Optimizing Signal Acquisition and Chemometric Pipelines for Micro NIR Plant Identification: Evaluating Spectral Backgrounds and Data Processing in Herbarium Specimens

Premise: Rapid and accurate plant species identification is a critical challenge exacerbated by the taxonomic impediment. Although portable near-infrared (Micro NIR) spectroscopy represents a promising solution, the current absence of standardized protocols and a fundamental understanding of how critical acquisition and analysis parameters influence accuracy remain significant barriers. This study focused on the systematic optimization and validation of a comprehensive workflow designed to maximize the reliability of plant identification using this technology. To ensure methodological robustness across diverse foliar matrices, four vascular plant species were strategically selected as a representative test set to encompass morphological extremes, including significant variations in leaf thickness, pubescence, and surface texture. Methods: Using a portable spectrometer on herbarium specimens (exsiccate) of four vascular plant species, we systematically tested five spectral backgrounds, seven pre-processing methods, and four classification models. Subsequently, we optimized the number of spectral readings and evaluated the influence of the leaf scanning surface (adaxial vs. abaxial) on model accuracy. Results: The highest-performing combination was a Shiny Aluminum background, Second Derivative pre-processing, and a Random Forest model, which achieved a mean cross-validated accuracy of 99%. An average of just three spectral readings from the adaxial (upper) leaf face was sufficient to saturate model performance, proving statistically superior to other approaches (p < 0.001). Discussion: This study establishes a validated, high-accuracy protocol for plant species identification from herbarium specimens using portable NIR, offering a powerful tool for biodiversity studies. Direct applicability to fresh plants in the field requires future validation to account for the spectral influence of moisture variability.