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Predicting Bitterness in Foods Using Mass Spectrometry

Predicting Bitterness in Foods Using Mass Spectrometry

14 July, 2024

 

BitterMasS, a novel tool utilizing mass spectrometry, promises a revolutionary leap in predicting bitterness in compounds. Developed through interdisciplinary collaboration, it offers enhanced precision and efficiency compared to traditional methods, with wide-ranging applications in food science, pharmaceuticals, and beyond. BitterMasS not only accelerates taste perception research but also holds potential for transforming food processing, health discoveries, and safety monitoring, marking a significant advancement in taste prediction and compound screening technologies.

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Introducing BitterMasS, a pioneering tool developed by a team led by Phd student Evgenii Ziaikin and Prof. Masha Niv from Hebrew University and Dr. Edisson Tello and Prof. Devin Peterson from Ohio State University. BitterMasS harnesses the power of mass spectrometry to predict bitterness in compounds without requiring prior knowledge of their chemical structures. This advancement marks a significant departure from traditional methods that relied on structural data, which only covers a small fraction of the metabolome.

Bitterness, a fundamental taste modality potentially related to toxic substances, has long intrigued scientists and food experts alike. Today, an innovating study promises to revolutionize how bitterness is understood and managed in foods and beverages.

Using a dataset of over 5,400 experimental mass spectra of bitter and non-bitter compounds, BitterMasS achieved remarkable precision and recall rates in internal tests. For external validation, the tool demonstrated robust performance, accurately identifying bitter compounds without structural information. These findings underscore BitterMasS potential to streamline compound screening processes in food science, pharmaceuticals, and beyond.

"BitterMasS represents a critical shift in taste prediction," said Prof. Masha Niv, lead researcher. "By leveraging mass spectrometry data, we can now predict bitterness directly and efficiently, opening doors to new discoveries in health-promoting compounds and enhanced food processing techniques."

Researchers envision BitterMasS as a versatile tool capable of monitoring bitterness changes over time, providing critical insights into food quality and safety. This innovative approach also offers practical applications in drug development and metabolomics. 

In summary, BitterMasS stands as a testament to the power of interdisciplinary collaboration and technological innovation in advancing our understanding of taste. Its implications extend far beyond the lab, potentially reshaping how we perceive and utilize bitter compounds in various industries.

The research paper titled “BitterMasS: Predicting Bitterness from Mass Spectra” is now available in the Journal of Agricultural and Food Chemistry and can be accessed at https://www.webofscience.com/wos/woscc/full-record/WOS:001226287400001 

DOI 10.1021/acs.jafc.3c09767

Researchers:

Evgenii Ziaikin1, Eddison Tellow2, Devin G. Peterson2, Masha Y. Niv1

Institutions:

1. Hebrew University Jerusalem, Institute of Biochemistry Food & Nutrition, Robert H Smith Faculty of Agriculture, Food & Environment

2. Ohio State University, College of Food Agriculture and Environmental Sciences

The Hebrew University of Jerusalem is Israel's premier academic and research institution. Serving over 23,000 students from 80 countries, the University produces nearly 40% of Israel’s civilian scientific research and has received over 11,000 patents. Faculty and alumni of the Hebrew University have won eight Nobel Prizes and a Fields Medal. For more information about the Hebrew University, please visit http://new.huji.ac.il/en. 

 

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Enhancing Quantum Technology Performance Tenfold

Enhancing Quantum Technology Performance Tenfold

10 July, 2024

 

Researchers have developed a new method to significantly enhance quantum technology performance by using the cross-correlation of two noise sources to extend coherence time, improve control fidelity, and increase sensitivity for high-frequency sensing. This innovative strategy addresses key challenges in quantum systems, offering a tenfold increase in stability and paving the way for more reliable and versatile quantum devices.

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Researchers have made a significant breakthrough in quantum technology by developing a novel method that dramatically improves the stability and performance of quantum systems. This pioneering work addresses the longstanding challenges of decoherence and imperfect control, paving the way for more reliable and sensitive quantum devices.

Quantum technologies, including quantum computers and sensors, hold immense potential for revolutionizing various fields such as computing, cryptography, and medical imaging. However, their development has been hampered by the detrimental effects of noise, which can disrupt quantum states and lead to errors.

Many traditional approaches to mitigating noise in quantum systems primarily focus on temporal autocorrelation, which examines how noise behaves over time. While effective to some extent, these methods fall short when other types of noise correlations are present.

