Abstract As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

RESUMO Introdução: O boxe é um esporte que exige muita força explosiva, isto é, requer que seus praticantes não apenas usem sua força, mas que o façam muito rapidamente. Objetivo: Analisar a influência do treino muscular na força explosiva de boxeadores, fornecendo uma referência teórica para seu treino de força do core. Métodos: Esse artigo analisa estatisticamente a força explosiva no boxe e os fatores relevantes para a velocidade do soco. Resultados: Os fatores-chave, que afetam a velocidade máxima do soco direto no boxe, são a flexão e a extensão máxima da articulação do cotovelo, sua velocidade angular, e a velocidade angular máxima do tronco. Conclusão: No treinamento diário, deve-se considerar com atenção a força dos membros superiores e o treino da força do core. Só assim boxeadores podem conseguir uma postura correta e um centro de gravidade confiável para seus socos. Nível de evidência II; Estudos terapêuticos – investigação de resultados de tratamento.

ABSTRACT Introduction: Boxing is a sport that requires a lot of explosive power, that is, it demands from its practitioners not only to use their strength, but to do so very rapidly. Objective: To analyze the influence of muscle training on the explosive power of boxers, providing a theoretical reference for the training of their core strength. Methods: This article makes a statistical analysis of boxing explosive power and analyzes the factors relevant to punch speed. Results: The key factors that affect the maximum speed of the straight punch in boxing are the maximum elbow joint flexion and extension, angular velocity, and the maximum trunk angular velocity. Conclusion: In daily training, attention should be paid to upper limb strength and core strength training, which is the only way for boxers to have a correct body posture and ensure a reliable center of gravity when punching. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMEN Introducción: El boxeo es un deporte que exige mucha fuerza explosiva, es decir, requiere que sus practicantes no solo usen su fuerza, sino que lo hagan muy rápidamente. Objetivo: Analizar la influencia del entrenamiento muscular en la fuerza explosiva de boxeadores, proporcionando una referencia teórica para su entrenamiento de fuerza del core. Métodos: Este artículo analiza estadísticamente la fuerza explosiva en el boxeo y los factores relevantes para la velocidad del golpe. Resultados: Los factores clave, que afectan la velocidad máxima del golpe directo en el boxeo, son la flexión y extensión máxima de la articulación del codo, su velocidad angular y la velocidad angular máxima del tronco. Conclusión: En el entrenamiento cotidiano, se debe considerar con atención la fuerza de los miembros superiores y el entrenamiento de la fuerza del core. Solo así los boxeadores pueden conseguir una postura correcta y un centro de gravedad confiable para sus golpes. Nivel de evidencia II; Estudios terapéuticos – investigación de resultados de tratamiento.

Abstract Prediction of seed vigor based on hyperspectral peant. The traditional method is time-consuming and laborious to detect seed vigor. At the same time, the accuracy of the detection result is not high, and it will cause damage to the seed itself. Therefore, in order to achieve rapid and non-destructive detection of peanut seed vigor, the test was performed with original health, artificial aging for 24h and Peanut seeds with different vigor gradients at 72 hours were used as the research samples. Hyperspectral images with a wavelength range of 387~1035 nm were collected, and the image of the central part of the peanut seeds with a pixel size of 60 × 60 after correction was intercepted and the average reflectance value was calculated. After a combination of processing analysis, characteristic band processing, and model selection, a hyperspectral prediction system with the highest correlation to the viability of extracted peanut seeds was finally established. Experiments shown that the combination of hyperspectral imaging technology and the MF-LightGBM-RF model had the best performance, with a prediction accuracy of 92.59% and a fitting time of 1.77s, which simplifies the model and improves efficiency.

Abstract The classification algorithms of different peanut varieties were studied based on hyperspectral imaging technology. Firstly, the spectral images of five peanut species were collected by hyperspectral instrument produced by Zhuolihanguang Co., LTD. Then SpacVIEW was used to correct the spectral images in black and white, and ENVI5.1 was used to extract the interest in the spectral image of each peanut and calculate the mean spectral reflection value of the region. The spectral characteristic curves of the five peanut samples all showed certain differences, which lays a foundation for the feasibility of modeling in the next step. In order to eliminate the influence of non-quality factor information in hyperspectral spectral data, a variety of data preprocessing methods were used to eliminate noise in the original spectral data, and XGBoost, LightGBM, CatBoost and GBDT algorithms were used to extract feature bands. XGBoost and LightGBM were then used for classification modeling of extracted feature bands. In the classification model, both XGBoost and LightGBM can reach 99.33%, while other performance evaluation indexes cannot distinguish these two models well. Therefore, Optuna algorithm was selected to optimize the two algorithms respectively. After optimization, both LightGBM and XGBoost have improved to varying degrees, but LightGBM is relatively obvious, especially in fit_time, which is 11 times faster than XGBoost and 16 times faster than before optimization. Therefore, the best classification algorithm selected in this study is MF-LightGBM-LightGBM-Optuna-LightGBM. The research of peanut classification method provides a strong theoretical basis and technical support for the revitalization of rural industry, the integration of peanut agriculture and industry and the acceleration of the modernization of agricultural industry system, production system and management system.

Abstract This study investigated the hyperspectral reflectance response of time series generated during oven drying to changes in the moisture content of potato tubers. Seventeen preprocessing methods were used to eliminate the influence of spectral noise on the spectral characteristic curve. Algorithms such as CatBoost, LightGBM, and XGBoost are used to obtain the first 40 effective characteristic spectra of hyperspectral images, which reduces the redundancy of data and improves the prediction accuracy. The water content prediction model of potato tubers was established by using the selected characteristic bands. The results showed that the combined model based on Lasso and XGBoost algorithm had the strongest prediction ability. The best model is MF-Lasso-XGBoost, which has R2 value of 0.8908, Rmse of 0.0610, Mdae of 0.0389, and R2cv of 0.8448. This research can provide reference for the detection of potato moisture content and theoretical basis for the development of crop moisture detector.

Abstract Turmeric is a natural active substance extracted from the Zingiberaceae plant, containing a variety of derivatives, the main component being curcumin. Curcumin has a wide range of sources, low cost, and has strong antioxidant, anti-inflammatory, anti-cancer, antibacterial and other physiological functions, but it has problems such as poor water solubility, poor stability, and low bioavailability. There are certain interactions (hydrophobic interactions, electrostatic interactions, hydrogen bonds, etc.) between curcumin and bio-based proteins or polysaccharides, which can be combined to form different encapsulation systems, which can effectively improve the poor water solubility, poor stability and intestinal problems such as low bioavailability. This article focuses on the types of natural bio-based encapsulation systems (emulsion, liposome, micelle, nanoparticles, gels, and microcapsules), mechanisms and their applications in the food field of curcumin.