Chemometrics and Lab Data Analysis Course

Lesson 1 • Probability Distributions in Measurement

Map common distributions—normal, Poisson, binomial—to real analytical scenarios. Supports hypothesis testing and error modeling introduced later.

Lesson 2 • Data Visualization Fundamentals

Select and construct histograms, box plots, and scatter plots appropriate for lab data. Visualization skills recur in every analytical chapter that follows.

Lesson 3 • Types and Sources of Laboratory Data

Distinguish continuous, discrete, and categorical measurement types common in analytical labs. Establishes the data vocabulary used throughout the course.

Lesson 4 • Error, Uncertainty, and Significant Figures

Differentiate systematic from random error and propagate uncertainty through calculations. Directly underpins calibration and validation work in later chapters.

Lesson 1 • Normalization and Scaling Techniques

Apply mean-centering, autoscaling, and range scaling to equalize variable contributions. Scaling choices critically affect PCA and regression outcomes in later chapters.

Lesson 2 • Spectral Preprocessing Methods

Correct baselines, smooth noise, and apply scatter corrections to spectral data. These steps are prerequisites for reliable multivariate spectral modeling.

Lesson 3 • Missing Data Strategies

Diagnose missing-at-random vs. systematic gaps and apply imputation or deletion methods. Proper handling preserves statistical power for multivariate models.

Lesson 4 • Outlier Detection and Treatment

Apply Grubbs, Dixon, and leverage-based tests to identify anomalous observations. Decisions made here directly affect model accuracy and regulatory defensibility.

Lesson 1 • Correlation and Association Measures

Quantify linear and rank-based relationships between analytical variables using Pearson and Spearman coefficients. Prepares students for regression and multivariate modeling.

Lesson 2 • Parametric Comparison Tests

Execute t-tests and F-tests to compare means and variances between analytical methods. Directly supports method comparison and equivalence studies.

Lesson 3 • Fundamentals of Hypothesis Testing

Define null and alternative hypotheses, Type I/II errors, and p-values in lab contexts. Establishes the decision framework used in all comparative analyses.

Lesson 4 • Analysis of Variance

Partition variance across multiple groups using one-way and two-way ANOVA designs. Enables simultaneous comparison of multiple instruments, operators, or batches.

Lesson 1 • Simple Linear Regression for Calibration

Fit ordinary least-squares lines to calibration data and extract slope, intercept, and R². Forms the quantitative core of all instrument calibration workflows.

Lesson 2 • Multiple Linear Regression

Extend calibration to multiple predictor variables and diagnose multicollinearity and leverage. Bridges univariate calibration to multivariate chemometric methods.

Lesson 3 • Limits of Detection and Quantitation

Calculate LOD and LOQ from calibration noise and slope using signal-to-noise and regression approaches. Critical outputs for method validation documentation.

Lesson 4 • Calibration Validation and Traceability

Verify calibration performance through recovery, linearity, and ruggedness tests aligned with regulatory expectations. Ensures models meet fitness-for-purpose criteria.

Lesson 1 • PCA Applications in Laboratory Settings

Apply PCA to spectral fingerprinting, batch monitoring, and raw material classification tasks. Demonstrates practical value before students advance to supervised methods.

Lesson 2 • Mathematical Foundations of PCA

Derive principal components from covariance matrices and singular value decomposition. Provides the conceptual basis for all latent-variable methods in the course.

Lesson 3 • Selecting the Number of Components

Apply cross-validation, scree plots, and explained variance criteria to choose optimal component count. Prevents underfitting and overfitting in downstream models.

Lesson 4 • Scores, Loadings, and Biplots

Interpret score plots for sample clustering and loading plots for variable contributions. These visualizations are the primary outputs of every PCA application.

Lesson 1 • Model Transfer and Standardization

Transfer calibration models between instruments using slope-bias correction and piecewise direct standardization. Enables multi-instrument deployment without full recalibration.

Lesson 2 • Model Validation Strategies

Validate multivariate models using cross-validation, test sets, and external prediction sets. Rigorous validation is mandatory for regulatory and publication acceptance.

Lesson 3 • Partial Least Squares Regression

Decompose X and Y simultaneously using PLS to maximize covariance with the response. PLS is the dominant multivariate calibration method in analytical chemistry.

Lesson 4 • Principal Component Regression

Regress reference values onto PCA scores to build PCR calibration models. Connects PCA knowledge directly to quantitative prediction tasks.

Lesson 1 • SIMCA and Soft Modeling

Build class-specific PCA models and assign samples using SIMCA distance thresholds. Enables one-class and multi-class authentication without rigid decision boundaries.

Lesson 2 • Linear Discriminant Analysis

Maximize between-class separation relative to within-class variance using LDA. A foundational supervised classifier widely used in food and pharmaceutical authentication.

Lesson 3 • k-Nearest Neighbors and Naive Bayes

Apply instance-based and probabilistic classifiers as alternatives to linear methods. Broadens the classifier toolkit for nonlinear and high-dimensional lab data.

Lesson 4 • Classifier Validation and Performance Reporting

Evaluate classifiers using confusion matrices, ROC curves, and cross-validated error rates. Ensures reported performance is unbiased and meets regulatory expectations.

Lesson 1 • Principles of Experimental Design

Define factors, responses, and design objectives to replace one-at-a-time experimentation. Establishes the strategic mindset for all optimization work in the chapter.

Lesson 2 • Response Surface Methodology

Fit second-order polynomial models to map response surfaces and locate optima. Enables fine optimization of analytical conditions after screening designs.

Lesson 3 • Robustness Testing and Ruggedness Studies

Use Plackett-Burman and Youden designs to identify method-critical factors during validation. Directly supports regulatory submission requirements for method robustness.

Lesson 4 • Mixture Designs for Formulation Studies

Apply simplex-lattice and simplex-centroid designs where component proportions sum to a constant. Addresses formulation optimization common in pharmaceutical and food labs.