Institut für Lebensmittelwissenschaft und Biotechnologie

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/6

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High protein - low viscosity? How to tailor rheological properties of fermented concentrated milk products
(2023) Piskors, Nico; Heck, Anisa; Filla, Jessica M.; Atamer, Zeynep; Hinrichs, Jörg
The rheological properties, e.g., viscosity and yield stress, of fermented concentrated milk products (protein content > 8%) are strongly dependent on their volume fraction. Post-treatment with high-power ultrasound can reduce the volume fraction of these products and, hence, lead to reduced crowding effects and thus lower viscosities and yield stress. Besides that, the particle size distribution (span) should stay unaltered. Increasing the energy input during the sonication of fat-free fresh cheese with a protein content of 8.9 ± 0.4% decreased the volume fraction below the limit for concentrated products (ϕ = 0.4), while the particle size also decreased. This led to a narrowed span and, hence, the viscosity should have increased; however, the results showed that viscosity and yield stress were decreasing. Consequently, the influence of the span was neglectable for concentrated fermented milk products with volume fractions below the concentrated area. Furthermore, the sonicated samples showed no syneresis over a storage time of two weeks. The sonicated samples reached similar rheological properties to commercial stirred yogurt, which demonstrated the suitability of high-power ultrasound as a post-treatment to tailor the rheological properties of high-protein fermented milk products.
Improved methods in optimal design of experiments for determination of water absorption kinetics of cereal grains
(2016) Paquet-Durand, Olivier; Hitzmann, Bernd
In this thesis, the optimal design of experiments was applied to determine hydration kinetics of wheat grains. In the first study the used mathematical model was the Peleg model for which the optimal design of experiments was carried out while investigating how the optimization criterion will influence the result. The parameter estimation errors could be reduced by up to 62% compared to a non-optimal equidistant experimental design. It has been shown that the individual parameter estimation errors vary significantly depending on the used criterion. In this application only the D-optimal experimental design can reduce the parameter estimation errors of both parameters. In case of the A, Pr and E criterion at least one of the two parameter error could be reduced significantly. As the numerical optimization is computationally demanding, an alternative method for the entire optimal experimental design was developed. This alternative method is based on a mathematical function which depends on the rough initial parameter values. This function allows optimal measuring points to be calculated directly and therefore much faster, than the usual optimal design approach using numerical optimization techniques. In case of the very commonly used D-optimality criterion, the derived function is the exact solution. The deviation of the parameter estimation errors acquired by using the approximate optimal design instead of a normal optimal design are mostly around 0.01 % and therefore negligible. In the second study, the suitability of the Peleg model for water absorption kinetics of wheat grains was investigated closer. Cereal grains usually consist of three major components, bran layer, endosperm and germ. All these components have different water absorption kinetics. Therefore, the normal two parameter Peleg model might be insufficient to describe the water absorption process of cereal grains properly. To address this, the Peleg model was enhanced and a second Peleg like term was added to account for the two biggest fractions of the grain, namely the endosperm and the bran layer. Two experiments were carried out, an initial experiment to get rough parameter values and a second experiment, which was then optimally designed. The modified Peleg model had now four parameters and could be used to describe the hydration process of wheat grains much more accurate. Using the parameters calculated from the initial experiment the optimal measurement points where calculated in a way that the determination of the parameters of the modified Peleg model was as accurate as possible. The percentage parameter errors for the four parameters in the initial experiment were 669%, 24%, 12%, and 2.4%. By applying the optimal design, they were reduced to 38% 5.4%, 4.5% and 1.9% respectively. The modified Peleg model resulted in a very low root mean square error of prediction of 0.45% where the normal Peleg model results in a prediction error of about 3%. In the third study, it was investigated if bootstrapping could be used as a feasible alternative method for optimal experimental design. The classical procedure to determine parameter estimation errors is based on the Cramér-Rao lower bound but bootstrapping or re-sampling can also be used for the estimation of parameter variances. The newly developed method is more computationally demanding compared to the Cramér-Rao lower bound approach. However, bootstrapping is not bound to any restrictive assumptions about the measurement and parameter variations. An optimal experimental design based on the bootstrap method was calculated to determine optimal measurement times for the parameter estimation of the Peleg model. The Cramér-Rao based optimal design results were used as a benchmark. It was shown, that a bootstrap based optimal design of experiments yields similar optimal measurement points and therefore comparable results to the Cramér-Rao lower bound optimal design. The parameter estimation errors obtained from both optimal experimental design methods deviate on average by 1.5%. It has also been shown, that the probability densities of the parameters are asymmetric and not at all normal distributions. Due to this asymmetry, the estimated parameter errors acquired by bootstrapping are in fact likely to be more accurate. So bootstrapping can in fact be used in an optimal design context. However, this comes at the cost of a high computational effort. The computation time for a bootstrap based optimal design was around 25 minutes compared to only 5 seconds when using the Cramér-Rao lower bound method. But compared to the time required to carry out the experiments this is neglectable. Furthermore, as computers get faster and faster over time, the computational demand will become less relevant in future.
