Detection of Heavy Metal Copper Stress in Apple Rootstocks Using Surface-Enhanced Raman Spectroscopy

Abstract

Excessive use of copper (Cu) chemicals has led to soil contamination. This study utilized surface-enhanced Raman spectroscopy (SERS) to investigate the effects of 10 commonly encountered concentrations of Cu stress in orchards on apple rootstocks. Spectral preprocessing methods were employed to eliminate baseline drift and fluorescence background interference from the Raman spectra, while data augmentation techniques were incorporated to develop a one-dimensional stacked autoencoder convolutional neural network (1D-SAE-CNN) for classifying Cu stress levels, resulting in evaluation indices greater than 0.9. Scanning electron microscopy with energy dispersive spectroscopy (SEM–EDS) quantified Cu distribution in root, stem, and leaf tissues, while micro-Raman imaging visualized lignin, cellulose, and pigments under Cu stress. The results indicate that SERS combined with a deep learning model enables rapid and accurate differentiation of Cu stress levels in apple rootstocks in orchards, while SEM–EDS and micro-Raman imaging techniques reveal the migration effect of Cu²⁺ within apple rootstock tissues and the ″low concentration promotion, high concentration inhibition″ effect of Cu on apple rootstock growth. Therefore, this approach showcases rapid and accurate detection of heavy metal Cu stress in apple rootstock tissues and has great potential for analyzing various types of heavy metal pollution in agricultural orchard ecosystems.