John H. Kalivas a professor in the Department of Chemistry at Idaho State University has been named the winner of the 2023 EAS Award for Outstanding Achievements in Chemometrics. This award is presented to a significant individual who has made contributions to the advancement of chemometrics by superior work in developing theory, techniques, or instrumentation. The award was presented at a special symposium, arranged in honor of the awardee, at the 2023 Eastern Analytical Symposium on Monday, November 13, at 1:30 pm.
Kalivas obtained his PhD at the University of Washington in 1982 under the direction of Bruce Kowalski. After two temporary lectureship positions at University of Minnesota-Morris and Texas A&M University, he started his tenure track position at ISU in 1985. He has been named the Most Influential Professor eight times by graduating undergraduates receiving the ISU Outstanding Academic Achievement Award.
In 1994, he was named ISU Distinguished Researcher and awarded a Camille and Henry Dreyfus Scholar Award, a recognition given to early career researchers. In 2003 he was named a fellow of the International Union of Pure and Applied Chemistry (IUPAC). In 2021, he received the Idaho Jean’ne M. Shreeve NSF EPSCoR Research Excellence Award and the Society of Applied Spectroscopy Fellows Award in 2022.
Since 1990, 1993, 1998, and 2007, he has been respectively serving on the editorial boards for the Journal of Chemometrics, Analytical Letters, Applied Spectroscopy, and Talanta. He became an associate editor for Applied Spectroscopy in 2010, and an editor for the Journal of Chemometrics in 2013. In 2012, he spearheaded the formation of the Bruce R. Kowalski Award in Chemometrics administered by the Society of Applied Spectroscopy. He is the author or co-author of over 130 professional papers, book chapters, and books dealing with chemometrics.
Much of his research is focused on methodology developments for autonomous optimization of multivariate calibration and classification processes. He has also completed extensive work developing multivariate figures of merit. His recent work established model updating methods concentrating on transfer learning approaches using unlabeled data (transductive semi-supervised learning). These new processes include an autonomous model selection algorithm for up to three metaparameters (tuning parameters) specifically directed toward predicting analyte amounts in a collection of new target samples. Recent work also involved developing a new local modeling strategy that mines a spectral library leveraging hidden sample physicochemical and physiochemical properties to identify matrix matched calibration sample sets relative to predicting each new target sample. With his research team, he advanced a unique in-house fusion process that removes the optimization step for many classification methods.
His current focus is pushing the chemometric frontier using immersive analytics for virtual reality (VR) data visualization to produce new hybrid data analysis structures by combining the computer with human cognitive skills to make more efficient and accurate decisions. Immersive VR allows the user to see inside data configurations as well as feel the inherent data structure with haptic gloves. Presently, the focus is resolving complex classification situations where autonomous algorithms fail.
The program for the award symposium is as follows:
Artificial Intelligence Accelerates Molecular Vibration Analysis, Study Finds
July 1st 2025A new review led by researchers from MIT and Oak Ridge National Laboratory outlines how artificial intelligence (AI) is transforming the study of molecular vibrations and phonons, making spectroscopic analysis faster, more accurate, and more accessible.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.
Combining AI and NIR Spectroscopy to Predict Resistant Starch (RS) Content in Rice
June 24th 2025A new study published in the journal Food Chemistry by lead authors Qian Zhao and Jun Huang from Zhejiang University of Science and Technology unveil a new data-driven framework for predicting resistant starch content in rice
New Spectroscopy Methods Target Counterfeit Oral Medication Syrups
June 23rd 2025Researchers at Georgia College and Purdue University have developed a fast, low-cost method using Raman and UV–visible spectroscopy combined with chemometric modeling to accurately screen and quantify active ingredients in over-the-counter oral syrups, helping to fight counterfeit medications.