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QualiCheese – Haltbarkeitsoptimierung von Schnittkäse in ökologisch-nachhaltigen Verpackungen
(2025)
In der Präsentation wurden die Methoden und Ergebnisse eines Lagerversuchs von Tiroler Bergkäse im Rahmen des Interreg-Projekts QualiCheese vergestellt. In diesem Projekt geht es um die Identifizierung von Problemen bei der Umstellung von Schnittkäseverpackungen auf nachhaltige Materialien. Insbesondere wurde auf die Methode der Permeationsmessung eingegangen und eine Übersicht zu Nachhaltigkeitsaspekten im Verpackungskontext gegeben.
QualiCheese – Haltbarkeitsoptimierung von Schnittkäse in ökologisch nachhaltigen Verpackungen
(2025)
Käseverpackungen aus Mehrschichtverbünden sind aktuell nicht recyclebar, weshalb nachhaltige Verpackungslösungen von der Industrie verlangt werden, um, unter anderem, besseres Recycling zu ermöglichen. Im dem Interreg-Projekt QualiCheese wird untersucht, welche praktischen Probleme bei der Umstellung auf neue Verpackungsmaterialien auftreten. In dieser Arbeit werden erste Ergebnisse aus dem laufenden Projekt vorgestellt.
Konkret wurde Tiroler Bergkäse am Block in einem Cellulose-Stärke-Verbund verpackt und mit einer PA-PE-Referenz verglichen. Die ersten Ergebnisse bei Standardbedingungen deuten nicht darauf hin, dass eine der beiden Verpackungen für den untersuchten Käse ungeeignet ist. Bei Extrembedingungen außerhalb des Anwendungsszenarios mit erhöhter Luftfeuchtigkeit konnte jedoch beobachtet werden, dass die Sauerstoffbarriere der Referenzverpackung abnimmt und es zu Schimmelbildung kommt.
QualiCheese – Haltbarkeitsoptimierung von Schnittkäse in ökologisch-nachhaltigen Verpackungen
(2025)
In der Präsentation wurden die Methoden und Ergebnisse eines Lagerversuchs von Tiroler Bergkäse im Rahmen des Interreg-Projekts QualiCheese vergestellt. In diesem Projekt geht es um die Identifizierung von Problemen bei der Umstellung von Schnittkäseverpackungen auf nachhaltige Materialien. Insbesondere wurde auf die Methode der Permeationsmessung eingegangen und eine Übersicht zu Nachhaltigkeitsaspekten im Verpackungskontext gegeben.
Die Süßwarenindustrie steht vor vielfältigen Herausforderungen wie steigenden Rohstoffpreisen, regulatorischen Vorgaben und wachsender Nachfrage nach innovativen Produkten. Der Artikel beleuchtet das Potenzial Künstlicher Intelligenz (KI), diesen Entwicklungen zu begegnen – etwa durch optimierte Anbauprozesse, nachhaltigere Verpackungslösungen und verbesserte Produktionsüberwachung. Dabei wird deutlich, dass technologische Lösungen allein nicht ausreichen: Der erfolgreiche KI-Einsatz hängt maßgeblich von interdisziplinärer Zusammenarbeit ab. Hemmnisse wie fachspezifische Denkweisen, mangelnde Kommunikation zwischen Disziplinen und strukturelle Barrieren in Ausbildung und Forschung behindern die Umsetzung. Der Artikel plädiert für die Ausbildung sogenannter „Brückenbauer“, die technisches und fachliches Know-how verbinden und so die Integration von KI in der Süßwarenindustrie vorantreiben. Um vom Hype zur Praxis zu gelangen, bedarf es einer realistischen Erwartungshaltung und gezielter Kooperation zwischen Informatik und Lebensmitteltechnologie.
A reoccurring problem during fresh cheese production is product loss due to necessary additional cleanings when whey gets too turbid due to decreased separation efficiency. This impacts the processing in two facets: a loss of product as well as delays in the production – especially the later can be very critical, as in large production facilities several acidification processes run simultaneously and the processing of the milk to curd must start within given time windows. The goal of this paper is the analysis of the process data to identify factors influencing the problem. This was done following the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model, where theoretical and company knowledge is combined to find hypotheses for the turbidity problem and then transferred into a data mining problem.
