Adrian Tica


Last Name

Tica

First Name

Adrian

ORCID

0009-0009-1249-6959

Organisational unit

09571 - Mathys, Alexander / Mathys, Alexander

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20254
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Publications 1 - 4 of 4
  • Soft sensing with ultrasound attenuation for in-line texture analysis of structured plant-based foods
    Item type: Other Conference Item
    Tica, Adrian; Windhab, Erich J.; Mathys, Alexander (2025)
    Driven by sustainability and animal welfare concerns, the development of alternatives to meat and dairy products has gained significant interest. Extrusion technologies, particularly High Moisture Extrusion Cooking (HMEC), have proven effective in producing fibrous structures that mimic the texture of real meat using plant-based proteins. To improve the scalability and autonomy of HMEC, this study focuses on integrating an ultrasound (US) measurement system with standard machine learning algorithms for in-line sensing and product characteristics control. A non-invasive sensing system based on US attenuation was adapted to monitor the extrudate during production trials and detect changes in transmitted signal amplitude, which could be correlated with variations in recipe and processing parameters. To build correlation models (referred to as soft sensors) between the measured US patterns and structural / textural attributes of extrudates, supervised machine learning algorithms were applied. Specifically, (i) Linear Regression, (ii) Random Forest, and (iii) eXtreme Gradient Boosting (XGBoost) were tested and used to predict the mechanical attributes: (1) hardness, (2) chewiness, and (3) gumminess, as measured in the laboratory using a Texture Analyser. The feasibility of the method was investigated on a lab-scale extruder for HMEC production of plant-based (soy- and pea-protein) meat alternatives. The ultrasound-based measurement system was able to detect changes as small as 1% in water and powder content, as well as temperature variations of ±5 °C in heating or cooling zones. Consequently, distinct bands of mechanical properties were tracked by the US patterns and accurately predicted by the developed models, yielding consistent results with relative errors below 3% on the test datasets. A framework for measuring the attenuation characteristics of US waves during HMEC processing has been established, along with a data pipeline for integrating these measurements as features into a predictive modeling approach. This framework provides a foundation for developing effective sensing systems capable of measuring in-line properties of structured, protein-rich foods. Furthermore, by enabling the collection and integration of larger and more diverse datasets, it supports the development of more reliable and generalized models that capture the functional relationships between process parameters, structural characteristics, and product properties.
  • Model predictive control of high moisture extrusion cooking
    Item type: Journal Article
    Tica, Adrian; Pinnamaraju, Vivek S.; Stirnemann, Eric; et al. (2025)
    Control Engineering Practice
    High Moisture Extrusion Cooking (HMEC) has become a promising technology for producing plant-based meat alternatives. By using HMEC, food manufacturers can create meat-like textures from plant proteins, offering a sustainable solution with reduced carbon footprint to consumers. However, at the current stage of development, the automation level in HMEC is insufficient to ensure operational autonomy, reliability, and product quality expected by industry demands. This paper presents a predictive control framework designed to transform experience-based handled HMEC into a more reliable process operation, improving its production performance and facilitating industrial up-scaling. The proposed control structure is hierarchical, comprising two layers. At the upper layer, a model predictive control (MPC) algorithm determines the optimal set-points for the controllers at the lower layer. The predictive framework is built on the existing HMEC control architecture and can be further extended to achieve fully optimized production. Leveraging linear dynamic models, the approach mainly focuses on the protein melt control aiming to enhance production performance by minimizing the tracking error of process quantities correlated to product quality. The practical feasibility of the designed control solution has been proven on a pilot-scale extruder. Validation results have shown improved operational stability and reproducibility, while effectively tracking set-points for consistent meat-like fibrous structure formation and desired textural characteristics.
  • The effect of nitrogen injection on the structure and textural properties of casein-based extrudates
    Item type: Journal Article
    Lorenzen, Mikkel; Tica, Adrian; van den Berg, Frans W.J.; et al. (2025)
    Food Hydrocolloids
    This study introduces a novel micro-foaming extrusion process for the production of innovative cheeses by injecting nitrogen (N2) (0, 0.05, 0.10, and 0.15% w/w) into a hot plasticized casein melt sheared at three different screw speeds (250, 300 and 350 RPM) during high-moisture extrusion. Structural and mechanical properties of the extrudates were assessed using X-ray microcomputed tomography (μCT), low-field nuclear magnetic resonance (LF-NMR), and tensile and cutting force tests. Macroscopic and μCT images revealed small and larger coalesced pores in extrudates with 0.05% w/w N2 and ≥0.10% w/w N2, respectively. The porosity ranged from 8.5 to 43.3% and increased with N2 injection. LF-NMR revealed that water molecules of less unbound water (T2.2) increased (from 19.9 ± 0.2 to 21.3 ± 0.3 ms) with increasing N2 and screw speed (from 250 to 350 RPM), confirming the more open porous protein structure in micro-foamed extrudates. Tensile tests showed that micro-foamed extrudates exhibited lower tensile strength (1.3 ± 0 to 0.6±0 N) and extensibility (36.9 ± 2.5 to 10.9 ± 1.9 mm) compared to non-foamed extrudates (2.9 ± 0.2 to 2.1 ± 0.2 N, 38.3 ± 1.3 to 20.1 ± 1.4 mm) in both perpendicular and parallel directions. The cutting force test revealed a reduced hardness (8.7 ± 0.7 to 3.4 ± 0.3 N) once N2 was injected into the protein network structure. Anisotropic index ranged between 0.97 ± 0.0 to 2.52 ± 0.28, exhibiting mechanical properties like other protein-based extrudates. This novel extrusion micro-foaming process creates new processing routes for developing and controlling the texture of extruded cheeses and high-protein products.
  • The influence of milk fat content on the extrusion of rennet casein emulsion gels
    Item type: Journal Article
    Lorenzen, Mikkel; Tica, Adrian; Lillevang, Søren K.; et al. (2025)
    Food Hydrocolloids
    The fat content in cheese is important for quality characteristics, like taste, texture, melting properties, and overall appearance. Understanding the role of fat during extrusion of casein emulsion gels will support cheesemakers in developing new products with diverse textures and functionalities. Four rennet casein emulsion gels with varying fat content (1–18% w/w) and a commercial rennet casein ingredient (Cagliata, 26.6% w/w fat) were extruded to investigate the role of milk fat on extrusion process and properties of the extrudate cheese. The extrusion process was assessed via specific mechanical energy (SME), and the extrudates were characterized using dynamic oscillatory rheology, texture profile analysis, confocal laser scanning microscopy (CLSM), and low-field nuclear magnetic resonance (LF-NMR). Local micro-fat separation in higher fat gels (>18% w/w fat) led to wall slip, which consequently lowered the SME from 70.3 ± 4.6 to 61.4 ± 2.9 kJ kg−1. Macroscopic and CLSM images revealed an anisotropic structure in higher fat gels (>18% w/w fat), with elongated fat droplets separating the casein network. LF-NMR revealed that high fat content led to a more open protein network, with higher mobility of tightly bound (T2.1) and less bound (T2.2) water, due to increased number of fat droplets and serum pockets. The strain sweep revealed an increase in storage and viscous modulus for extrudates with the highest milk fat contents and an increase in the gel-sol transition temperature from 64.0 ± 0.8 to 75.5 ± 0.7 °C. Texture profile analysis revealed lower force values with increasing fat content, indicating a softening behavior at the macroscopic level. This study provides new insights regarding the effect of fat concentration on structural and rheological properties of extruded casein gels.
Publications 1 - 4 of 4