AI Technology and the Lab of the Future

In 2022, Agilent announced its acquisition of advanced artificial intelligence (AI) technology developed by Virtual Control, an AI and machine learning software developer that creates innovative analysis solutions in lab testing. Agilent will integrate the software, known as ACIES, into its industry-leading gas chromatography and mass spectrometry (GS/MS) platforms to improve the productivity, efficiency and accuracy of high-throughput labs the company serves around the world.

ACIES automates the labour-intensive task of gas chromatography/mass spectrometry data analysis improving efficiency in the laboratory workflow, from sampling to reporting. Agilent will integrate the technology into its MassHunter software package for LC/MS and GC/MS instruments.

 

Digital labs

This move by Agilent signals that the digital age is very much here for laboratories. Science has always driven the world forward and now it will do the same for laboratories.

The lab of the future is a concept built on the foundation of digitalised labs. It encompasses smart technological workflow systems that are connected and capable of collecting vast amounts of data via integrated automation.

A digitalised lab should be considered a more advanced lab as it has more access to data. With data being key to transforming science, increasing amounts of data generated in any lab, let alone a digitally connected lab, could be a game-changer – but only if it’s collected and synthesised into information and knowledge that is useful.

The digital environment (i.e., paperless work in an electronic format) capitalises on digitalisation. It incorporates all of the necessary instrumentation for complete data analysis and enables the full value of the data for decision-making. The ability to monitor operations and provide more sophisticated insights is a core reason for introducing AI into the operational lab environment.

 

 

Transforming science

Artificial intelligence (AI) is often defined as the ability of a machine to learn how to solve cognitive challenges. However, in the context of scientific methodology and laboratory interconnectivity, AI is starting to be used for capturing data to model human observation and decision-making processes.

Taken forward, connecting all instruments in a lab via AI enables the opportunity for an even more astute understanding of the interactions between technology and also users, potentially providing an all-inclusive view of all laboratory operations.

Accessing this powerful source of information will become a necessary component of scientific productivity. This is an inevitable next step in creating lab management systems that are so efficient and provide knowledge that is so valuable that only AI will be able to produce them.

AI, coupled with universal sensing capabilities to detect and monitor a range of variables, e.g., an instrument’s power draw, enables companies to realise certain operational and financial benefits to their business and plan for the future. Through high-quality and readily available insights, AI enables the simultaneous monitoring of all equipment usage in the lab and holistic capacity tracking.

Watch our webinar on Industrialising High-Throughput Glycoproteomics Using AI for Clinical Use

 

Staying competitive in a competitive world

Globally, scientific innovation is accelerating, so labs need to consider the technology investments required to become digitally enabled in order to keep up and stay competitive. We live in a data-driven world, so scientific laboratories must fundamentally transform how they create, manage, and effectively use all the data that is generated in their lab ecosystem. Achieving and sustaining a competitive edge in a world of constant change will require the continual transformation of lab operations and scientific data management. This will be the first and most important step toward becoming a truly digitalised lab.

 

Standardising honey fingerprinting methods

Although previous work has been done developing case studies for fingerprinting foodstuffs, including honey, the approaches among laboratories have been different regarding sample preparation and instrumental conditions. There are also differences in terms of data processing and analysis. As a result, two laboratories analysing the same sample may obtain slightly different results. Ideally, developing a standardised fingerprinting method that could be used across all LC/MS-based workflows, enabling the same testing technique to be used across multiple laboratories, would be optimal and where future work is aimed.

Read our article on Fingerprinting Honey to Ensure Purity

When addressing the issues of food safety, product quality, and authenticity, each may be governed by separate sets of regulations. For example, looking at the residues of contaminants in honey, such as pesticides, there may be differences globally. Countries may have their restrictions for the maximum limit for specific compounds. Contaminants are a part of the picture when considering fingerprinting for honey, but permitted levels may vary between countries.

Additionally, as samples come from the field to the lab for testing, there is potential interest in reversing this and bringing the lab out into the field instead. This interesting but not yet recognised capability would enable regulators and the global food industry to respond more quickly to honey contamination and food fraud.

Step into the future, elevate your business and talk to our team of experts about how you can improve the productivity, efficiency and accuracy of your lab.

High-Throughput Intact Native Protein Analysis

Ion mobility-mass spectrometry has become a valuable analytical tool in native protein analysis. In protein structure studies, ion mobility spectrometry provides rotationally averaged collision cross-section values that correlate to size and shape of the biomolecule. For proteins, ground state CCS and accurate mass are not adequate to identify different proteins. Therefore, the introduction of gas phase unfolding followed by ion mobility measurements provide unique fingerprints for native protein analysis. This collision-induced unfolding (CIU) technique can be utilized to identify proteins and protein complexes. Typical CIU experiments utilize static nano-ESI or standard ESI using a syringe pump for sample introduction which is difficult to automate. In this study, we have developed a new automated sample introduction method for high-throughput CIU experiments which can be adapted for IgG and other proteins.

 

Sheher Banu Mohsin, Ph.D.
Senior Applications Scientist
Agilent Technologies, Inc.

 

Sheher Mohsin is a senior applications scientist at Agilent Technologies. She received her Ph. D in physical chemistry from the University of Illinois and an MBA from Rockhurst University. She started her career at the US Environmental Protection Agency working on dioxin analysis with high-resolution mass spectrometers. She later joined Bayer and worked in the special analysis lab using mass spectrometry to solve problems in synthesis, impurity determination and submission of final product impurity profile to regulatory agencies. Sheher’s current focus is on lipidomics using GC, LC and SFC separations and mass spectrometry. Sheher collaborates with academic and government researchers working on complex problems to come up with innovative, simplified workflows using the latest tools in separation and mass spectrometry.

 

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Microplastics in the Environment Virtual Symposium

Agilent’s expertise provides a range of analytical solutions to both identify and quantify microplastics in the environment.

In our Microplastics Symposium, hear from industry experts and peers working within the field of Microplastics.

With a mixture of live talks across various topic areas and product demonstrations, this event is a great opportunity to uncover more about microplastics analysis in the lab. We will also have our experts available to chat live on the day, allowing you to further increase your knowledge and skills on this topical issue.

 

What topic areas can you expect to see on the day?

  • Microplastics Analysis with the 8700 LDIR with a focus on the marine environment;
  • Quantification of Microplastics with GC/MSD;
  • Current activities in the world of standardization;
  • Microplastics Analysis with the GC/Q-TOF;
  • And more.

 

Agenda

 

Register Here >