FT-NIR combined with multiple intelligent algorithms for rapid identification and quantitative analysis of Iron Mineral Decoction Pieces

Calcined and Vinegar-quenched Magnetite (CVQM), Calcined and Vinegar-quenched Hematite (CVQH), Calcined and Vinegar-quenched Pyrite (CVQP), Calcined and Vinegar-quenched Limonite (CVQL) are all iron-containing mineral decoction pieces, which are easily be confused because of their similar primary compositions and appearances. However, their medicinal values differ significantly, misuse in clinical settings could pose substantial safety risks to patients. In this study, E-eye and Fourier transform near infrared (FT-NIR) combined with multivariate algorithms were employed for the qualitative identification and quantitative prediction of iron content in these four kinds of mineral decoction pieces. The results indicated that the PCA model alongside machine learning classification models with E-eye was ineffective for distinguishing among the four types of decoction pieces, achieving an accuracy rate below 80 %. Furthermore, by utilizing FT-NIR technology with SNV + ICO optimization on raw spectra, we achieved machine-learning classification model accuracies around 90 %, which were improved by 28 %–36 % compared to analyses based solely on raw spectra. Additionally, the quantitative prediction regression (PLSR) model for predicting iron content demonstrated R²_C = 0.9627 and R²_P = 0.9451, indicating strong linearity and predictive accuracy of the model. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided an effective approach for identifying and evaluating the quality of mineral medicines with similar appearances and compositions.