Dunne, R. A. | Search bioRxiv

bioRxiv2024

Texture profile analysis and rheology of plant-based and animal meat

Plant-based meat can help combat climate change and health risks associated with high meat consumption. To create adequate mimics of animal meats, plant-based meats must match in mouthfeel, taste, and texture. The gold standard to characterize the texture of meat is the double compression test, but this test suffers from a lack of standardization and reporting inconsistencies. Here we characterize the texture of five plant-based and three animal meats using texture profile analysis and rheology, and report ten mechanical features associated with each products elasticity, viscosity, and loss of integrity. Our findings suggest that, of all ten features, the stiffness, storage, and loss moduli are the most meaningful and consistent parameter to report, while other parameters suffer from a lack of interpretability and inconsistent definitions. We find that the sample stiffness varies by an order of magnitude, from 418.9{+/-}41.7kPa for plant-based turkey to 56.7{+/-}14.1kPa for tofu. Similarly, the storage and loss moduli vary from 50.4{+/-}4.1kPa and 25.3{+/-}3.0kPa for plant-based turkey to 5.7{+/-}0.5kPa and 1.3{+/-}0.1kPa for tofu. All three animal products, animal turkey, sausage, and hotdog, consistently rank in between these two extremes. Our results suggest that-with the right ingredients, additives, and formulation-modern food fabrication techniques can create plant-based meats that successfully replicate the full viscoelastic texture spectrum of processed animal meat.

bioengineering↗

bioRxiv2022

Threshold Values for the Gini Variable Importance: A Empirical Bayes Approach

BackgroundRandom Forests (RF) are a widely used modelling tool, enabling feature-selection via a variable importance measure. For this, a threshold is required that separates label-associated features from false positives. In the absence of a good understanding of the characteristics of the variable importance measures, current approaches attempt to select features by training multiple RFs to generate statistical power via a permutation null, employ recursive feature elimination or a combination of both. However, for high-dimensional datasets, such as genome data with millions of variables, this is computationally infeasible. MethodWe present RFlocalfdr, a statistical approach for thresholding that identifies which features are significantly associated with the prediction label and reduces false positives. It builds on the empirical Bayes argument of Efron (2005) and models the variable importance as mixture of two distributions - null and non-null "genes." ResultWe demonstrate on synthetic data that RFlocalfdr has an equivalent accuracy to computationally more intensive approaches, while being up to 100 times faster. RFlocalfdr is the only tested method able to successfully threshold a dataset with 6 Million features and 10,000 samples. RFlocalfdr performs analysis in real-time and is compatible with any RF implementation that returns variable importance and counts, such as ranger or VariantSpark. ConclusionRFlocalfdr allows for robust feature selection by placing a confidence value on the predicted importance score. It does so without repeated fitting of the RF or the use of additional shadow variables and is thus usable for data sets with very large numbers of variables.

bioinformatics↗