Machine learning-based non-destructive terahertz detection of seed quality in peanut.

Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.

[Evolution Trend of Water Quality in the Inner Mongolia Section of the Yellow River from 2003 to 2020].

The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology.

Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.

Temporal and spatial variability of water quality in an urban wetland and the effects of season and rainfall: a case study in the Daguan Wetland, China.

Urban wetlands provide multiple functions including water treatment, recreation, and education, but they are also highly vulnerable, so it is important to monitor wetland water quality to ensure wetland health. In this study, water quality parameters of an urban wetland and rainfall were monitored at 6 sites for 1 year. The correlation analysis of water quality parameters and spatial-temporal variability analysis of water quality were carried out. Besides, the effects of season and rainfall on the wetland water quality were evaluated by the comprehensive water quality identification index (CWQII). These results have shown that there is a significant correlation between nutrient pollutants and Chl-a. Wetland water quality changed with the seasons, but it also varied due to changes in rainfall and location. The water quality of the shallow areas both had high susceptibility and response to seasonal changes and rainfall, but the water quality of the deepwater area was relatively stable. The CWQIIs in different seasons were ranked: Winter (5.98) > spring (4.67) > autumn (4.66) > summer (4.26), and the CWQIIs of different rainfall intensities were ranked: torrential rain (5.09) > heavy rain (4.88) > light rain (4.50) > no rain (4.39) > moderate rain (3.95). The results of this study distinctly explained the effects of season and rainfall on water quality in an urban wetland in a subtropical monsoon climate zone and would be helpful to the policymakers and concerned authorities in developing better water quality management strategies for these wetlands.

[Water Quality Assessment and Spatial-temporal Variation Analysis in Yellow River Basin].

During the implementation of ecological protection in the Yellow River basin, understanding the water pollution status and spatio-temporal variation of water quality has become the most important thing for water safety in the basin. To analyze the water quality in recent years, the water quality data in the Yellow River basin from 2004 to 2018 were firstly collected from eight typical monitoring stations. Using a combination of multivariate data analysis methods including the Mann-Kendall (M-K) trend test, hierarchical clustering analysis (HCA), principal component analysis (PCA), and modified comprehensive water quality identification index (WQI), the spatio-temporal variation characteristics of the water quality were then explored in the Yellow River basin. The results indicated that in terms of time variation, the HCA from the water quality time series showed that the water quality of the Yellow River basin could be divided into the wet season, normal season, and dry season, being basically consistent with the hydrological period. Combined with the M-K trend test and WQI-based water quality assessment, the water quality of the Yellow River basin was improving gradually, with 2010 as the critical year. The water quality in the wet season was superior to that in the dry season. The pollution indicator NH4+-N and permanganate index were dominant in both the wet season and dry season. According to the spatial variation analysis, the water quality for all the studied stations improved significantly. Spatial clustering showed that the S6 (Shanxi Yuncheng Hejin Bridge) was obviously different from others, and further comparative study demonstrated that S6 was constantly seriously polluted. The S7 (Henan Jiyuan Xiaolangdi) exhibited different characteristics in the wet and dry season. In all stations, NH4+-N was considered to be the most common pollution indicator, whereas the permanganate index and DO were also relatively serious for S6. In different hydrological seasons, NH4+-N and the permanganate index showed different characteristics, and their variety was related to the fact that the former mainly came from domestic and industrial sources, whereas the latter was mainly derived from agricultural sources. The modified WQI showed obvious advantages over single-factor water quality assessment, and the findings from this study can provide scientific evidence for water pollution control and comprehensive water quality management in the Yellow River basin.

Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory.

The Yongding New River is essential for the water supplies of Tianjin. To date, there is no comprehensive report that assesses the year-round water quality of the Yongding New River Main stream. Moreover, little attention has been given to determining a combined weight for improving the traditional comprehensive water quality identification index (ICWQII) by the game theory. Seven water quality parameters were investigated monthly along the main stream of the Yongding New River from May 2018 to April 2019. Organic contaminants and nitrogen pollution were mainly caused by point sources pollution, and the total phosphorus mainly by non-point source pollution. Dramatic spatio-temporal variations of water quality parameters were jointly caused by different pollutant sources and hydrometeorological factors. In terms of this study, an improved comprehensive water quality identification index (ICWQII) based on entropy weight or variation coefficient and traditional CWQII underestimated the water qualities, and an ICWQII based on the superstandard multiple method overvalued the assessments. By contrast, water qualities assessments done with an ICWQII based on the game theory matched perfectly with the practical situation. The ICWQII based on game theory proposed in this study takes into account not only the degree of disorder and variation of water quality data, but also the influence of standard-exceeded pollution indicators, whose results are relatively reasonable. All findings and the ICWQII based on game theory can provide scientific support for decisions related to the water environment management of the Yongding New River and other waters.

