Raman Spectroscopy Analysis Application

Getting Started

  • Getting Started
    • System Requirements
      • Minimum Requirements
      • Recommended Requirements
    • Installation Options
      • 1. Source Installation (Recommended for Developers)
      • 2. Portable Executable (Windows Only)
      • 3. Installer (Windows Only)
    • Quick Start Tutorial
      • Step 1: Launch the Application
      • Step 2: Create Your First Project
      • Step 3: Import Data
      • Step 4: Preprocess Your Data
      • Step 5: Analyze Your Data
      • Step 6: Machine Learning (Optional)
    • Next Steps
    • Getting Help
      • Documentation Resources
      • Community Support
    • Video Tutorials (Coming Soon)
    • Feedback
  • Installation Guide
    • Prerequisites
      • Python Version
      • System Dependencies
    • Installation Methods
      • Method 1: From Source
        • Step 1: Clone the Repository
        • Step 2: Create Virtual Environment
        • Step 3: Verify Installation
        • Step 4: Run the Application
        • Updating from Source
      • Method 2: Portable Executable (Windows Only)
        • Step 1: Download
        • Step 2: Extract
        • Step 3: Run
        • Features
        • Limitations
      • Method 3: Installer (Windows Only)
        • Step 1: Download
        • Step 2: Run Installer
        • Step 3: Launch
        • Features
        • Uninstallation
    • Post-Installation
      • Verify Installation
      • Optional: Install Development Tools
      • Configure Application Settings
    • Troubleshooting Installation
      • Common Issues
        • Python Version Mismatch
        • Module Not Found Errors
        • Permission Denied (Linux/macOS)
        • UV Installation Fails
        • Windows Executable Blocked
      • Getting Help
    • Next Steps
    • Advanced Installation
      • Installing Specific Versions
      • Installing with Optional Dependencies
      • Building from Source (Advanced)
  • Quick Start
    • Prerequisites
    • Tutorial: Analyzing Blood Plasma Samples
      • Step 1: Launch and Create Project (2 minutes)
      • Step 2: Import Data (3 minutes)
      • Step 3: Preprocess Data (5 minutes)
      • Step 4: Exploratory Analysis with PCA (3 minutes)
      • Step 5: Statistical Testing (2 minutes)
    • Optional: Machine Learning Classification
      • Step 6: Train ML Model (Optional, +10 minutes)
    • Next Steps
      • Learn More About Methods
      • Advanced Workflows
      • Best Practices
    • Common Issues
      • Data Import Problems
      • Preprocessing Errors
      • Analysis Issues
    • Getting Help
    • Feedback

User Guide

  • User Guide
    • Guide Structure
      • 1. Interface Overview
      • 2. Data Import and Management
      • 3. Preprocessing Pipeline
      • 4. Analysis Methods
      • 5. Machine Learning
      • 6. Best Practices
    • Quick Navigation
      • By Task
      • By Research Goal
    • Typical Workflows
      • Workflow 1: Quality Control and Exploratory Analysis
      • Workflow 2: Classification Model Development
      • Workflow 3: Spectral Unmixing
    • Common Questions
      • When Should I Use Each Analysis Method?
      • What Preprocessing Should I Use?
      • How Do I Ensure Valid Results?
