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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]

In recent years, the technology of deep learning has made great achievements in the field of machine learning. In this study, with the help of the transfer learning method, a kind of soft sensor is designed for the classification of iron ore tailings grade. Firstly, a sample database of froth images of flotation tailings was established. Secondly, the three most reliable models are determined after comparing the accuracy of 13 deep neural network models applied in the flotation froth image. A more accurate hybrid deep neural network model is established, with an accuracy of 97%. Finally, a software system is designed and developed, which can operate stably in the flotation plant. The experimental results show the effectiveness of the proposed hybrid deep neural network in the field of iron ore froth flotation. [ABSTRACT FROM AUTHOR]

To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the NMobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a twodimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%, and the number of parameters was decreased by 66% compared with the original MobileNetXt model. The N-MobileNetXt model was superior to other network models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification. It can provide a theoretical basis and technical support for automatic irrigation. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS: Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first‐order derivative (1stD) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF‐PLS), synergy interval partial least square (Si‐PLS), genetic algorithm‐partial least square (GA‐PLS) and competitive adaptive reweighted sampling partial least square (CARS‐PLS). RF‐PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51–14.58% and 2.10–3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION: The proposed method can be used as a rapid, accurate and non‐destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herbal medicines are commonly used in many countries after they undergo processing. Quality decoction pieces are a guarantee of the efficacy and safety of the herbal medical products. Here, a strategy based on chemical analysis combined with chemometric techniques was proposed for the classification and prediction of the different grades of the decoction pieces. Considering the necessity for a shared and simple method for the grade classification for the public, in this paper, the characterization of the chemical constituents was determined by utilizing high-performance liquid chromatography (HPLC)/diode array detection. HPLC was first established for the characterization of the chemical constituents of the different grade decoction pieces. Furthermore, a simultaneous quantification of several of the marker compounds in these decoction pieces was obtained. Finally, a partial least squares-based pattern recognition method was utilized to obtain a predictive model for the grade classification of the decoction pieces. Saposhnikovia divaricata (Turcz.) Schischk was used as a case study. The partial least squares -based pattern recognition for the grade classification of the decoction pieces of S. divaricata demonstrated good sensitivity, specificity and prediction performance, which may efficiently validate the identification results of appearance assessment. The proposed strategy is expected to provide a new insight for the grade classification and quality control of the decoction pieces. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the predictive indicator for a good prognosis of surgical resection for liver metastasis of colorectal cancer. Method: Between 1990 and 2009 at our institute, 117 patients underwent 132 hepatic resections for liver metastasis of colorectal cancer. The clinical, pathological, and outcome parameters affecting their prognoses were analyzed. The extent of the liver metastases was subdivided according to the Japanese Classification of Colorectal Carcinoma (JCCRC), and the patients were classified into different grades based on the JCCRC classification and the status of the nodal involvement of the primary tumor. Result: The median survival time (MST) of the 117 patients was 58 months, and the actuarial survival rates at 1, 3, and 5 years were 92.3, 60.0, and 46.1 %, respectively. A multivariate analysis revealed that the JCCRC Grade classification was an independent prognostic indicator (Grade A vs. B vs. C: MST, 72 vs. 41 vs. 23 months; 5-year survival, 59.0 vs. 38.6 vs. 0 %: p < 0.0001). Conclusion: Our findings indicate that the JCCRC Grade classification for liver metastasis of colorectal cancer is a significant prognostic indicator and may be useful for making decisions regarding the treatment of liver metastasis. [ABSTRACT FROM AUTHOR]

Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades ( P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine ( P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades. [ABSTRACT FROM AUTHOR]

Objective: To systematically evaluate the efficacy and safety of traditional Chinese medicine for regulating spleen and kidney. Methods: We developed a search strategy and then retrieved the database including CNKI, Wanfang data knowledge service platform, VIP journals resource integration service platform, PubMed, Embasefor randomized controlled trial of regulating spleen and kidney traditional Chinese medicine compared with conventional western medicine in the treatment of chronic uric acid nephropathy. The search deadline was set to June 30,2020. For the included literature, we applied the cochrane collaboration network risk bias assessment tool to evaluate the methodological quality, and evaluated the level of evidence according to GRADE standards. Quantitative data was analyzed by RevMan5.3 software, and trial sequential analysis method was used to analyze its efficiency. Results: A total of 709 cases in 10 articles were included. Compared with the control group, the related traditional Chinese medicine group improved the effective rate [RR=1.45,95%CI(1.32,1.58)], reduced the level of UA [MD=-36.24,95%CI(-41.03,-31.45)], BUN [SMD=-1.27, 95%CI(-1.47,-1.07)] and SCR [MD=-36.33, 95%CI(-55.79,-16.87), P=0.0003], the difference between the two groups was statistically significant (P<0.05). There was no evidence that a significant adverse reaction occurred. The results of TSA analysis showed that the Chinese medicine group had definite evidence for improving the efficiency. According to the GRADE evaluation criteria, the efficiency,UA,BUN and SCR outcome indicators were extremely low-quality evidence. Conclusions: Traditional Chinese medicine for regulating spleen and kidney in the treatment of chronic uric acid nephropathy improved efficiency, reduced the level of UA, BUN, SCR. Meanwhile, the therapy was proved to be safe. Nevertheless, the conclusions need further highquality evidence to support. [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]

To achieve high yield of crops and to avoid pesticide abuse, different control methods have been adopted according to different degrees of disease in crop plants. To address the issue, a leaf disease grade identification method based on a convolutional neural network (CNN) was proposed. First, nonuniform illumination images were processed using an adaptive adjustment algorithm based on a two-dimensional (2-D) gamma function. Then a threshold segmentation method was used to segment diseased leaf images and thus to obtain binary images. Next, the ratio of the number of pixels in the lesion area to that in the diseased leaf area was calculated. This ratio was regarded as the classification threshold of the disease grades, and therefore was used to determine the disease grade category. In addition, use of a ResNet50-based CNN was proposed to identify disease grades, with a focal loss function replacing the standard cross entropy loss function, and with the Adam optimization method. Finally, leaf disease grade identification was performed on a database containing 10 types of disease leaf images for 8 crops, and it yielded a recognition accuracy of 95.61%. The experimental results showed that the proposed method was feasible and effective for the classification of leaf disease grades. [ABSTRACT FROM AUTHOR]