Related Experiment Video
Updated: May 18, 2026
06:50
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
Introducing nonlinear, multivariate 'Predictor Surfaces' for quantitative modeling of chemical systems with
Rebecca B Horton1, Morgan McConico, Currie Landry
1Department of Chemistry, University of Tennessee, 552 Buehler Hall, Knoxville, TN 37996-1600, USA.
Analytica Chimica Acta
|September 15, 2012
Summary
New chemometric methods called Predictor Surfaces accurately model complex biological systems. These nonlinear models improve quantitative analysis and visualization of coupled chemical parameters in environmental monitoring using microalgae sensors.
Area of Science:
- Environmental analytical chemistry
- Chemometrics
- Bioanalytical chemistry
Background:
- Chemical systems with nonlinear responses and parameter interdependencies require advanced chemometric methods.
- Conventional linear models are insufficient for complex systems like biological materials.
- Microalgae adapt to environmental changes, showing potential as in situ sensors.
Purpose of the Study:
- To introduce and validate 'Predictor Surfaces' for analyzing nonlinear chemical systems.
- To apply Predictor Surfaces to environmental analytical chemistry using microalgae.
- To compare the performance of Predictor Surfaces against traditional methods like principal component regression.
Main Methods:
- Development of Predictor Surfaces using multivariate Taylor expansions to approximate nonlinear functions.
- Utilizing FT-IR spectroscopy to acquire chemical signatures of microalgae.
- Measuring concentrations of inorganic carbon and nitrogen-containing ions as predictor variables.
Main Results:
- Predictor Surfaces demonstrated higher accuracy in predicting nutrient concentrations compared to principal component regression.
- The method effectively visualizes nonlinearities and coupling among predictor variables.
- Successful application in analyzing microalgae's response to nutrient availability.
Conclusions:
- Predictor Surfaces offer a novel and accurate approach for quantitative and qualitative analysis of complex chemical systems.
- This method enhances the study of biological materials and environmental monitoring.
- Predictor Surfaces serve as a valuable tool for bioanalytical chemistry, biology, and ecology.
Related Concept Videos
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
01:14Multicompartment Models: Overview
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
01:09Pharmacodynamic Models: Additive and Proportional Drug Effect Model
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
02:24Predicting Reaction Outcomes
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...