    • Getting Help
      • Documentation Resources
      • Community Support
    • Video Tutorials (Coming Soon)
    • Contributing to This Guide
  • Interface Overview
    • Table of Contents
    • Main Window Layout
      • Window Structure
      • Key Components
    • Navigation System
      • Tab-Based Navigation
        • Home Page
        • Data Package Page
        • Preprocess Page
        • Exploratory Analysis Page
        • Modeling & Classification Page
        • Workspace Page
    • Common UI Elements
      • Panels and Widgets
        • Data Selector Panel
        • Parameter Panel
        • Results Panel
        • Toast Notifications
      • Dialog Windows
        • Multi-Group Selection Dialog
        • External Evaluation Dialog
    • Customization
      • Theme Selection
      • Language Selection
      • Layout Customization
        • Split-View Mode
        • Compact Mode
      • Font Settings
      • Panel Visibility
      • Default Directories
    • Workflow Integration
      • Typical User Workflow
      • Context Preservation
    • Accessibility Features
      • High Contrast Mode
      • Screen Reader Support
      • Keyboard-Only Navigation
    • Tips and Best Practices
      • Performance Optimization
      • Multi-Monitor Setup
      • Quick Actions
    • Troubleshooting UI Issues
      • Interface Not Responding
      • Missing Panels
      • Font Rendering Issues
      • Theme Not Applying
    • See Also
  • Data Import Guide
    • Table of Contents
    • Supported File Formats
      • Primary Formats
        • CSV Files (Recommended)
        • TXT Files (Text Format)
        • ASC/ASCII Files
        • PKL Files
      • Future Import Support (Planned)
    • Import Workflow
      • Step 1: Navigate to Data Package Page
      • Step 2: Select Files for Import
      • Step 3: Data Validation
      • Step 4: Preview and Confirm
      • Step 5: Confirmation
    • Data Organization
      • Project Structure
      • Creating Data Packages
      • Metadata Management
    • Group Management
      • Creating Sample Groups
      • Assigning Samples to Groups
      • Multi-Group Assignment
    • Data Validation
      • Automatic Checks
        • 1. Wavenumber Consistency
        • 2. Missing Values
        • 3. Outlier Detection
        • 4. Duplicate Spectra
      • Manual Validation Tools
        • Spectrum Viewer
        • Batch Validation
    • Advanced Features
      • Wavenumber Calibration
      • Data Merging
      • Data Splitting
      • Export Data
      • Batch Import
    • Best Practices
      • File Organization
      • Naming Conventions
      • Quality Control
    • Troubleshooting
      • Import Fails
      • Wavenumber Mismatch
      • Memory Issues
      • Missing Groups
    • See Also
  • Preprocessing Guide
    • Table of Contents
    • Overview
      • Why Preprocess?
      • Preprocessing Philosophy
    • Pipeline Builder Interface
      • Main Layout
      • Components
    • Method Categories
      • 1. Baseline Correction
        • AsLS (Asymmetric Least Squares)
        • AirPLS (Adaptive Iteratively Reweighted Penalized Least Squares)
        • Polynomial Baseline
        • FABC (Fully Automatic Baseline Correction)
      • 2. Smoothing and Denoising
        • Savitzky-Golay Filter
        • Gaussian Smoothing
        • Moving Average
        • Median Filter
      • 3. Normalization
        • Vector Normalization (L2 Norm)
        • Min-Max Normalization
        • Area Normalization
        • SNV (Standard Normal Variate)
        • MSC (Multiplicative Scatter Correction)
      • 4. Derivatives
        • First Derivative (Savitzky-Golay)
        • Second Derivative
      • 5. Advanced Methods
        • Convolutional Denoising Autoencoder (CDAE)
        • Wavelet Transform
        • Peak Ratio Feature Engineering
    • Building a Pipeline
      • Step-by-Step Guide
        • Step 1: Start with Raw Data
        • Step 2: Add Baseline Correction
        • Step 3: Add Smoothing (Optional)
        • Step 4: Add Normalization
        • Step 5: Validate Pipeline
        • Step 6: Save and Apply
    • Common Pipelines
      • 1. Standard Pipeline (General Purpose)
      • 2. Minimal Pipeline (Low Noise Data)
      • 3. Aggressive Denoising (High Noise Data)
      • 4. Derivative-Based Pipeline
      • 5. Chemometric Pipeline (Quantitative Analysis)
      • 6. Deep Learning Preprocessing
    • Advanced Tips
      • Parameter Optimization
      • Method Order Guidelines
      • Computational Efficiency
      • Validation Strategy
    • Troubleshooting
