Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer
| dc.contributor.author | Weishaupt, Imke | |
| dc.contributor.author | Zimmer, Manuel | |
| dc.contributor.author | Neubauer, Peter | |
| dc.contributor.author | Schneider, Jan | |
| dc.date.accessioned | 2020-11-11T08:50:02Z | |
| dc.date.available | 2020-11-11T08:50:02Z | |
| dc.date.issued | 2020-06-29 | |
| dc.date.updated | 2020-11-09T11:34:10Z | |
| dc.description.abstract | Near infrared spectroscopy in combination with a transflection probe was investigated as inline measurement in a continuous flash pasteurizer system with a sugar–water model solution. Robustness and reproducibility of fluctuations of recorded spectra as well as trueness of the chemometric analysis were compared under different process parameter settings. Variable parameters were the flow rate (from laminar flow at 30 L/h to turbulent flow at 90 L/h), temperature (20 to 100 °C) and the path length of the transflection probe (2 and 4 mm) while the pressure was kept constant at 2.5 bar. Temperature and path length were identified as the most affecting parameters, in case of homogenous test medium. In case of particle containing systems, the flow rate could have an impact as well. However, the application of a PLS model, which includes a broad temperature range, and the correction of prediction results by applying a polynomial regression function for prediction errors, was able to compensate these effects. Also, a path length of 2 mm leads to a higher accuracy. The applied strategy shows that by the identification of relevant process parameters and settings as well as the establishment of a compensation strategy, near infrared spectroscopy is a powerful process analytical tool for continuous flash pasteurization systems. | en |
| dc.description.sponsorship | BMBF, 13FH024IX6, IngenieurNachwuchs 2016: Cyber-Physisches System (CPS) zur thermischen Entkeimung von Getränken unter Nutzung der NIR-Sensorik als Schlüsseltechnologie | en |
| dc.identifier.eissn | 1750-3841 | |
| dc.identifier.issn | 0022-1147 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/11894 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-10785 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | 10.14279/depositonce-17804 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 660 Chemische Verfahrenstechnik | de |
| dc.subject.other | flash pasteurization | en |
| dc.subject.other | inline near infrared spectroscopy | en |
| dc.subject.other | multivariate data analysis | en |
| dc.subject.other | process condition influences | en |
| dc.subject.other | sugar‐water‐solution model beverage | en |
| dc.title | Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1111/1750-3841.15307 | en |
| dcterms.bibliographicCitation.issue | 7 | en |
| dcterms.bibliographicCitation.journaltitle | Journal of Food Science | en |
| dcterms.bibliographicCitation.originalpublishername | Wiley | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 2031 | en |
| dcterms.bibliographicCitation.pagestart | 2020 | en |
| dcterms.bibliographicCitation.volume | 85 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 3 Prozesswissenschaften::Inst. Biotechnologie::FG Bioverfahrenstechnik | de |
| tub.affiliation.faculty | Fak. 3 Prozesswissenschaften | de |
| tub.affiliation.group | FG Bioverfahrenstechnik | de |
| tub.affiliation.institute | Inst. Biotechnologie | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |