Best of the Week: Machine Learning in Measurements, AOAC International Awards

This week, LCGC International published a variety of articles on the hottest topics in chromatography. Below, we’ve highlighted some of the most popular articles, according to our readers. Happy reading!

The LCGC Blog: Molecular Feature Generation for Machine Learning in Analytical Measurements

Kevin A. Schug

Predicting physicochemical properties for molecules and optimizing chemical analysis processes are both important aspects of modern analytical science. These are data-intense processes, which can often involve a multitude of chemical measurements or high-level theoretical computations. The development of artificial intelligence (AI) strategies, such as those based on machine learning (ML) and deep learning (DL), have helped make processes more efficient, but they still require a significant amount of high-quality data. In this edition of The LCGC Blog, Kevin Schug of the Univerity of Texas at Arlington discusses how machine learning can be optimized for these processes.

AOAC International Recognizes Innovators with 2024 Awards

Aaron Acevedo

Founded in 1884, AOAC International is an independent, non-profit member association of analytical science professionals in government, industry, and academia. Its mission is to advance food safety and product integrity through standards, validated test methods, and laboratory quality programs. This Annual Meeting marks the 138th iteration of the conference. The conference aims to help attendees take advantage of emerging methods and trending topics, discover new technologies, and gain professional insights from industry insiders. At the AOAC International 2024 Annual Meeting & Exposition in Baltimore, Maryland, analytical several scientists were recognized for excellence across multiple disciplines, including method development, expert review panels, editorial contributions, and technical service.

SFE-SFC-MS Used to Analyze Transferred Plastic Additives from Laboratory Materials

Aaron Acevedo

Composed of organic polymers and small-molecule additives to obtain specific physico-chemical properties in the final product, each type of plastic composition has different properties that are suitable for different applications. One such type of application is in the hospital sector, with plastics being used in medical devices like infusion tubing, blood bags, syringes, and medical gloves. Introducing additives is vital during plastic formulation, as it can help control material properties, like flexibility or color, while potentially increasing a product’s lifetime by enhancing resistance to oxidation, thermal stability, and aging degradation. However, with plastic additives, there is always a risk for potential migration, which can lead to their transfer to blood, nutritive liquid, water, or human skin, among others. For this study, the scientists combined supercritical fluid extraction SFE-SFC-MS in an online system in hopes of characterizing plastic additives in laboratory gloves, which were taken as samples of medical devices.

SESI-HRMS and GC-MS, LC-MS Compared for Exhaled Breath Analysis

Aaron Acevedo

One valuable technique for studying exhaled breath is on-line breath analysis using secondary electrospray ionization (SESI) coupled to high-resolution-mass spectrometry (HRMS). This technique is a non-destructive, real-time method that does not require sample preparation for assessing food safety, quality, or the region of origin of some specific products (2). This approach offers continuous monitoring due to real-time analysis, which is an advantage that has been applied in various studies. However, one limitation is the absence of a separation stage prior to ionization, such as chromatography. To address this, scientists from ETHZ and Agroscope in Zurich and Bern, Switzerland, tested a new means of analysis against secondary electrospray ionization (SESI) coupled to high-resolution-mass spectrometry (HRMS) for exhaled breath analysis compared to gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS).

Identifying Key Volatile Compounds in Tilapia During Air Frying by Quantitative GC-IMS

John Chasse

Tilapia, the world’s third largest freshwater aquaculture fish, has the advantages of fast growth and large yield around the world due to the strong environmental adaptability (2,3). Rich in nutrients such as unsaturated fatty acids and proteins, tilapia is usually processed into frozen filets because of the lack of intermuscular spines. A recent study published in Molecules identified and quantified the volatile compounds (VCs) of tilapia by quantitative gas chromatography–ion mobility spectrometry (GC-IMS), followed by the selection of key VCs based on the calculation of the odor activity value (OAV). The contents of malondialdehyde (MDA), total sulfhydryl, and protein carbonyl were analyzed as well. The relationship among the key VCs and oxidative indexes of proteins and lipids was researched by a correlation analysis to determine the possible formation mechanism.