Influence of transport distance, animal weight, and muscle position on the quality factors of meat of young bulls during the summer months
(2024) Poveda-Arteaga, Alejandro; Bobe, Alexander; Krell, Johannes; Heinz, Volker; Terjung, Nino; Tomasevic, Igor; Gibis, Monika
This study investigated the potential effects of transport distance, animal weight, and muscle position on meat quality in young bulls under commercial conditions across four slaughtering weeks during the summer months (May to September). Data on transport distance, lairage time, and ambient temperature during slaughtering days were collected from 80 young bulls from North German farms. Meat quality parameters, including pH, temperature, and meat color were also recorded at several post-mortem times from two different carcass locations (shoulder clod and silverside). Meat texture was evaluated both by sensory and instrumental analysis, and their values were compared to find possible correlations between them. All of the aforementioned main factors (transport distance, animal weight, and muscle position), as well as the interaction between animal weight and transport distance, significantly influenced (p < 0.01) meat quality traits. The results of the assessment of the meat texture from the cooked meat patties suggested that silverside cuts were consistently harder than shoulder clod cuts, despite having lower pH48 values.
Intrinsic and extrinsic factors affecting the color of fresh beef meat - comprehensive review
(2023) Poveda-Arteaga, Alejandro; Krell, Johannes; Gibis, Monika; Heinz, Volker; Terjung, Nino; Tomasevic, Igor
Meat color research from the last two decades suggests that a combination of different intrinsic (ultimate pH, age of the animals, muscle position, breed, slaughter weight, and sex) and extrinsic factors (production systems and feeding, pre-mortem stress, slaughter season, and chilling rates) might have a deep impact in the color of beef muscle and influence consumers’ acceptance of fresh meat. Ultimate pH and muscle position were perceived as the most determinant intrinsic factors, whereas production systems, feeding, and ante-mortem stress were the extrinsic factors that more strongly influenced beef color attributes. From an industrial perspective, the extrinsic factors can be improved through the technological process at a higher ratio than the intrinsic ones. This review aims to evaluate the effect of each of those factors on myoglobin oxidation and beef color traits from a comprehensive standpoint. All the information discussed in this manuscript focuses on an industrial environment and offers possible solutions and recommendations for the global meat industry.
Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder
(2023) Munyendo, Leah; Njoroge, Daniel; Zhang, Yanyan; Hitzmann, Bernd
Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.
Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications
(2023) Nagel‐Held, Johannes; El Hassouni, Khaoula; Longin, Friedrich; Hitzmann, Bernd
Background and Objectives: Quality assessment of bread wheat is time-consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near-infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods. Findings: Successful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences. Conclusions: The proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations. Significance and Novelty With up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro-doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.