      • Problem: Peaks Disappear After Preprocessing
      • Problem: Baseline Still Present
      • Problem: Spectra Look Too Smooth
      • Problem: Pipeline Slow to Apply
      • Problem: Inconsistent Results
    • See Also
  • Analysis Guide
    • Table of Contents
    • Overview
      • Analysis Workflow
      • Exploratory Analysis Page Interface
    • Exploratory Analysis
      • Principal Component Analysis (PCA)
        • Running PCA
        • Results
        • Interpretation Tips
      • UMAP (Uniform Manifold Approximation and Projection)
        • Running UMAP
        • UMAP vs PCA
      • t-SNE (t-Distributed Stochastic Neighbor Embedding)
      • Clustering Analysis
        • Hierarchical Clustering
        • K-Means Clustering
    • Statistical Analysis
      • Pairwise Group Comparisons
        • t-Test (Parametric)
        • Mann-Whitney U Test (Non-Parametric)
      • Multi-Group Comparisons
        • ANOVA (Analysis of Variance)
      • Correlation Analysis
        • Pearson Correlation
        • Spearman Correlation
      • Band Ratio Analysis
    • Visualization Methods
      • Interactive Heatmap
      • Waterfall Plot
      • Overlaid Spectra
      • Peak Scatter Plot
      • Correlation Matrix
    • Results Interpretation
      • Statistical Significance
      • Effect Size
      • Biological Interpretation
    • Export and Reporting
      • Export Options
      • Creating Reports
      • Publication-Ready Figures
    • Troubleshooting
      • No Group Separation in PCA
      • Statistical Tests Show No Significance
      • Analysis Takes Too Long
    • See Also
  • Machine Learning Guide
    • Table of Contents
    • Overview
      • Modeling & Classification Page
      • When to Use ML
    • ML Workflow
      • Complete Workflow
      • Step-by-Step Guide
        • Step 1: Prepare Data
        • Step 2: Split Data
        • Step 3: Select Training Data
        • Step 4: Choose Algorithm
        • Step 5: Configure Validation
        • Step 6: Set Hyperparameters
        • Step 7: Train Model
        • Step 8: Evaluate on Test Set
    • Algorithm Selection
      • Support Vector Machine (SVM)
      • Random Forest (RF)
      • XGBoost (Extreme Gradient Boosting)
      • Logistic Regression (LR)
      • Algorithm Comparison
    • Training and Validation
      • Validation Strategies
        • GroupKFold (Recommended)
        • Stratified K-Fold
        • Leave-One-Patient-Out (LOPOCV)
        • Hold-out Test Set
      • Hyperparameter Optimization
        • Grid Search
        • Random Search
        • Bayesian Optimization
      • Preventing Overfitting
    • Model Evaluation
      • Classification Metrics
        • Accuracy
        • Precision, Recall, F1-Score
        • ROC Curve and AUC
        • Confusion Matrix
      • Multi-Class Metrics
      • Regression Metrics
        • MAE (Mean Absolute Error)
        • RMSE (Root Mean Squared Error)
        • R² (Coefficient of Determination)
    • Model Interpretation
      • Feature Importance
        • Random Forest Feature Importance
        • SHAP Values (SHapley Additive exPlanations)
        • Permutation Importance
      • Model Transparency
    • Model Export and Deployment
      • Saving Trained Models
      • Loading Models for Prediction
      • Model Deployment Checklist
    • Troubleshooting
      • Poor Performance (Accuracy <70%)
      • Overfitting (Train >> Test)
      • Class Imbalance
      • Data Leakage
    • See Also
  • Best Practices
    • Table of Contents
    • Data Quality
      • Sample Preparation
      • Data Acquisition
      • Sample Size Determination
      • Data Organization
    • Preprocessing Strategy
      • Choosing Methods
      • Validation
      • Documentation
    • Statistical Analysis
      • Test Selection
      • Multiple Testing Correction
      • Effect Sizes
    • Machine Learning
      • Data Splitting
      • Cross-Validation Strategy
      • Feature Selection
      • Model Selection
      • Validation Requirements
    • Reproducibility
      • Code and Environment
      • Documentation
    • Publication and Reporting
      • Figures
      • Tables
      • Methods Section
      • Results Section
      • Discussion
      • Ethical Considerations
    • Checklists
      • Before Analysis
      • Before Preprocessing
      • Before Statistical Analysis
      • Before Machine Learning
      • Before Publication
    • Common Pitfalls to Avoid
      • ❌ Data Leakage
      • ❌ Spectrum-Level Splitting
      • ❌ Peeking at Test Set
      • ❌ Multiple Testing Without Correction
