Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

In order to realize the rapid nondestructive detection of mildew peanut in the process of peanuts storage, hyperspectral imaging technology was proposed to detect mildew peanut. A total of 200 peanuts were selected from 5 kinds of peanuts purchased in the market for moldy treatment, and the remaining 400 peanuts were kept sterile. After completion, samples were collected with a hyperspectral instrument to obtain spectral data of the samples. According to the characteristics of the data, 10 pre-processing algorithms were used to de-noise the data, and Median Filtering (MF) had the best effect, with the recognition accuracy reaching 97.7%. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to extract important feature bands in the spectral data pre-processed by MF. GBDT, LightGBM, CatBoost and XGBoost algorithms were used to model the extracted feature bands. The results showed that LightGBM is the best algorithm with a detection rate of 99.10%. Optuna algorithm was used to tune its parameters. Compared with the previous model, the running time of the optimized model was improved by about 0.25 s. The results showed that hyperspectral imaging provides an efficient and nondestructive method for detecting mildew in peanut storage.

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A comprehensive study of zircon U‐Pb geochronology, in situ Hf isotopes, whole‐rock major and trace element geochemistry, and Nd isotopes was carried out for two Early Jurassic two‐mica granites (Longtang and Menglong) in the southern part of the Tengchong terrane, which is in the northern part of the larger Sibumasu terrane. We assess the origin of the granites and explore their possible genetic relationship to the Paleo‐Tethyan regime. LA‐ICP‐MS zircon U‐Pb dating shows that they were simultaneously emplaced in the Early Jurassic (ca. 199 Ma). They have SiO2 contents of 69.7–75.1 wt% and are mainly strongly peraluminous with alumina saturation index (ASI) values ranging from 1.06 to 1.46. They show similar Mg# (0.29–0.42) to experimental partial melts of metasedimentary rocks under continental pressure‐temperature (P‐T) conditions. They are enriched in light rare earth elements (LREEs) relative to heavy rare earth elements (HREEs), with moderately negative Eu anomalies and flat HREEs patterns. They show negative εNd(t) values (–9.0 to –12.4) and εHf(t) values (–8.0 to –9.1). Elemental and isotopic data suggest that they most likely to formed by muscovite‐dehydration melting of a metapelitic source at lower temperatures in the range of 700°C to 750°C. The granites might represent a post‐collisional tectonic setting response to Paleo‐Tethyan regime.