The challenge was to combine continues time series data from sensors and punctual non-time series data from each production batch (e.g., used starter culture). Therefore, we generated time-independent features using metrics like mean for time series data together with the non-time series data, each representing attributes of the curd process. Eleven different machine learning (ML) classifiers were trained and evaluated using data from 87 production batches in an iterative approach. In the initial analysis the most important
features show the effect but not the cause of cleanings. The results of the two iterations – with revised features and a selection of the most important features – revealed some features explaining the change in separation performance. Following our approach, further analysis will be performed to clarify the relationships identified in more depth. Using the same approach, further iterations and analyses could reveal further influencing factors.
Obesity is one of the major challenges of modern societies. The food industry is under increasing pressure from government and the consumer side to reformulate foods to reduce the fat, sugar and salt content. However, if fat or sugar is reduced the overall flavor perception of the food is changed. In the case of fat reduction these changes are caused by a different aroma release during in mouth processing.
Additionally the texture is perceived as less creamy. It was recently found that aeration can increase aroma release and change texture perception in terms of creaminess.
The goal of this work is to study both effects in perspective of fat reduction in fermented dairy products.
Various mathematical models will be used to understand and describe the different effects involved. Since temperature is a critical parameter for aroma release and foam stability, heat transfer into a simplified mouth model for aroma release analysis was studied using the finite element method. In a second step the aroma release during foam collapse will be modelled and validated by aroma analysis data. In the last step the model linking the sensory perception of creaminess to the physical parameters of the foam (friction coefficient, viscosity, bubble size distribution) and the chemical attributes of the aroma substance (hydrophobicity, volatility) will be shown.
The findings from this work, both from the effect of aeration and the methodological approach may be transferred to other food categories as well.
Fat reduction is a major challenge for food manufacturers, because the desired calorie reduction goes along with a different aroma perception of low-fat products. To compensate for these changes, this work investigated the effect of foaming on aroma release from a fat-free dairy matrix. A mouth model and an analytical method based on HS-SPME-GC-IMS were designed and validated to quantify dynamic aroma release during thermally induced foam collapse. It was demonstrated that foaming significantly changes the release of aroma compounds. Depending on the hydrophobicity of the aroma substance, release was either increased or decreased after the inclusion of a gas phase.
Since aeration can increase aroma perception, it may be used to compensate the perceived aroma loss of low-fat dairy products. To be able to study the effect of aeration on aroma release systematically in the future, a temperature study was conducted on an in vitro model built to study aroma release at temperatures relevant for oral processing. The studied dairy foam contained 4% (w/w) milk protein and 0.75% (w/w) alginate and had a gas volume fraction of 71% after whipping. The temperature development during incubation of the foam at 40 °C was calculated using a numeric simulation. Thermal equilibrium was reached after 50 min. Sampling for dynamic aroma release was found to be ideal at 2, 7 and 15 min, because mean foam temperatures were 25, 30 and 35 °C. Experimental temperature analysis of the foam core was in good agreement with the simulation (R2 = 0.95).
When we are trying to decrease caloric intake by reducing fat content, aroma perception of the food is changed as well. Since previous studies indicated that foaming changes the aroma release, it was our goal to understand the physico-chemical background of this effect. Therefore, ten aroma compounds were added to a foamed acidified dairy matrix (4% milk protein, 1% gelatin, 60% gas volume). We simulated oral temperature conditions in a simplistic way through incubation at 40 °C and analyzed aroma release using headspace-solid phase microextraction-gas chromatography-ion mobility spectrometry. Significantly more highly hydrophobic aroma compounds were released from the foamed matrix than the unfoamed matrix, while compounds of intermediate hydrophobicity were released more from unfoamed matrix. The effect was independent from foam collapse and persisted for hours afterwards. Analytical results were complemented by orthonasal and retronasal sensory perception studies, which confirmed significant differences between aroma release behavior from foamed foods.
The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.