Search

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades. [ABSTRACT FROM AUTHOR]

Addressing issues such as a low operational condition recognition efficiency, strong subjectivity, and significant fluctuations in Outotec X fluorescence analysis data in copper flotation production, a copper concentrate grade classification model is constructed based on image processing technology and the Stacking ensemble learning algorithm. Firstly, a feature extraction model for copper concentration flotation foam images is established, extracting color, texture, and size statistical features to build a feature dataset. Secondly, to avoid redundancy in the feature data, which could reduce model accuracy, a combined correlation feature selection is employed for dimensionality reduction, with the filtered feature subset being used as the model input. Finally, to fully leverage the strengths of each model, a Stacking ensemble learning copper concentrate grade classification model is constructed with support vector machine (SVM), random forest (RF), and adaptive boosting (AdaBoost) as base models and logistic regression (LR) as the meta-model. The experimental results show that this ensemble model achieves good recognition for different grade categories, with a precision, recall, and F1 score of 90.01%, 89.85%, and 89.93%, respectively. The accuracy of this Stacking ensemble model, with a 7% improvement over Outotec X fluorescence analysis, demonstrates a potential to meet the daily production needs of beneficiation plants. [ABSTRACT FROM AUTHOR]

The eating quality evaluation of rice is raising further concerns among researchers and consumers. This research is aimed to apply lipidomics in determining the distinction between different grades of indica rice and establishing effective models for rice quality evaluation. Herein, a high-throughput ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight (UPLC-QTOF/MS) method for comprehensive lipidomics profiling of rice was developed. Then, a total of 42 significantly different lipids among 3 sensory levels were identified and quantified for indica rice. The orthogonal partial least-squares discriminant analysis (OPLS-DA) models with the two sets of differential lipids showed clear distinction among three grades of indica rice. A correlation coefficient of 0.917 was obtained between the practical and model-predicted tasting scores of indica rice. Random forest (RF) results further verified the OPLS-DA model, and the accuracy of this method for grade prediction was 90.20%. Thus, this established approach was an efficient method for the eating grade prediction of indica rice. [ABSTRACT FROM AUTHOR]

Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China's Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. It is made from Yunnan large-leaf sun-green tea with specific processing techniques. The quality formation of Pu-erh tea is closely related to the soil's environmental conditions. In this paper, time-by-time data of the soil environment of tea plantations during the autumn tea harvesting period in Menghai County, Xishuangbanna, Yunnan Province, China, in 2021 were analyzed. Spearman's correlation analysis was conducted between the inner components of Pu'er tea and the soil environmental factor. The analysis showed that three soil environmental indicators, soil temperature, soil moisture, and soil pH, were highly significantly correlated. The soil environmental quality evaluation method was proposed based on the selected soil environmental characteristics. Meanwhile, a deep learning model of Long Short Term Memory (LSTM) Network for the soil environmental quality of tea plantation was established according to the proposed method, and the soil environmental quality of tea was classified into four classes. In addition, the paper also compares the constructed models based on BP neural network and random forest to evaluate the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the indicators for comparative analysis. This paper innovatively proposes to introduce the main inclusions of Pu'er tea into the classification and discrimination model of the soil environment in tea plantations, while using machine learning-related algorithms to classify and predict the categories of soil environmental quality, instead of relying solely on statistical data for analysis. This research work makes it possible to quickly and accurately determines the physiological status of tea leaves based on the establishment of a soil environment quality prediction model, which provides effective data for the intelligent management of tea plantations and has the advantage of rapid and low-cost assessment compared with the need to measure the intrinsic quality of Pu-erh tea after harvesting is completed. [ABSTRACT FROM AUTHOR]

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management. [ABSTRACT FROM AUTHOR]

City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This paper mainly utilizes Luojia I Night-Time Light (NTL) images to analyze the rank classification of cities in Henan Province, China. Intensity values can be expressed as the mathematical surface of continuous human activities, and the basic characteristics of urban structures are determined by analogy with the topography of the earth. A connectivity analysis method for NTL images is proposed to analyze the connected regions of images at different intensity levels. By constructing a tree structure, different cities can be analyzed "crosswise" and "lengthwise" to generate a series of parametric information from connected regions of NTL images. Based on these parameters, 18 cities in Henan Province were classified and analyzed. The results show that these attribute information can be well used for city center detection and grade classification, and can meet the requirements of application analysis. [ABSTRACT FROM AUTHOR]

Storm surge has become an important factor restricting the economic and social development of China's coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation., Competing Interests: The authors declare no conflict of interest.