal Society, B: Biological Sciences, 365(1554), 3065–3081.
Pesti, G., & Bakalli, R. (1997). Estimation of the composition of broiler carcasses from their specific gravity. Poultry Science, 76(7), 948–951.
Poulsen, N. A., Eskildsen, C. E., Akkerman, M., Johansen, L. B., Hansen, M. S., Hansen, P. W., Skov, T., & Larsen, L. B. (2016). Predicting hydrolysis of whey protein by mid-infrared spectroscopy. International Dairy Journal, 61, 44–50.
Pu, Y., Wang, W., & Alfano, R. R. (2013). Optical detection of meat spoilage using fluorescence spectroscopy with selective excitation wavelength. Applied Spectroscopy, 67(2), 210–213.
Rutherfurd, S. M. (2010). Methodology for determining degree of hydrolysis of proteins in hydrolysates: a review. Journal of AOAC International, 93(5), 1515–1522.
Sahni, N., Isaksson, T., & Næs, T. (2004). In-line near infrared spectroscopy for use in product and process monitoring in the food industry. Journal of Near Infrared Spectroscopy, 12(2), 77–83.
Šližytė, R., Daukšas, E., Falch, E., Storrø, I., & Rustad, T. (2005). Characteristics of protein fractions generated from hydrolysed cod (Gadus morhua) by-products. Process Biochemistry, 40(6), 2021–2033.
Tajammal, M. M., Yu, W., Young, B. R., & Wilson, D. I. (2015). The current status of process analytical technologies in the dairy industry. Trends in Food Science & Technology, 43(2), 205–218.
Tang, J. E., Moore, D. R., Kujbida, G. W., Tarnopolsky, M. A., & Phillips, S. M. (2009). Ingestion of whey hydrolysate, casein, or soy protein isolate: effects on mixed muscle protein synthesis at rest and following resistance exercise in young men. Journal of Applied Physiology, 107(3), 987–992.
Tavano, O. L. (2013). Protein hydrolysis using proteases: an important tool for food biotechnology. Journal of Molecular Catalysis B: Enzymatic, 90, 1–11.
van den Berg, F., Lyndgaard, C. B., Sørensen, K. M., & Engelsen, S. B. (2013). Process analytical technology in the food industry. Trends in Food Science & Technology, 31(1), 27–35.
Wold, J. P., Bjerke, F., & Mage, I. (2016). Automatic control of fat content in multiple batches of meat trimmings by process analytical technology. Fleischwirtschaft, 96(11), 100–106.
Wold, J. P., Veiseth-Kent, E., Host, V., & Lovland, A. (2017). Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy. PLoS One, 12(3), e0173384.
Wubshet, S. G., Mage, I., Bocker, U., Lindberg, D., Knutsen, S. H., et al. (2017). FTIR as a rapid tool for monitoring molecular weight distribution during enzymatic protein hydrolysis of food processing by-products. Analytical Methods, 9(29), 4247–4254.
Acknowledgments
Bjørg Narum and Marte Ryen Dalsnes are acknowledged for the excellent technical assistance. Nortura and Norilia are acknowledged for providing the raw materials used in this study and for the partnership related to enzymatic hydrolysis.
Funding
This study received financial support from the Norwegian Research Council through the CYCLE project (225349/E40) and the Chickenlysis project (235839), and the Norwegian Agricultural Food Research Foundation through the project FoodSMaCK—Spectroscopy, Modelling and Consumer Knowledge (262308/F40).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wubshet, S.G., Wold, J.P., Afseth, N.K. et al. Feed-Forward Prediction of Product Qualities in Enzymatic Protein Hydrolysis of Poultry By-products: a Spectroscopic Approach. Food Bioprocess Technol 11, 2032–2043 (2018). https://doi.org/10.1007/s11947-018-2161-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11947-018-2161-y