      • ❌ Cherry-Picking Results
      • ❌ Overfitting
      • ❌ Missing Documentation
    • Resources
      • Recommended Reading
      • Software Citations
    • See Also

Analysis Methods

  • Analysis Methods Reference
    • Purpose of This Reference
    • Method Categories
      • Preprocessing Methods
      • Exploratory Analysis
      • Statistical Methods
      • Machine Learning
    • Quick Method Selector
      • By Research Question
      • By Data Characteristics
    • Method Selection Flowchart
    • Preprocessing Guidelines
      • Recommended Pipeline for Raman Data
    • Validation Best Practices
      • Critical Rules
    • Parameter Selection Guides
      • Parameter Tables
      • Visual Parameter Guides
      • Decision Trees
    • Interpretation Guides
      • Example Results
      • What to Report
      • Common Misinterpretations
    • Citations and References
      • Using This Documentation in Publications
      • Bibliography
    • Contributing Method Documentation
    • Support
  • Preprocessing Methods Reference
    • Table of Contents
    • Baseline Correction
      • AsLS (Asymmetric Least Squares)
      • AirPLS (Adaptive Iteratively Reweighted Penalized Least Squares)
      • Polynomial Baseline
      • Whittaker Smoothing
      • FABC (Fully Automatic Baseline Correction)
      • Butterworth High-Pass Filter
    • Smoothing and Denoising
      • Savitzky-Golay Filter
      • Gaussian Smoothing
      • Moving Average
      • Median Filter
      • Kernel Denoising
    • Normalization
      • Vector Normalization (L2 Norm)
      • Min-Max Normalization
      • Area Normalization
      • Standard Normal Variate (SNV)
      • Multiplicative Scatter Correction (MSC)
      • Quantile Normalization
      • Probabilistic Quotient Normalization (PQN)
      • Rank Transformation
    • Derivatives
      • First Derivative (Savitzky-Golay)
      • Second Derivative
    • Feature Engineering
      • Peak Ratio
      • Wavelet Transform
    • Advanced Methods
      • Convolutional Denoising Autoencoder (CDAE)
      • Background Subtraction
      • Calibration
    • Method Selection Guide
      • Decision Matrix
      • Common Pipelines
    • Parameter Constraints
      • Automatic Validation
    • Best Practices
      • General Guidelines
      • Method-Specific Tips
    • Troubleshooting
      • Common Issues
    • References
    • See Also
  • Exploratory Analysis Methods
    • Table of Contents
    • Principal Component Analysis (PCA)
      • Theory
      • Parameters
      • Usage Example
      • Output Components
      • Interpretation
        • Scores Plot
        • Loadings Plot
        • Explained Variance
      • Common Use Cases
        • 1. Group Visualization
        • 2. Outlier Detection
        • 3. Feature Selection
        • 4. Dimensionality Reduction for ML
      • Troubleshooting
      • Assumptions
      • When to Use
      • Advanced Options
        • Whitening
        • Incremental PCA
      • Reference
    • MCR-ALS
    • UMAP (Uniform Manifold Approximation and Projection)
      • Theory
      • Parameters
      • Usage Example
      • PCA vs UMAP Comparison
      • Interpretation
      • Troubleshooting
      • When to Use
      • Reference
    • t-SNE (t-Distributed Stochastic Neighbor Embedding)
      • Theory
      • Parameters
      • Usage Example
      • Interpretation
      • UMAP vs t-SNE
      • Troubleshooting
      • When to Use
      • Reference
    • Hierarchical Clustering
      • Theory
      • Parameters
      • Usage Example
      • Dendrogram Interpretation
      • Visualization
      • Choosing Number of Clusters
      • Troubleshooting
      • When to Use
      • Reference
    • K-Means Clustering
      • Theory
      • Parameters
      • Usage Example
      • Choosing Number of Clusters (K)
        • Method 1: Elbow Method
        • Method 2: Silhouette Score
        • Method 3: Gap Statistic
      • Cluster Validation
        • Silhouette Analysis
      • Troubleshooting
      • Assumptions and Limitations
      • When to Use
      • Reference
    • DBSCAN (Density-Based Spatial Clustering)
      • Theory
      • Parameters
      • Usage Example
      • Advantages
      • Disadvantages
      • Troubleshooting
      • When to Use
      • Reference
    • Method Comparison
      • Quick Reference Table
      • Decision Tree
      • Typical Workflow
    • Validation Metrics
      • Silhouette Score
      • Davies-Bouldin Index
      • Calinski-Harabasz Index
    • Best Practices
      • General Guidelines
