Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm

Area of Science:

• Analytical Chemistry
• Biophysics

Background:

• Accurate discrimination between human and nonhuman blood is crucial for forensic science and customs inspections.
• Existing methods may face challenges with genetic disclosure or require complex analysis.

Purpose of the Study:

• To develop and validate a novel self-reference algorithm for discriminating human and nonhuman blood using Raman spectroscopy.
• To identify stable reference and specific identification Raman peaks for reliable blood analysis.

Main Methods:

• Utilizing Raman spectroscopy to analyze blood samples.
• Calculating ratios of identification Raman peaks to reference Raman peaks.
• Employing a self-reference algorithm with optimized threshold values.
• Comparing the algorithm's performance against the partial least square method.

Main Results:

• A Raman peak at 1003 cm⁻¹ was identified as a stable reference peak, unaffected by laser intensity or sample amount.
• A Raman peak at 1341 cm⁻¹ was identified as an efficient indicator, linked to the C-H bend in tryptophan, differentiating blood types.
• The self-reference algorithm achieved 100% true positive rate, matching the accuracy of the partial least square method while providing chemical composition insights.

Conclusions:

• The self-reference algorithm provides a robust and accurate method for human and nonhuman blood discrimination.
• This technique is valuable for customs inspection, preventing genetic disclosure, and advancing forensic science.
• The algorithm's ability to reveal chemical differences enhances its utility beyond simple identification.