The research was conducted by experts in quantum physics, PhD. student Alon Salhov under the guidance of Prof. Alex Retzker from Hebrew University, PhD. student Qingyun Cao under the guidance of Prof. Fedor Jelezko and Dr. Genko Genov from Ulm University and Prof. Jianming Cai from Huazhong University of Science and Technology. They have introduced an innovative strategy that leverages the cross-correlation between two noise sources. By exploiting the destructive interference of cross-correlated noise, the team has managed to significantly extend the coherence time of quantum states, improve control fidelity, and enhance sensitivity for high-frequency quantum sensing.

Key achievements of this new strategy include:

Tenfold Increase in Coherence Time: The duration for which quantum information remains intact is extended ten times longer compared to previous methods.

Improved Control Fidelity: Enhanced precision in manipulating quantum systems leads to more accurate and reliable operations.

Superior Sensitivity: The ability to detect high-frequency signals surpasses the current state-of-the-art, enabling new applications in quantum sensing.

Alon Salhov said, "Our innovative approach extends our toolbox for protecting quantum systems from noise. By focusing on the interplay between multiple noise sources, we've unlocked unprecedented levels of performance, bringing us closer to the practical implementation of quantum technologies."

This advancement not only marks a significant leap in the field of quantum research but also holds promise for a wide range of applications. Industries that rely on highly sensitive measurements, such as healthcare, stand to benefit enormously from these improvements.

The study titled “Protecting Quantum Information via Destructive Interference of Correlated Noise” is now available in Physical Review Letters and can be openly accessed at https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.223601

Researchers:

Alon Salhov1, Qingyun Cao2,3, Jianming Cai3, Alex Retzker1,4, Fedor Jelezko2, and Genko Genov2

Institutions:

1. Racah Institute of Physics, The Hebrew University of Jerusalem

2. Institute for Quantum Optics, Ulm University, Germany

3. School of Physics, International Joint Laboratory on Quantum Sensing and Quantum Metrology, Huazhong University of Science and Technology, China

4. AWS Center for Quantum Computing, USA

Funding

Clore Israel Foundation Scholars Programme, the Israeli Council for Higher Education, and the Milner Foundation. This work was funded by the German Federal Ministry of Research (BMBF) by future cluster QSENS and projects DE-Brill (No. 13N16207), SPINNING, DIAQNOS (No. 13N16463), quNV2.0 (No. 13N16707), QR. X and Quamapolis (No. 13N15375), DLR via project QUASIMODO (No. 50WM2170), Deutsche Forschungsgemeinschaft (DFG) via Projects No. 386028944, No. 445243414, and No. 387073854, and Excellence Cluster POLiS European Union’s HORIZON Europe program via projects QuMicro (No. 101046911), SPINUS (No. 101135699), CQuENS (No. 101135359), QCIRCLE (No. 101059999) and FLORIN (No. 101086142), European Research Council (ERC) via Synergy grant HyperQ (No. 856432) and Carl-Zeiss-Stiftung via the Center of Integrated Quantum Science and technology (IQST) and project Utrasens-Vir. A. R. acknowledges the support of European Research Council grant QRES, Project No. 770929, Quantera grant MfQDS, Israel Science Foundation and the Schwartzmann university chair. J. M. acknowledges the National Natural Science Foundation of China (Grants No. 12161141011).

Pictures credit - the authors of the paper.

Image 1)

Title: Enhanced Quantum Memory and Sensitivity by Interfering Noise

Description:

Schematic representation of destructive interference of cross-correlated noise, control sequences and experimental setup. 

Detailed description (from the paper):

(a) The qubit is subjected to environmental noise δ(t). Applying a resonant drive with Rabi frequency Ω1 creates a protected dressed qubit which decoheres mainly due to ε1(t) - the amplitude noise in Ω1. Applying a second drive with modulation frequency eΩ1, Rabi frequency Ω2 and amplitude fluctuations ε2(t), reduces decoherence due to ε1(t). 

(b) If the cross-correlation, c, of ε1(t) and ε2(t) is nonzero, a detuning eΩ1 = Ω1 + c Ω2 2/Ω1 tilts the effective-drive axis and induces a destructive interference of the cross-correlated noise, resulting in a doubly-dressed qubit with a longer coherence time. 

(c) Measurement sequences for standard and correlated double drive (DD) protocols. (d) Experimental setup and level

scheme of the NV center

Image 2)

Title: Enhanced Quantum Memory and Sensitivity by Interfering Noise

Description: A Bloch-sphere of a qubit subjected to cross-correlated noise (blue and red). The method destructively interferes this noise, resulting in superior performance.