      • Reproducibility
    • See Also
  • Statistical Analysis Methods
    • Table of Contents
    • T-Tests
      • Types of T-Tests
        • 1. Independent Samples T-Test
        • 2. Paired Samples T-Test
        • 3. One-Sample T-Test
      • Interpretation
      • Troubleshooting
      • When to Use
    • Mann-Whitney U Test
      • Theory
      • Assumptions
      • Usage Example
      • Interpretation
      • When to Use
    • ANOVA (Analysis of Variance)
      • One-Way ANOVA
      • Usage Example
      • Application Parameters
      • Grouped Mode vs Simple Mode
      • Post-Hoc Tests (Optional)
      • Checking Assumptions
      • Welch’s ANOVA
      • Interpretation
      • When to Use
    • Pairwise Statistical Tests
      • Available Tests
        • 1. Independent T-Test (t_test)
        • 2. Mann-Whitney U Test (mann_whitney)
        • 3. Wilcoxon Signed-Rank Test (wilcoxon)
      • Usage in Application
      • Interpretation
      • Best Practices
    • Correlation Analysis
      • Pearson Correlation
      • Usage Example
      • Spearman Correlation
      • Kendall’s Tau
      • Partial Correlation
      • Correlation Matrix
      • Troubleshooting
    • Multiple Testing Correction
      • Methods
        • 1. Bonferroni Correction
        • 2. Holm-Bonferroni Method
        • 3. False Discovery Rate (FDR) - Benjamini-Hochberg
        • 4. Permutation Testing
      • Comparison Table
      • Decision Guide
    • Effect Size Measures
      • Cohen’s d
      • Eta-Squared (η²)
      • Omega-Squared (ω²)
      • Reporting Effect Sizes
    • Band Ratio Analysis
      • Theory
      • Calculation
      • Integration Methods
      • Statistical Testing
      • Best Practices
    • Best Practices
      • General Workflow
      • Reporting Checklist
      • Common Mistakes to Avoid
    • See Also
  • Machine Learning Methods
    • Table of Contents
    • Support Vector Machines (SVM)
      • Theory
      • Kernel Functions
        • 1. Linear Kernel
        • 2. RBF (Radial Basis Function) Kernel
        • 3. Polynomial Kernel
      • Hyperparameters
        • C (Regularization Parameter)
        • Gamma (γ) - RBF Kernel Only
      • Usage Example
      • Hyperparameter Optimization
      • Interpretation
      • Troubleshooting
      • When to Use
      • Class Imbalance
      • Reference
    • Random Forest
      • Theory
      • Hyperparameters
        • n_estimators
        • max_depth
        • min_samples_split
        • min_samples_leaf
        • max_features
      • Usage Example
      • Hyperparameter Optimization
      • Feature Importance
        • SHAP Values
      • Out-of-Bag (OOB) Score
      • Advantages
      • Limitations
      • When to Use
      • Reference
    • XGBoost
      • Theory
      • Hyperparameters
        • n_estimators
        • learning_rate (eta)
        • max_depth
        • subsample
        • colsample_bytree
        • gamma (min_split_loss)
        • lambda (reg_lambda) - L2 Regularization
        • alpha (reg_alpha) - L1 Regularization
      • Usage Example
      • Hyperparameter Optimization
      • Feature Importance
      • Learning Curves
      • Advantages
      • Limitations
      • When to Use
      • Reference
    • Logistic Regression
      • Theory
      • Hyperparameters
        • C (Inverse Regularization)
        • penalty
        • solver
        • max_iter
      • Usage Example
      • Hyperparameter Optimization
      • Interpretation
      • Advantages
      • Limitations
      • When to Use
      • Reference
    • Multi-Layer Perceptron (MLP)
      • Theory
      • Hyperparameters
        • hidden_layer_sizes
        • activation
        • alpha
        • learning_rate_init
        • max_iter
        • early_stopping
      • Usage Example
      • Hyperparameter Optimization
      • Learning Curves
      • Advantages
      • Limitations
      • When to Use
      • Reference
    • Model Evaluation
      • Classification Metrics
        • Accuracy
        • Precision
        • Recall (Sensitivity)
        • F1-Score
        • ROC-AUC
        • Confusion Matrix
        • Classification Report
      • Cross-Validation
        • K-Fold Cross-Validation
        • Stratified K-Fold
        • Group K-Fold (Patient-Level)
    • See Also

API Documentation

  • API Documentation
    • Documentation Organization
      • Core Modules
      • Pages
      • Components
      • Functions
      • Widgets
    • Quick Reference
      • Key Classes
        • Application Core
        • Data Management
        • Preprocessing
        • Analysis
