Evaluating impact of milling degree on rice origin identification by laser-induced breakdown spectroscopy

<bold>Objective</bold> Rice is milled to improve its sensory quality.However，this process can mask the intrinsic characteristics indicative of the rice’s geographical origin，increasing the difficulty to identfiy the authentic provenance of the product.Laser-Induced Breakdown Spectroscopy （LIBS）is an all-optical method for sensing the elemental composition of rice，which enables rapid and green identification of rice origin.Based on the collection of LIBS spectra of rice samples with different degrees of milling，the effects of milling degree on the accuracy of identifying the origin of rice by LIBS were analyzed by combining machine learning algorithms such as Random Forest（RF）and Logistic Regression（LR）. <bold>Method</bold> Rice samples from 11 different origins in Jiangxi Province were subjected to four distinct milling levels，that is，slight，mild，moderate，and severe，to prepare samples for LIBS analysis.The work compared the LIBS spectra of rice samples across these milling levels and evaluated the origin identification accuracy of RF and LR models using both full spectral data and data post-feature extraction via Extreme Gradient Boosting（XGBoost）. <bold>Result</bold> The results showed that increasing milling degrees altered the internal mineral element information of rice，reducing the detectable LIBS information and consequently affecting origin identification accuracy.When the full spectrum data was input，the average identification accuracy rate of the LR model was 89.97%，while after feature extraction by XGBoost，the average identification accuracy rate was increased to 94.85%.Similarly，the RF model’s accuracy increased from 85.11% to 94.31%.Notably，XGBoost feature extraction significantly enhanced model performance.Post spectral feature selection，the RF model achieved identification accuracies of 97.64%，93.88%，93.15%，and 92.55% for slight，mild，moderate，and severe milling，respectively.The LR model demonstrated even better performance with accuracies of 97.76%，94.79%，94.48%，and 92.36%，respectively，outperforming the RF model. <bold>Conclusion</bold> This work illustrates that the LIBS method combined with machine learning algorithms has the potential to identify the origin of rice at different levels of milling，and the lower the level of milling，the higher the accuracy of identification.