Institut für Lebensmittelwissenschaft und Biotechnologie

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Coffee leaf tea from El Salvador: on-site production considering influences of processing on chemical composition
(2022) Steger, Marc C.; Rigling, Marina; Blumenthal, Patrik; Segatz, Valerie; Quintanilla-Belucci, Andrès; Beisel, Julia M.; Rieke-Zapp, Jörg; Schwarz, Steffen; Lachenmeier, Dirk W.; Zhang, Yanyan
The production of coffee leaf tea (Coffea arabica) in El Salvador and the influences of processing steps on non-volatile compounds and volatile aroma-active compounds were investigated. The tea was produced according to the process steps of conventional tea (Camellia sinensis) with the available possibilities on the farm. Influencing factors were the leaf type (old, young, yellow, shoots), processing (blending, cutting, rolling, freezing, steaming), drying (sun drying, oven drying, roasting) and fermentation (wild, yeast, Lactobacillus). Subsequently, the samples were analysed for the maximum levels of caffeine, chlorogenic acid, and epigallocatechin gallate permitted by the European Commission. The caffeine content ranged between 0.37–1.33 g/100 g dry mass (DM), the chlorogenic acid was between not detectable and 9.35 g/100 g DM and epigallocatechin gallate could not be detected at all. Furthermore, water content, essential oil, ash content, total polyphenols, total catechins, organic acids, and trigonelline were determined. Gas chromatography—mass spectrometry—olfactometry and calculation of the odour activity values (OAVs) were carried out to determine the main aroma-active compounds, which are β-ionone (honey-like, OAV 132-927), decanal (citrus-like, floral, OAV 14-301), α-ionone (floral, OAV 30-100), (E,Z)-2,6-nonadienal (cucumber-like, OAV 18-256), 2,4-nonadienal (melon-like, OAV 2-18), octanal (fruity, OAV 7-23), (E)-2 nonenal (citrus-like, OAV 1-11), hexanal (grassy, OAV 1-10), and 4-heptenal (green, OAV 1-9). The data obtained in this study may help to adjust process parameters directly to consumer preferences and allow coffee farmers to earn an extra income from this by-product.
Fed-batch bioreactor cultivation of Bacillus subtilis using vegetable juice as an alternative carbon source for lipopeptides production: a shift towards a circular bioeconomy
(2024) Gugel, Irene; Vahidinasab, Maliheh; Benatto Perino, Elvio Henrique; Hiller, Eric; Marchetti, Filippo; Costa, Stefania; Pfannstiel, Jens; Konnerth, Philipp; Vertuani, Silvia; Manfredini, Stefano; Hausmann, Rudolf; Gugel, Irene; Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy, (S.V.);; Vahidinasab, Maliheh; Department of Bioprocess Engineering (150k), Institute of Food Science and Biotechnology, University of Hohenheim, Fruwirthstrasse 12, 70599 Stuttgart, Germany; (E.H.B.P.);; Benatto Perino, Elvio Henrique; Department of Bioprocess Engineering (150k), Institute of Food Science and Biotechnology, University of Hohenheim, Fruwirthstrasse 12, 70599 Stuttgart, Germany; (E.H.B.P.);; Hiller, Eric; Department of Bioprocess Engineering (150k), Institute of Food Science and Biotechnology, University of Hohenheim, Fruwirthstrasse 12, 70599 Stuttgart, Germany; (E.H.B.P.);; Marchetti, Filippo; Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy, (S.V.);; Costa, Stefania; Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy, (S.V.);; Pfannstiel, Jens; Core Facility Hohenheim, Mass Spectrometry Unit, University of Hohenheim, Ottlie-Zeller-Weg 2, 70599 Stuttgart, Germany; Konnerth, Philipp; Department of Conversion Technology of Biobased Resources, University of Hohenheim, Garbenstrasse 9, 70599 Stuttgart, Germany;; Vertuani, Silvia; Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy, (S.V.);; Manfredini, Stefano; Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy, (S.V.);; Hausmann, Rudolf; Department of Bioprocess Engineering (150k), Institute of Food Science and Biotechnology, University of Hohenheim, Fruwirthstrasse 12, 70599 Stuttgart, Germany; (E.H.B.P.);; Gudiña, Eduardo
In a scenario of increasing alarm about food waste due to rapid urbanization, population growth and lifestyle changes, this study aims to explore the valorization of waste from the retail sector as potential substrates for the biotechnological production of biosurfactants. With a perspective of increasingly contributing to the realization of the circular bioeconomy, a vegetable juice, derived from unsold fruits and vegetables, as a carbon source was used to produce lipopeptides such as surfactin and fengycin. The results from the shake flask cultivations revealed that different concentrations of vegetable juice could effectively serve as carbon sources and that the fed-batch bioreactor cultivation strategy allowed the yields of lipopeptides to be significantly increased. In particular, the product/substrate yield of 0.09 g/g for surfactin and 0.85 mg/g for fengycin was obtained with maximum concentrations of 2.77 g/L and 27.53 mg/L after 16 h, respectively. To conclude, this study provides the successful fed-batch cultivation of B. subtilis using waste product as the carbon source to produce secondary metabolites. Therefore, the consumption of agricultural product wastes might be a promising source for producing valuable metabolites which have promising application potential to be used in several fields of biological controls of fungal diseases.