        • Machine Learning
      • Common Patterns
        • Adding a New Preprocessing Method
        • Adding a New Analysis Method
        • Creating Custom Widgets
    • Development Setup
      • Prerequisites
      • Running Tests
      • Building Documentation
      • Code Style
    • Architecture Overview
      • Module Dependencies
      • Data Flow
      • Signal/Slot Connections
    • Extension Points
      • Plugin System (Planned)
      • Integration APIs
        • REST API (Planned)
        • Command Line Interface (Planned)
    • Contributing
    • API Versioning
    • Deprecation Policy
    • Support
    • License
  • Core Application API
    • Table of Contents
    • Application Entry Points
      • main.py
        • MainApplication Class
      • dev_runner.py
        • DevRunner Class
    • Configuration System
      • configs/configs.py
        • AppConfig Class
      • configs/user_settings.py
        • UserSettings Class
    • Localization
      • Locale System
        • Translation Files
        • LocaleManager Class
    • Utilities
      • utils.py
        • File I/O Functions
        • Data Validation
        • Progress Tracking
    • Splash Screen
      • splash_screen.py
        • SplashScreen Class
    • Error Handling
      • Custom Exceptions
        • DataError
        • PreprocessingError
        • ModelError
      • Error Logging
    • Threading and Async Operations
      • WorkerThread Class
    • Performance Utilities
      • Caching
      • Profiling
    • Testing Utilities
      • Mock Data Generation
    • Best Practices
      • Configuration Management
      • Error Handling
      • Threading
    • See Also
  • Pages API
    • Table of Contents
    • Page Architecture
      • BasePage Class
    • Home Page
      • pages/home_page.py
        • HomePage Class
    • Data Package Page
      • pages/data_package_page.py
        • DataPackagePage Class
    • Preprocessing Page
      • pages/preprocess_page.py
        • PreprocessPage Class
    • Exploratory Analysis Page
      • pages/exploratory_analysis_page.py
        • AnalysisPage Class
    • Modeling & Classification Page
      • pages/machine_learning_page.py
        • MachineLearningPage Class
    • Workspace Page
      • pages/workspace_page.py
        • WorkspacePage Class
    • Best Practices
      • Data Flow Between Pages
      • Error Handling in Pages
      • Progress Reporting
    • See Also
  • Components API
    • Table of Contents
    • Component Architecture
      • Component Hierarchy
    • App Tabs
      • components/app_tabs.py
        • AppTabs Class
    • Page Registry
      • components/page_registry.py
        • PageRegistry Class
    • Toast Notifications
      • components/toast.py
        • Toast Class
    • Spectrum Viewer
      • components/widgets/matplotlib_widget.py
        • SpectrumViewer Class
    • Data Table
      • components/widgets/views_widget.py
        • DataTable Class
    • Parameter Panel
      • components/widgets/parameter_widgets.py
        • ParameterPanel Class
    • Pipeline Builder
      • components/widgets/component_selector_panel.py
        • PipelineBuilder Class
    • Results Panel
      • components/widgets/results_panel.py
        • ResultsPanel Class
    • Multi-Group Dialog
      • components/widgets/multi_group_dialog.py
        • MultiGroupDialog Class
    • External Evaluation Dialog
      • components/widgets/external_evaluation_dialog.py
        • ExternalEvaluationDialog Class
    • Component Communication
      • Signal-Slot Patterns
      • Event Bus Pattern
    • Best Practices
      • Component Reusability
      • State Management
      • Error Handling in Components
    • See Also
  • Functions API
    • Table of Contents
    • Data Loading Functions
      • functions/data_loader.py
        • load_spectra_from_csv()
        • save_spectra_to_csv()
        • load_raman_peaks()
    • Preprocessing Functions
      • Baseline Correction
        • apply_asls()
        • apply_airpls()
        • apply_polynomial_baseline()
        • apply_whittaker_baseline()
        • apply_fabc()
        • apply_butterworth_filter()
      • Smoothing
        • apply_savgol()
        • apply_gaussian_filter()
        • apply_moving_average()
        • apply_median_filter()
        • apply_kernel_denoise()
      • Normalization
        • apply_vector_norm()
        • apply_minmax_norm()
        • apply_area_norm()
        • apply_snv()
        • apply_msc()
        • apply_quantile_norm()
        • apply_pqn()
        • apply_rank_transform()
      • Derivatives