The Hebrew University of Jerusalem is Israel’s premier academic and research institution. With over 25,000 students from 90 countries, it is a hub for advancing scientific knowledge and holds a significant role in Israel’s civilian scientific research output, accounting for nearly 40% of it and has registered over 11,000 patents. The university’s faculty and alumni have earned eight Nobel Prizes and a Fields Medal, underscoring their contributions to ground-breaking discoveries. In the global arena, the Hebrew University ranks 86th according to the Shanghai Ranking. To learn more about the university’s academic programs, research initiatives, and achievements, visit the official website at http://new.huji.ac.il/en

 

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Revolutionizing Disease Classification and Identifying Hidden Disease Patterns

Revolutionizing Disease Classification and Identifying Hidden Disease Patterns

9 July, 2024

 

Researchers have developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model, which achieved an 89.4% ROC AUC, uncovered 515 previously unannotated disease subtypes, demonstrating the potential for more precise and personalized medical treatments.

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Researchers from the Hebrew University of Jerusalem have developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing the field of disease classification and treatment strategies. The study, led by PhD student Dan Ofer and Prof. Michal Linial from the Department of Biological Chemistry at The Life Science Institute at Hebrew University, marks a significant advancement in the use of artificial intelligence in medical research.

Distinguishing diseases into distinct subtypes is pivotal for accurate study and effective treatment strategies. The Open Targets Platform integrates biomedical, genetic, and biochemical datasets to support disease ontologies, classifications, and potential gene targets. However, many disease annotations remain incomplete, often necessitating extensive expert medical input. This challenge is especially significant for rare and orphan diseases, where resources are limited.

The research introduces a novel machine learning approach to identify diseases with potential subtypes. Utilizing the extensive database of approximately 23,000 diseases documented in the Open Targets Platform, they derived new features to predict diseases with subtypes using direct evidence. Machine learning models were then applied to analyze feature importance and evaluate predictive performance, uncovering both known and novel disease subtypes.

The model achieved an impressive 89.4% ROC Area Under the Receiver Operating Characteristic Curve in identifying known disease subtypes. The integration of pre-trained deep-learning language models further enhanced the model's performance. Notably, the research identified 515 disease candidates predicted to possess previously unannotated subtypes, paving the way for new insights into disease classification.

"This project demonstrates the incredible potential of machine learning in expanding our understanding of complex diseases," said Dan Ofer. "By leveraging advanced models, we can uncover patterns and subtypes that were previously hidden, ultimately contributing to more precise and personalized treatments."

This innovative methodology enables a robust and scalable approach for improving knowledge-based annotations and provides a comprehensive assessment of disease ontology tiers. "We are excited about the potential of our machine learning approach to revolutionize disease classification," said Prof. Michal Linial. "Our findings can significantly contribute to personalized medicine, offering new avenues for therapeutic development."

The research paper titled “Automated annotation of disease subtypes” is now available in Journal of Biomedical Informatics and can be accessed at https://doi.org/10.1016/j.jbi.2024.104650.

Researchers:

Dan Ofer, Michal Linial

Institution:

Department of Biological Chemistry, The Life Science Institute, The Hebrew University of Jerusalem, Israel

The Hebrew University of Jerusalem is Israel's premier academic and research institution. Serving over 23,000 students from 80 countries, the University produces nearly 40% of Israel’s civilian scientific research and has received over 11,000 patents. Faculty and alumni of the Hebrew University have won eight Nobel Prizes and a Fields Medal. For more information about the Hebrew University, please visit http://new.huji.ac.il/en. 

 

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Ozone's Influence on Exoplanetary Climate – New Study

Ozone's Influence on Exoplanetary Climate – New Study

9 May, 2024

 

In the quest for life beyond our solar system, a new study delves into the atmospheric dynamics of planet Proxima Centauri b, illuminating ozone's pivotal role in shaping planetary climates. This research signifies a significant leap forward in our understanding of habitable exoplanets.

Unravelling Isopods' Culinary Secrets and Why It Matters for Ecosystems

Unravelling Isopods' Culinary Secrets and Why It Matters for Ecosystems

6 May, 2024

 

New research on desert isopods' dietary preferences is the revelation of the complex factors influencing their food choices. By understanding how these animals meticulously regulate their nutrient intake and prefer biological soil crusts over plant litter, the study highlights the intricate dynamics of trophic interactions. Understanding the dietary preferences of desert isopods sheds light on the intricate interplay between organisms and their environment, informing ecosystem management and conservation strategies.