[Study on quality identification of Curcumae Rhizoma from different origins based on quantitative analysis of appearance color and content of main components].

L~*, a~* and b~* values of prepared slices of Curcumae Rhizoma were measured by spectrophotometer. SPSS 21.0 was used for discriminant analysis to establish the color range and mathematical prediction model of prepared slices of Curcumae Rhizoma. The values of L~*, a~* and b~* of kwangsiensis ranged from 58.09-62.40, 4.53-5.66 and 23.61-24.29, while the values of L~*, a~* and b~* of phaeocaulis were between 64.02-70.71,-0.89-4.13 and 44.59-54.52, respectively. The values of L~*, a~* and b~* of wenyujin were 68.55-70.99,-0.11-1.47 and 28.26-32.19, respectively. The mathematical prediction model was proved to be able to realize 100% identification of Curcumae Rhizome of different origins through original and cross validation and external samples validation. A dual wavelength HPLC was established; the contents of 9 sesquiterpenoids and 3 Curcumae Rhizomes were determined simultaneously; and the contents of Curcumae Rhizome of different origins were determined. The results showed that kwangsiensis had higher contents of neocurdione, ß-elemene and isocurcumaenol, phaeocaulis curcumin, furadienone, demethoxycurcumin and curcumin; and wenyujin mainly contained curdione, furadienes and guimarone. Pearson correlation analysis on L~*, a~*, b~* value and content of 12 components showed that curcumin, furadienone, demethoxycurcumin and curcumin had a significant positive correlation with b~* value(P<0.01). There was a significant negative correlation between neocurdione, ß-elemene and isocurcumaenol and L~* value(P<0.01). Curdione, furadienes and guimarone were significantly correlated with L~* value(P<0.01),indicating that the appearance co-lor of Curcumae Rhizoma could reflect the change of the content of the internal components. This study provided reference for the rapid recognition of Curcumae Rhizoma and the establishment of quality evaluation system.

Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors.

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.

Study on quality identification of Curcumae Rhizoma from different origins based on quantitative analysis of appearance color and content of main components / 中国中药杂志

L~*, a~* and b~* values of prepared slices of Curcumae Rhizoma were measured by spectrophotometer. SPSS 21.0 was used for discriminant analysis to establish the color range and mathematical prediction model of prepared slices of Curcumae Rhizoma. The values of L~*, a~* and b~* of kwangsiensis ranged from 58.09-62.40, 4.53-5.66 and 23.61-24.29, while the values of L~*, a~* and b~* of phaeocaulis were between 64.02-70.71,-0.89-4.13 and 44.59-54.52, respectively. The values of L~*, a~* and b~* of wenyujin were 68.55-70.99,-0.11-1.47 and 28.26-32.19, respectively. The mathematical prediction model was proved to be able to realize 100% identification of Curcumae Rhizome of different origins through original and cross validation and external samples validation. A dual wavelength HPLC was established; the contents of 9 sesquiterpenoids and 3 Curcumae Rhizomes were determined simultaneously; and the contents of Curcumae Rhizome of different origins were determined. The results showed that kwangsiensis had higher contents of neocurdione, β-elemene and isocurcumaenol, phaeocaulis curcumin, furadienone, demethoxycurcumin and curcumin; and wenyujin mainly contained curdione, furadienes and guimarone. Pearson correlation analysis on L~*, a~*, b~* value and content of 12 components showed that curcumin, furadienone, demethoxycurcumin and curcumin had a significant positive correlation with b~* value(P<0.01). There was a significant negative correlation between neocurdione, β-elemene and isocurcumaenol and L~* value(P<0.01). Curdione, furadienes and guimarone were significantly correlated with L~* value(P<0.01),indicating that the appearance co-lor of Curcumae Rhizoma could reflect the change of the content of the internal components. This study provided reference for the rapid recognition of Curcumae Rhizoma and the establishment of quality evaluation system.