        • apply_first_derivative()
        • apply_second_derivative()
      • Advanced Processing
        • apply_cdae()
        • apply_background_subtraction()
        • apply_wavelength_calibration()
        • apply_peak_ratio()
        • apply_wavelet_transform()
    • Analysis Functions
      • Dimensionality Reduction
        • apply_pca()
        • apply_umap()
        • apply_tsne()
      • Clustering
        • apply_kmeans()
        • apply_hierarchical_clustering()
        • apply_dbscan()
      • Statistical Tests
        • apply_ttest()
        • apply_mannwhitneyu()
        • apply_anova()
        • apply_kruskal()
        • apply_correlation()
        • apply_multiple_testing_correction()
    • Machine Learning Functions
      • Model Training
        • train_svm()
        • train_random_forest()
        • train_xgboost()
        • train_logistic_regression()
        • train_mlp()
      • Model Evaluation
        • evaluate_model()
        • plot_confusion_matrix()
        • plot_roc_curve()
        • plot_learning_curve()
      • Feature Importance
        • get_feature_importance()
        • plot_feature_importance()
        • calculate_permutation_importance()
    • Utility Functions
      • functions/utils.py
        • validate_spectra_data()
        • generate_mock_spectra()
        • calculate_snr()
        • find_peaks()
        • integrate_region()
        • resample_spectrum()
        • split_train_test()
        • export_pipeline()
        • load_pipeline()
    • Pipeline Execution
      • execute_preprocessing_pipeline()
    • Best Practices
      • Function Usage
      • Error Handling
      • Pipeline Design
      • Performance Optimization
    • See Also
  • Widgets API
    • Table of Contents
    • Widget Architecture
      • Base Widget Principles
      • Common Widget Pattern
    • Enhanced Parameter Widgets
      • components/widgets/enhanced_parameter_widgets.py
        • FloatParameterWidget
        • IntParameterWidget
        • BoolParameterWidget
        • ChoiceParameterWidget
        • StringParameterWidget
        • RangeParameterWidget
    • Constrained Parameter Widgets
      • components/widgets/constrained_parameter_widgets.py
        • ConstrainedFloatWidget
        • AutoValidatingComboBox
    • Icons and Resources
      • components/widgets/icons.py
        • get_icon()
        • get_themed_icon()
        • IconButton
    • Matplotlib Widget
      • components/widgets/matplotlib_widget.py
        • MatplotlibWidget
        • InteractivePlot
    • Component Selector Panel
      • components/widgets/component_selector_panel.py
        • ComponentSelectorPanel
    • Results Panel Details
      • components/widgets/results_panel.py
        • ResultsPanel
    • Views Widget
      • components/widgets/views_widget.py
        • SpectrumTableView
    • Grouping Widgets
      • components/widgets/grouping/
        • GroupManager
    • Custom Dialogs
      • Parameter Editor Dialog
      • Method Selection Dialog
    • Widget Styling
      • Theme Support
      • Widget-Specific Styling
    • Best Practices
      • Widget Design
      • Signal Management
      • State Management
      • Performance
    • See Also

Development

  • Development Guide
    • 📚 Guide Contents
      • Architecture
      • Contributing Guide
      • Build System
      • Testing Guide
    • 🚀 Quick Start for Developers
      • Prerequisites
      • Setup Development Environment
      • Running the Application
    • 📂 Project Structure
    • 🛠️ Development Tools
      • Code Quality
      • Testing
      • Documentation
      • Build Tools
    • 🔧 Common Development Tasks
      • Adding a New Preprocessing Method
      • Adding a New Page
      • Adding a New Widget
      • Updating Translations
    • 📖 Coding Standards
      • Python Style
      • Documentation
      • Example
    • 🤝 Getting Help
    • 📝 License
    • 🔗 See Also
  • Architecture
    • What to read first
  • Contributing Guide
    • Minimal contribution workflow (current)
  • Build System
    • Recommended approach
  • Testing Guide
    • Smoke tests (current)

Additional Resources

  • Changelog
    • [Unreleased]
      • Documentation
    • [1.0.0-alpha] - 2026-01-24
      • Added
        • Core Features
        • Preprocessing (40+ Methods)
        • Analysis Methods
        • Machine Learning
        • Build System
      • Fixed
        • January 2026
        • October 2025
      • Changed
      • Security
    • [0.1.0] - 2025-10-01
      • Added
    • Release Notes
      • Version 1.0.0-alpha
