Dr. John Kalivas
Office: PSC 253A, Pocatello
Ph.D. Analytical Chemistry, University of Washington, 1982
Research areas: Analytical Chemistry, Chemometrics, Chemical Education
Student experience required for research: CHEM 1111 and CHEM 1112
Student experience gained from research: Data analysis, modeling, computational chemistry, spectroscopy, teaching
Ideal preparation for: Chemical industry, pharmaceuticals, medical, environmental, and agriculture research, teaching, and preparation for graduate school in Chemistry or other professional schools.
Our analytical chemistry research focus is in an area termed chemometrics concerned with mathematical and statistical analysis of chemical data. While this are of research was well established in the early 1970’s, the area is now popularized as data science, machine learning, and artificial intelligence.
We concentrate on developing computer algorithms to determine mathematical relationships between chemical data and analyte properties desired such as the concentration estimate of a substance in a sample. Algorithms are trained to recognize data patterns and predict properties for new situations. For example, training an algorithm to distinguish noninvasively measured spectra of cancer cells from noncancer cells. Our mission is creating new algorithms to solve difficult analytical chemistry problems by leveraging sample and measurement matrix effects (hidden physicochemical properties) as information to work with. Some research projects follow.
Multivariate Calibration (Modeling): Central to many disciplines is multivariate calibration including food analysis, food adulteration detection and authentication of product origin, environmental monitoring, industrial process control, medical diagnosis such as disease detection, pharmaceutical analysis, forensic analysis, detection of hidden radioactive material, and the list goes on. Work in our laboratory consists of developing new mathematical processes and the corresponding computer algorithm implementations in order to improve calibration quality in conjunction with eliminating user decisions making calibration and subsequent analyte predictions automatic. A persistent multidisciplinary problem is developing a calibration model in one set of environmental, instrumental, physicochemical conditions (the primary source conditions) to now work in new target conditions, i.e., the calibration maintenance (transfer) problem.
Our laboratory has two approaches to solve this problem that has been restricting onsite analyses with handheld devices including consumer applications. One solution is local modeling.
With the ever-growing availability of spectral data, e.g., near infrared (NIR), Raman, fluorescence, and other spectral processes, there is a high need for computer algorithms to mine through such data libraries and identify those spectra similar to a spectrum just measured for a new target sample. The user is interested in using the new spectrum for quantitative analysis of an analyte, perhaps glucose content for diabetic, and a model must first be formed using calibration spectra matrix matched to the new target sample spectrum. Because the analyte amount in the new sample is not known, identifying fully matched calibration samples from a library is not a simple task. However, we have developed an algorithm to accomplish this objective that we call local adaptive fusion regression (LAFR). Key takeaways for LAFR are: (1) models are formed by an understandable process based on physicochemical properties and principles with a Beer’s law-like relationship, (2) final calibration sets bracket target sample analyte values, and (3) all adjustable parameters are self-optimized. Ongoing work involves providing a reliability measure based on the probability of a correct prediction.
The second approach is model updating by the transfer learning approach domain adaptation. In collaboration with Erik Andries at Central New Mexico Community College, we now have a null augmented regression (NAR) algorithm that correctly predicts new target samples. Models are formed using the new unlabeled target samples for which is prediction is desired.
The limitation to advancing model updating for practical use has been model selection due to the multiple tuning parameters requiring optimization. We recently developed model diversity prediction similarity (MDPS), an algorithm that selects accurate models for the unlabeled samples. Up to three tuning parameters can be involved and the approach is generalizable to additional tuning parameters.
Both LAFR and NAR/MDPS can make automatic analysis by handheld devices easier, i.e., bringing the lab to the sample.
Data Set Characterization, Outlier Detection, and Classification: Key to both local modeling and model updating is assessing the similarity of a primary source calibration set to new unlabeled target samples. Our laboratory is further developing two measures termed indicators of system uniqueness (ISU). One characterizes hidden physicochemical spectral differences using hundreds of spectral similarities measures (ISUX) and the other is based on analyte amount similarities (ISUy) even though the new target samples are unlabeled. Ongoing work involves combining the two ISU values for a membership product value (ISUXy)
Education: We also have on going chemical education projects in our research laboratory. These consist of developing new laboratory exercises for quantitative analysis and instrumental analysis. In the past, our laboratory has developed new guided inquiry labs for general chemistry and quantitative analysis including chemical analysis of live trout for their fat and moisture contents. Currently, we are working on new labs for instrumental analysis that provide greener approaches to multivariate calibration using one of our newly developed NAR model updating process.
Service-learning is becoming ever more important in the education of students to become responsible chemists. Service-learning involves students in thoughtfully organized service activities addressing community needs and complementing students’ academic studies. Service-learning results from a curriculum that extends the classroom into the community combing education and service and includes class time to reflect on the service experience. Our research desires are to develop new service-learning components in analytical chemistry courses
C. Spiers, J.H. Kalivas: “Calibration Model Updating to Novel Sample and Measurement Conditions without Reference Values”, Analytical Chemistry. In press (2021). https://doi.org/10.1021/acs.analchem.1c00578
C. Spiers, J.H. Kalivas: “Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity”, Journal of Chemical Information and Modeling, 61, 2220-2230 (2021). https://doi.org/10.1021/acs.jcim.0c01493
Struhs, S. Hansen, A. Mirkouei, M.M. Ramirez-Corredores, K. Sharma, R. Spiers, J.H. Kalivas: “Ultrasonic-Assisted Catalytic Transfer Hydrogenation for Upgrading Pyrolysis-oil”, Ultrasonics Sonochemistry, 73, 105502 (2021), Open Access https://doi.org/10.1016/j.ultsonch.2021.105502
H. Kalivas, S.D. Brown: "Calibration Methodologies" in Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, 2nd Edition, editors-in-chief S. Brown, R. Tauler, and B. Walczak, Elsevier, The Netherlands, (2020) https://doi.org/10.1016/B978-0-12-409547-2.14666-9
K. Chabuka, J.H. Kalivas: “Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on FTIR Spectroscopy”, Applied Spectroscopy, 74, 11671183 (2020) https://doi.org/10.1177/0003702820923993.
For MATLAB code to preform Tikhonov regularization without reference samples, see MATLAB Code
For MATLAB code to preform sum of ranking differences (SRD), see 2013_12_16_SRD.7z
For MATLAB code to preform fusion classification, see 2018_3_1_ClassificationCode.7z
For MATLAB code to preform Model updating by sample and feature augmentation, see 2018_9_13_SAFA
For MATLAB code to preform single-class fusion classification with SRD, see 2019_11_25_SingleClassCode.7z
For MATLAB code to preform model selection by model diversity and prediction similarity, see 2020_7_1_MDPS.7z