{"title":"Investigation of near infrared and Raman fibre optic process sensors for protein determination in milk protein concentrate","authors":"","doi":"10.1016/j.fbp.2024.09.013","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigated the potential of two fibre optic process sensors based on near infrared (NIR) or Raman spectroscopic technology for protein measurement in milk protein concentrate (MPC). Partial least squares (PLS) models were developed using NIR, Raman, and fusion of NIR and Raman spectra data. Calibration models developed were optimized by selecting different spectral pre-treatment methods and spectral regions. Overall, the three optimal models (NIR, Raman, fused NIR and Raman) yielded R<sup>2</sup>p values >0.9 and RMSEP values in the range of 0.168–0.185 %. The optimised fusion model outperformed all Raman models and had a similar protein prediction accuracy (R<sup>2</sup>p = 0.911 and RMSEP = 0.178 %) compared to the optimised NIR model (R<sup>2</sup>p = 0.917 and RMSEP =0.168 %). Results of the study demonstrated that both NIR and Raman process probes can be used as process analytical technology (PAT) tools for inline protein measurements of MPC post membrane filtration.</div></div>","PeriodicalId":12134,"journal":{"name":"Food and Bioproducts Processing","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food and Bioproducts Processing","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960308524001901","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigated the potential of two fibre optic process sensors based on near infrared (NIR) or Raman spectroscopic technology for protein measurement in milk protein concentrate (MPC). Partial least squares (PLS) models were developed using NIR, Raman, and fusion of NIR and Raman spectra data. Calibration models developed were optimized by selecting different spectral pre-treatment methods and spectral regions. Overall, the three optimal models (NIR, Raman, fused NIR and Raman) yielded R2p values >0.9 and RMSEP values in the range of 0.168–0.185 %. The optimised fusion model outperformed all Raman models and had a similar protein prediction accuracy (R2p = 0.911 and RMSEP = 0.178 %) compared to the optimised NIR model (R2p = 0.917 and RMSEP =0.168 %). Results of the study demonstrated that both NIR and Raman process probes can be used as process analytical technology (PAT) tools for inline protein measurements of MPC post membrane filtration.
期刊介绍:
Official Journal of the European Federation of Chemical Engineering:
Part C
FBP aims to be the principal international journal for publication of high quality, original papers in the branches of engineering and science dedicated to the safe processing of biological products. It is the only journal to exploit the synergy between biotechnology, bioprocessing and food engineering.
Papers showing how research results can be used in engineering design, and accounts of experimental or theoretical research work bringing new perspectives to established principles, highlighting unsolved problems or indicating directions for future research, are particularly welcome. Contributions that deal with new developments in equipment or processes and that can be given quantitative expression are encouraged. The journal is especially interested in papers that extend the boundaries of food and bioproducts processing.
The journal has a strong emphasis on the interface between engineering and food or bioproducts. Papers that are not likely to be published are those:
• Primarily concerned with food formulation
• That use experimental design techniques to obtain response surfaces but gain little insight from them
• That are empirical and ignore established mechanistic models, e.g., empirical drying curves
• That are primarily concerned about sensory evaluation and colour
• Concern the extraction, encapsulation and/or antioxidant activity of a specific biological material without providing insight that could be applied to a similar but different material,
• Containing only chemical analyses of biological materials.