The impact of milk properties and process conditions on consistency of rennet-coagulated curd and syneresis of rennet curd grains
(2008) Thomann, Stephan; Hinrichs, Jörg
Although cheesemaking is an ancient art, modern cheese production relies on the implementation of innovative technology and tailor-made starter bacteria to remain competitive in the production of commodity-type cheeses such as soft and semi-hard cheese. Any intervention in the cheesemaking procedure, i.e. in milk composition, milk treatment and microbial fermentation, affects textural properties of curd at cutting and finally syneresis. The latter is the key step in cheesemaking since the degree of syneresis determines the moisture content of the raw cheese, by which ripening as well as rheological properties and sensory are affected. This work aimed to investigate the syneresis of rennet curd grains in order to generate a kinetic model for predicting syneresis. On the one hand, the experiments covered the implementation of EPS-(exopolysaccharide producing) cultures in the manufacture of soft cheese and likewise the investigation of the cheesemaking potential of Dahlem Cashmere goat?s milk. On the other hand, the interrelated effects of homogenization, microfiltration and pH on rheological properties of rennet-induced milk gels, on syneresis and finally on cheese composition, yield and functionality were to study. Three mathematical models were compared for their suitability describing syneresis and providing kinetic parameters. The kinetic parameters obtained by a linearised model gave best curve fittings to the experimental data with high coefficient of correlation (r² > 0.99). Furthermore, the model provides a parameter (RWRmax) that gives information about the endpoint of syneresis. From this value, interpretation about the curd structure and the interaction of milk composition and physical factors on syneresis is possible. Fermentation media inoculated with non-EPS-producing Streptococcus thermophilus and EPS-producing strains of Lactococcus lactis subsp. cremoris and Lactobacillus sakei were added in a concentration from 5 % to 10 % (w/w) to the milk prior to soft cheese manufacture. The cheesemaking experiments showed that the addition of fermentation media with EPS-cultures retarded syneresis, accelerated microbial fermentation and finally caused ripening problems. By means of model experiments regarding syneresis and influence of pH value, the manufacture of soft cheese was technologically adapted. The approach demonstrated that soft cheese manufacture was yet feasible and moisture content of the raw cheese was increased by the addition of fermentation media, inoculated with EPS-cultures. Analysis of variance revealed that syneresis was significantly affected by homogenization, MF and pH. It was shown that milk composition and MF markedly influenced the endpoint of syneresis, RWRmax. Curd grains made from skim milk had the highest RWRmax value. It is assumed, that differences in curd microstructure due to fat globule distribution and content affect syneresis since cutting was performed at equal curd firmness. The experiments demonstrate that homogenization and MF can be combined to reach curd firmness and syneresis which are in accordance with values in conventional cheesemaking. Combination of homogenization and MF was promising on cheese yield, and based on the results and experience gained in this study, a new and simplified process for semi-hard cheesemaking was invented. It was shown, that the adjusted cheese yield and component recovery increased due to the interaction of homogenization and MF. Hence, the combination of homogenization and MF in cheese manufacture is promising.
Variations in the metabolome of unaged and aged beef from black-and-white cows and heifers by 1H NMR spectroscopy
(2023) Bischof, Greta; Januschewski, Edwin; Witte, Franziska; Terjung, Nino; Heinz, Volker; Juadjur, Andreas; Gibis, Monika
(1) Background: The selection of raw material and the postmortem processing of beef influence its quality, such as taste. In this study, the metabolome of beef from cows and heifers is examined for differences during aging. (2) Methods: Thirty strip loins from eight heifers and seven cows (breed code: 01–SBT) were cut into ten pieces and aged for 0, 7, 14, 21 and 28 days. Samples from the left strip loins were wet-aged in vacuum, while samples from right strip loins were dry-aged at 2 °C and 75% relative humidity. The beef samples were extracted with methanol–chloroform–water, and the polar fraction was used for 1H NMR analysis. (3) Results: The PCA and OPLS-DA showed that the metabolome of cows and heifers varied. Eight metabolites revealed significant differences (p < 0.05) in the samples from cows and heifers. The aging time and aging type of beef also affected the metabolome. Twenty-eight and 12 metabolites differed significantly (p < 0.05) with aging time and aging type, respectively. (4) Conclusions: The variations between cows and heifers and aging time affect the metabolome of beef. By comparison, the influence of aging type is present but less pronounced.

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