Automated Mass Spectrometry Data Normalization Pipeline

Design a robust Python script using pandas and numpy that can automatically preprocess and normalize raw mass spectrometry data from multiple instrument formats. The solution must handle variable column structures, detect and remove outliers using statistical methods, apply multiple normalization techniques (z-score, min-max, robust scaling), and generate a comprehensive validation report with visualization using seaborn. Include error handling for different data inconsistencies and support batch processing of multiple CSV/TSV files from different lab instruments.