      • Migration Guides
        • From v0.1.0 to v1.0.0-alpha
    • Contributors
      • Core Development
      • Supervision
      • Acknowledgments
    • License
    • Citation
    • Support
  • Troubleshooting Guide
    • Quick Diagnostic Steps
    • Installation Issues
      • Python Version Error
      • Module Not Found
      • UV Installation Fails
      • Permission Denied (Linux/macOS)
      • Windows SmartScreen Blocks Executable
    • Application Launch Issues
      • Application Doesn’t Start
      • Application Crashes on Startup
      • Black/Blank Window
    • Data Import Issues
      • File Not Recognized
      • Dimension Mismatch Error
      • Data Looks Wrong After Import
    • Preprocessing Issues
      • Preview Shows All Zeros
      • Baseline Correction Not Working
      • Smoothing Removes Peaks
      • Normalization Produces Strange Results
      • Pipeline Fails to Execute
    • Analysis Issues
      • PCA Shows No Group Separation
      • Statistical Tests Show No Significant Differences
      • Analysis Takes Forever
      • Plots Don’t Appear
    • Machine Learning Issues
      • Model Training Fails
      • 100% Training Accuracy, Poor Test Accuracy
      • Groups Imbalanced (90% vs 10%)
      • SHAP Values Take Forever
      • Can’t Export Trained Model
    • Performance Issues
      • Application Runs Slowly
      • High RAM Usage
      • Disk Space Issues
    • UI Issues
      • Text Too Small/Large
      • Japanese Text Shows as Boxes (□□□)
      • Buttons Not Responding
    • Getting More Help
      • Collect Diagnostic Information
      • Where to Get Help
      • Emergency: Application Completely Broken
    • Contributing to This Guide
  • Frequently Asked Questions (FAQ)
    • General Questions
      • What is this application for?
      • Who developed this software?
      • Is this software free?
      • Can I use this for clinical diagnosis?
      • What platforms are supported?
    • Installation Questions
      • Do I need Python installed?
      • Why is the executable so large (375 MB)?
      • How do I update to a new version?
      • Can I install on a computer without internet?
    • Data Questions
      • What file formats are supported?
      • What data structure is required?
      • My data has x-axis in nm, not cm⁻¹. What should I do?
      • Can I import multiple files at once?
      • How do I handle replicates?
    • Preprocessing Questions
      • What preprocessing should I use?
      • What is the difference between AsLS and AirPLS?
      • Should I normalize before or after baseline correction?
      • Can I save my preprocessing pipeline?
      • My preview shows all zeros after preprocessing. What’s wrong?
    • Analysis Questions
      • PCA shows no group separation. What should I do?
      • How many principal components should I use?
      • What is “multiple testing correction” and do I need it?
    • Machine Learning Questions
      • What algorithm should I choose?
      • How much data do I need?
      • My model has 100% accuracy. Is that good?
    • Export and Results Questions
      • How do I export results?
      • Can I get publication-quality figures?
      • How do I cite this software?
    • Language and Localization Questions
      • Can I use the interface in Japanese?
      • Some text is still in English after changing language. Why?
    • Still Have Questions?
      • Documentation
      • Community
      • Contributing
  • Glossary
    • A
    • B
    • C
    • D
    • E
    • F
    • G
    • H
    • I
    • K
    • L
    • M
    • N
    • O
    • P
    • Q
    • R
    • S
    • T
    • U
    • V
    • W
    • X
    • Japanese Terms / 日本語用語
    • Abbreviations
    • Contributing
  • References
    • Table of Contents
    • Raman Spectroscopy
      • Fundamentals
      • Medical Applications
    • Preprocessing Methods
      • Baseline Correction
      • Smoothing and Denoising
      • Normalization
      • Feature Engineering
    • Machine Learning
      • Dimensionality Reduction
      • Classification
      • Interpretability
      • Validation
    • Software Libraries
      • Core Dependencies
      • Specialized Libraries
    • Medical Applications
      • Blood Plasma Analysis
      • Pre-disease (未病) Detection
    • Standards and Guidelines
      • Best Practices
      • Quality Control
    • Related Resources
      • Online Resources
      • Educational Materials
    • Software Citation
    • Contributing
Raman Spectroscopy Analysis Application
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