Lebensmittelanalytik

Matissek, Reinhard; Fischer, Markus; Steiner, Gabriele

doi:10.1007/978-3-662-55722-8

Cited by 28 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The fat content was determined by the Röse-Gottlieb method. 34 For homogenization experiments, the milk was treated by a Sonifier 450 homogenizer (Branson, Germany). Samples of 400 g each were treated for times from 0 min to 25 min.…”

Section: Methodsmentioning

confidence: 99%

Broadband Optical Properties of Milk

et al. 2016

View full text Add to dashboard Cite

Dairy products play an important role in our daily nutrition. As a turbid scattering medium with different kinds of particles and droplets, each alteration of these components changes the scattering properties of milk. The goal of this work is the determination of the amount of main scattering components, the fat droplets and the casein micelles, by understanding the light propagation in homogenized milk and in raw milk. To provide the absolute impact of these milk components, the geometrical and optical properties such as the size distribution and the refractive index (RI) of the components have to be examined. We determined the reduced scattering coefficient [Formula: see text] and the absorption coefficient [Formula: see text] from integrating sphere measurements. By use of a collimated transmission setup, the scattering coefficient [Formula: see text] was measured. Size measurements were performed to validate the influence of the fat droplet size on the results of the scattering properties; also, the RI of both components was determined by the said coefficients. These results were used to determine the absolute impact of the milk components on the scattering behavior. By fitting Mie theory calculations on scattering spectra [Formula: see text] and [Formula: see text] from different raw milk samples, it was possible to get reliable values for the concentrations of fat and casein and for the size of the fat droplets. By destroying the casein micelles, it was possible to separate the influence of the different scattering components on scattering behavior.

show abstract

Section: Methodsmentioning

confidence: 99%

Broadband Optical Properties of Milk

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Die Verseifungszahl gibt in mg KOH pro 1 g Ester an, wieviel vorliegende Esterbindungen gespalten werden können. Die Säurezahl gibt in diesem Fall an, wie viel Milligramm KOH pro Gramm Säure zur Neutralisation aufgewendet werden müssen, wenn sie komplett nicht verestert ist – dies weicht ab von der üblichen Praxis etwa für Fette, in der die gebundenen Säureanteile nicht ‚gezählt‘ werden, sondern nur der unveresterte Anteil titriert wird [10]. Da es sich bei den aufgelisteten Lebensmittelzusatzstoffen um Stoffgemische handelt, variiert die Anzahl vorliegender Esterbindungen [1].…”

Section: Erklärungunclassified

“…Da es sich bei den aufgelisteten Lebensmittelzusatzstoffen um Stoffgemische handelt, variiert die Anzahl vorliegender Esterbindungen [1]. Bei Span 60 ® darf beispielsweise die Verseifungszahl maximal zwischen 147 und 157 schwanken [9]; der Mittelwert für die Säurezahl liegt bei 197 [10]. In der folgenden exemplarischen Berechnung nimmt die VZ einen mittleren Wert an:…”

Section: Erklärungunclassified

Drum prüfe, was sich ewig mischet …

Hild

Kallinna

Murer

et al. 2021

Chemie in nserer Zeit

View full text Add to dashboard Cite

Anliegen der in loser Folge erscheinenden Versuche ist es, Impulse für chemische Versuche zu geben, die gefahrlos zu Hause mit haushaltsüblichen Gegenständen durchgeführt werden können. Sie können damit als Quelle für das gemeinsame Ausprobieren mit Kindern zuhause dienen, in Kindergärten oder an Schulen sowie zur Verwendung bei Tagen der offenen Tür. Ein Stückweit sind sie zu verstehen als Antwort auf den oft gehörten Satz: „Ach, Du bist Chemiker/in!? Dann mach doch mal eben ein Experiment.“

show abstract

“…Figure 1 illustrates schematically this cascade. These applications are often also called high-throughput technologies, producing a substantial amount of data [5][6][7]. Every discipline provides different information about the composition of the target.…”

Section: Introductionmentioning

confidence: 99%

“…So, it is obvious that the metabolome is heavily influenced by all kinds of exogenous factors. However, the metabolome enables an even more pronounced fingerprint of a system, as many compounds can be taken into account for an evaluation and a certain status of product at a certain timepoint can be estimated [4][5][6][7]14,16].…”

Section: Introductionmentioning

confidence: 99%

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

View full text Add to dashboard Cite

Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

show abstract

Lebensmittelanalytik

Abstract: Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar.Springer Spektrum

Cited by 28 publications

References 0 publications

Broadband Optical Properties of Milk

Broadband Optical Properties of Milk

Drum prüfe, was sich ewig mischet …

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Contact Info

Product

Resources

About