Analytical Chemistry

Code Examples

import numpy as np
from typing import List, Dict, Tuple
from scipy import stats

class AnalyticalMethod:
    def __init__(self, name: str, detection_limit: float = 0.0):
        self.name = name
        self.detection_limit = detection_limit
        self.calibration_data = []

    def linear_calibration(self, concentrations: List[float], 
                          responses: List[float]) -> Dict:
        slope, intercept, r_value, p_value, std_err = stats.linregress(
            concentrations, responses
        )
        self.calibration_data = list(zip(concentrations, responses))
        return {
            'slope': slope,
            'intercept': intercept,
            'r_squared': r_value**2,
            'standard_error': std_err
        }

    def calculate_unknown(self, response: float, calibration: Dict) -> float:
        return (response - calibration['intercept']) / calibration['slope']

    def detection_limit_3sigma(self, blank_responses: List[float]) -> float:
        blank_mean = np.mean(blank_responses)
        blank_std = np.std(blank_responses)
        return blank_mean + 3 * blank_std

    def standard_addition(self, original_response: float,
                         added_concentrations: List[float],
                         spiked_responses: List[float]) -> float:
        slope, intercept, _, _, _ = stats.linregress(
            added_concentrations, spiked_responses
        )
        return -intercept / slope

    def precision_check(self, measurements: List[float]) -> Dict:
        return {
            'mean': np.mean(measurements),
            'std_dev': np.std(measurements),
            'relative_std_dev': np.std(measurements) / np.mean(measurements) * 100,
            'confidence_interval_95': stats.t.interval(
                0.95, len(measurements)-1, 
                loc=np.mean(measurements),
                scale=stats.sem(measurements)
            )
        }

method = AnalyticalMethod("HPLC Quantification")
cal = method.linear_calibration(
    [0, 10, 20, 30, 40],
    [0.02, 0.45, 0.89, 1.32, 1.78]
)
unknown = method.calculate_unknown(0.65, cal)
print(f"R²: {cal['r_squared']:.4f}")
print(f"Unknown concentration: {unknown:.2f}")

Best Practices

1. Always include proper blanks and controls in analytical runs
2. Perform calibration curves with sufficient data points (minimum 5)
3. Validate methods with certified reference materials when available
4. Report uncertainty alongside analytical results
5. Use appropriate sample preparation to avoid matrix effects
6. Perform replicate measurements for statistical reliability
7. Check linearity and dynamic range before quantitative analysis
8. Document all procedures for reproducibility and audit trails
9. Use appropriate sig figs and significant figures in reporting
10. Implement proper QA/QC protocols including proficiency testing