Food informatics - Review of the current state-of-the-art, revised definition, and classification into the research landscape
(2021) Krupitzer, Christian; Stein, Anthony
Background: The increasing population of humans, changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the Internet of Things (IoT) and Artificial Intelligence (AI) technology, including Machine Learning and data analytics, might help to account for these challenges. Scope and Approach: Several research perspectives, among them Precision Agriculture, Industrial IoT, Internet of Food, or Smart Health, already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept is Food Informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of Food Informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangent to the food production process and delineate it from other, existing research streams in the domain. Key Findings and Conclusions: Many different concepts related to the digitalization in food science overlap. Further, Food Informatics is vaguely defined. In this paper, we provide a clear definition of Food Informatics and delineate it from related concepts. We corroborate our new perspective on Food Informatics by presenting several case studies about how it can support the food production as well as the intermediate steps until its consumption, and further describe its integration with related concepts.
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.
Microencapsulation of bacteriophages for the delivery to and modulation of the human gut microbiota through milk and cereal products
(2022) Schubert, Christina; Fischer, Sabina; Dorsch, Kathrin; Teßmer, Lutz; Hinrichs, Jörg; Atamer, Zeynep
There is a bidirectional interaction between the gut microbiota and human health status. Disturbance of the microbiota increases the risk of pathogen infections and other diseases. The use of bacteriophages as antibacterial therapy or prophylaxis is intended to counteract intestinal disorders. To deliver bacteriophages unharmed into the gut, they must be protected from acidic conditions in the stomach. Therefore, an encapsulation method based on in situ complexation of alginate (2%), calcium ions (0.5%), and milk proteins (1%) by spray drying was investigated. Powdered capsules with particle sizes of ~10 µm and bacteriophage K5 titers of ~108 plaque forming units (pfu) g−1 were obtained. They protected the bacteriophages from acid (pH 2.5) in the stomach for 2 h and released them within 30 min under intestinal conditions (in vitro). There was no loss of viability during storage over two months (4 °C). Instead of consuming bacteriophage capsules in pure form (i.e., as powder/tablets), they could be inserted into food matrices, as exemplary shown in this study using cereal cookies as a semi-solid food matrix. By consuming bacteriophages in combination with probiotic organisms (e.g., via yoghurt with cereal cookies), probiotics could directly repopulate the niches generated by bacteriophages and, thus, contribute to a healthier life.
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.
Online 2D fluorescence monitoring in microtiter plates allows prediction of cultivation parameters and considerable reduction in sampling efforts for parallel cultivations of Hansenula polymorpha
(2022) Berg, Christoph; Ihling, Nina; Finger, Maurice; Paquet-Durand, Olivier; Hitzmann, Bernd; Büchs, Jochen
Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.
The potential of spectroscopic techniques in coffee analysis - a review
(2021) Munyendo, Leah; Njoroge, Daniel; Hitzmann, Bernd
This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.
Production of coffee cherry spirits from Coffea arabica varieties
(2022) Blumenthal, Patrik; Steger, Marc C.; Quintanilla Bellucci, Andrès; Segatz, Valerie; Rieke-Zapp, Jörg; Sommerfeld, Katharina; Schwarz, Steffen; Einfalt, Daniel; Lachenmeier, Dirk W.
Coffee pulp, obtained from wet coffee processing, is the major by-product accumulating in the coffee producing countries. One of the many approaches valorising this underestimated agricultural residue is the production of distillates. This research project deals with the production of spirits from coffee pulp using three different Coffea arabica varieties as a substrate. Coffee pulp was fermented for 72 h with a selected yeast strain (Saccharomyces cerevisiae L.), acid, pectin lyase, and water. Several parameters, such as temperature, pH, sugar concentration and alcoholic strength were measured to monitor the fermentation process. Subsequently, the alcoholic mashes were double distilled with stainless steel pot stills and a sensory evaluation of the products was conducted. Furthermore, the chemical composition of fermented mashes and produced distillates were evaluated. It showed that elevated methanol concentrations (>1.3 g/L) were present in mashes and products of all three varieties. The sensory evaluation found the major aroma descriptor for the coffee pulp spirits as being stone fruit. The fermentation and distillation experiments revealed that coffee pulp can be successfully used as a raw material for the production of fruit spirits. However, the spirit quality and its flavour characteristics can be improved with optimised process parameters and distillation equipment.
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.
Using a machine learning regression approach to predict the aroma partitioning in dairy matrices
(2024) Anker, Marvin; Borsum, Christine; Zhang, Youfeng; Zhang, Yanyan; Krupitzer, Christian
Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.
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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