2results about How to "Short detection process" patented technology

This invention discloses a method for detecting the alumina content in aluminum-manganese-calcium alloys, mainly solving the technical problem that existing methods cannot accurately and rapidly detect the alumina content in aluminum-manganese-calcium alloys. The technical solution is as follows: a method for detecting the alumina content in aluminum-manganese-calcium alloys, comprising: 1) sample preparation: after removing the oxide layer on the sample surface, the sample is placed in a reagent bottle containing organic solvent for storage; 2) alumina extraction from the sample: first, alumina is extracted from the sample by electrolysis, the sample is placed in an electrolytic cell for electrolysis, the sample is the anode, a platinum electrode is the cathode, and a saturated calomel electrode is the reference electrode; 3) detection of alumina in the sample: the filter membrane and filter material from step 2) are placed in a platinum crucible, and the filter membrane is removed by high-temperature ashing; 4) calculation of the mass percentage of alumina in the aluminum-manganese-calcium alloy. The method of this invention for determining the alumina content in aluminum-manganese-calcium alloys has a relative standard deviation (RSD) of less than 3%, indicating good precision, accuracy, and reliability of the detection data.

This invention belongs to the interdisciplinary field of biomedical engineering and artificial intelligence-assisted diagnosis. It discloses a training method for a SERS spectral classification prediction model and its application. The disclosed SERS spectral classification prediction model training method combines CatBoost feature selection and deep learning. First, surface-enhanced Raman spectroscopy (SERS) data of serum from different categories of subjects is collected. After baseline removal, filtering, and normalization preprocessing, CatBoost gradient boosting algorithm is used to evaluate feature importance, considering the high-dimensional redundancy of the spectral data, and to select a subset of discrete feature bands containing key biomarker information. This feature subset is then input into a one-dimensional convolutional neural network model for training. The model constructed by this invention possesses deep feature mining capabilities, and the biological interpretability of the decisions is verified through SHAP analysis. It effectively solves the problems of large spectral noise interference and difficulty in extracting weak pathological features in traditional methods. For example, it can be used for the auxiliary diagnosis of coronary heart disease, achieving non-invasive, rapid, and high-precision classification and diagnosis of coronary heart disease and its subtypes.