Margriet M. W. B. Hendriks

Rationale: The proteolytic behavior of chymosins during cheese ripening impacts cheese texture, flavor, and quality. Especially, the specific hydrolysis of αS1‐casein to αS1‐I‐casein is relevant for early cheese texture development, and this activity varies between chymosin variants. Accurate profiling of αS1‐casein and αS1‐I‐casein during cheese ripening is crucial for understanding these differences between chymosins and the impact on cheese quality. Methods: Intact‐protein liquid chromatography–high‐resolution mass spectrometry (LC‐HRMS) was employed to monitor the hydrolysis of αS1‐casein to αS1‐I‐casein during mozzarella cheese ripening. Cheese samples, produced with both bovine and camel chymosin variants, were collected at multiple ripening stages. Protein extraction, separation, and quantification were performed using optimized LC‐HRMS protocols to assess hydrolysis kinetics and phosphorylation variants. Results: LC‐HRMS analysis revealed that the tested bovine chymosins displayed significantly higher rates of αS1‐casein hydrolysis to αS1‐I‐casein compared to camel chymosins throughout mozzarella cheese ripening. Distinct phosphorylation variants were confidently detected and quantified. Kinetic profiles demonstrated consistent proteolytic activity differences between coagulants. Conclusions: LC‐HRMS allows tracking of αS1‐casein hydrolysis kinetics throughout mozzarella cheese ripening. This approach not only highlights the pronounced and faster proteolytic activity of bovine chymosins compared to camel chymosins, resulting in more rapid αS1‐I‐casein formation, but also facilitates the monitoring of phosphorylation variants present in the cheese. These findings emphasize the enzyme‐specific effects on cheese texture and quality, demonstrating the importance of advanced protein analysis for comprehensively understanding cheese production and maturation processes.

Background: There is a continuous demand to create new, superior sensory food experiences. In the food industry, yeast-derived flavor products (YPs) are often used as ingredients in foods to create new aromas and taste qualities that are appreciated by consumers. Methods: Chicken bouillon samples containing diverse YPs were chemically and sensorially characterized using statistical multivariate analyses. The sensory evaluation was performed using quantitative descriptive analysis (QDA) by trained panelists. Thirty-four sensory attributes were scored, including odor, flavor, mouthfeel, aftertaste and afterfeel. Untargeted metabolomic profiles were obtained using stir bar sorptive extraction (SBSE) coupled to GC-MS, RPLC-MS and targeted HILIC-MS. Results: In total, 261 volatiles were detected using GC-MS, from chemical groups of predominantly aldehydes, esters, pyrazines and ketones. Random Forest (RF) modeling revealed volatiles associated with roast odor (2-ethyl-5-methyl pyrazine, 2,3,5-trimethyl-6-isopentyl pyrazine) and chicken odor (2,4-nonadienal, 2,4-decadienal, 2-acetyl furan), which could be predicted by our combined model with R² > 0.5. In total, 2305 non-volatiles were detected for RPLC-MS and 34 for targeted HILIC-MS, where fructose-isoleucine and cyclo-leucine-proline were found to correlate with roast flavor and odor. Furthermore, a list of metabolites (glutamate, monophosphates, methionyl-leucine) was linked to umami-related flavor. This study describes a straightforward data-driven approach for studying foods with added YPs to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights limitations and preconditions for good prediction models. Overall, this study emphasizes a matrix-based approach for the prediction of food taste, which can be used to analyze foods for targeted flavor design or quality control.

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having “garlic-like” and “onion-like” attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.

Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates for correlation coefficients. In modern life sciences, comprehensive measurement techniques are used to measure metabolites, proteins, gene-expressions and other types of data. All these measurement techniques have errors. Whereas in the old days, with simple measurements, the errors were also simple, that is not the case anymore. Errors are heterogeneous, non-constant and not independent. This hampers the quality of the estimated correlation coefficients seriously. We will discuss the different types of errors as present in modern comprehensive life science data and show with theory, simulations and real-life data how these affect the correlation coefficients. We will briefly discuss ways to improve the estimation of such coefficients.

Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates for correlation coefficients. In modern life sciences, comprehensive measurement techniques are used to measure metabolites, proteins, gene-expressions and other types of data. All these measurement techniques have errors. Whereas in the old days, with simple measurements, the errors were also simple, that is not the case anymore. Errors are heterogeneous, non-constant and not independent. This hampers the quality of the estimated correlation coefficients seriously. We will discuss the different types of errors as present in modern comprehensive life science data and show with theory, simulations and real-life data how these affect the correlation coefficients. We will briefly discuss ways to improve the estimation of such coefficients.

Eigenanalysis is common practice in biostatistics, and the largest eigenvalue of a data set contains valuable information about the data. However, to make inferences about the size of the largest eigenvalue, its distribution must be known. Johnstone's theorem states that the largest eigenvalues l₁ of real random covariance matrices are distributed according to the Tracy–Widom distribution of order 1 when properly normalized to , where η_np and ξ_np are functions of the data matrix dimensions n and p. Very often, data are expressed in terms of correlations (autoscaling) for which case Johnstone's theorem does not work because the normalizing parameters η_np and ξ_np are not theoretically known. In this paper we propose a semi‐empirical method based on test‐equating theory to numerically approximate the normalization parameters in the case of autoscaled matrices. This opens the way of making inferences regarding the largest eigenvalue of an autoscaled data set. The method is illustrated by means of application to two real‐life data sets. Copyright © 2011 John Wiley & Sons, Ltd.