[A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology].

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.

A rapid identification of authenticity and specifications of Chinese medicine Fritillariae Cirrhosae Bulbus based on E-eye technology / 中国中药杂志

The quality of traditional Chinese medicine tablets is correlated with clinical efficacy and drug safety, and plays a great role in promoting the development of traditional Chinese medicine. However, the existing traditional artificial identification and modern instrument detection in terms of accuracy and timeliness have both advantages and disadvantages. Therefore, how to quickly and accurately identify the quality of traditional Chinese medicine tablets has become a high-profile issue. The purpose of this paper is to explore the feasibility of the application of electronic eye technology in the study of rapid identification of traditional Chinese medicine quality. A total of 80 batches of samples were collected and tested by Fritillariae Cirrhosae Bulbus for traditional empirical identification(M_1) and modern pharmacopeia(M_2). The optical data was collected from electronic eyes, and the chemical metrology was used to establish suitable discrimination models(M_3). Four authenticity and commodity specification models, namely identification analysis(DA), minimum bidirectional support vector machine(LS-SVM), partial minimum two-multiplier analysis(PLS-DA), main component analysis identification analysis(PCA-DA), were established, respectively. The accuracies of the authenticity identification models were 82.5%, 90.0%, 96.2% and 93.8%, while the accuracies of the commodity specification identification models were 89.3%, 96.0%, 90.7% and 97.3%, respectively. The models were well judged, the authenticity identification was based on the final identification model of PLS-DA, and the commodity specification was based on the final identification model of PCA-DA. There was no significant difference between its accuracy and M_1, and the time of determination was much shorter than M_2(P<0.01). Therefore, electronic-eye technology could be used for the rapid identification of the quality of Fritillariae Cirrhosae Bulbus.

A Novel Method to Identify Three Quality Grades of Herbal Medicine Ophiopogonis Radix by Microscopic Quantification.

Maidong, the root tuber of Ophiopogon japonicus (Thunb.) Ker Gawl., is a commonly used herbal medicine in China. There are three quality grades of Maidong according to traditional opinion and modern research studies: superior quality (Zhe-Maidong), medium quality (Chuan-Maidong), and poorest quality (Chuan-Maidong with paclobutrazol, which is a kind of plant growth regulator). However, no efficient way to distinguish the three quality grades of Maidong exists; thus, the herbal markets and botanical pharmacies are flooded with Chuan-Maidong with paclobutrazol. To ensure the safety and quality of Maidong, a comparative microscopic study was performed on three quality grades of Maidong. The result was to establish a microscopic quantification method based on the area ratio between xylem and pith to distinguish the three quality grades of Maidong. Subsequently, Maidong from regional markets was evaluated by this method. In this study, we developed a novel quantification method to identify the three quality grades of Maidong, which could in turn make efforts on the quality improvement of Maidong. Our study is the first to demonstrate that microscopic technology could be used to distinguish different quality grades of a specific herbal medicine.

[Applications of mass spectrometry-based proteomics in food authentication and quality identification].

Proteomics, as a new research direction in the post-genomics era, has been developing rapidly in recent years and has been applied in many fields, becoming a powerful research tool in food quality identification and safety control. Proteomics opens up a new horizon in food science research. It can not only identify the species of characteristic proteins, but also quantify the response of targeted proteins. Proteomics allows for the dynamic analysis of protein composition and content in samples that vary in species, geographical origin or growth stage. The research methods of proteomics are varied, and mass spectrometry (MS) is one of the most common techniques. This paper introduces the concept, classification, and research technology of proteomics, as well as common protein databases. Applications of MS-based proteomics in food authentication and quality identification were reviewed, including seafood, meat products, dairy products, health food, and value-added food. Furthermore, the future development of proteomics was investigated.

[Research progress on identification and quality control of Cervi Cornu Pantotrichum].

Cervi Cornu Pantotrichum, as a traditional Chinese medicine, has great potential for development. However, the identification and quality control system is not perfect, leading to the market chaos and chronic slow growth in deep processing of Cervi Cornu Pantotrichum. This paper gives an overview of present situation in identification and quality control system of the Cervi Cornu Pantotrichum, and analyzes present problems. Based on these results, the feasibility study scheme in identification and quality control system for Cervi Cornu Pantotrichum would be then put forward, providing ideas to establish its comprehensive evaluation system.

Chronic Toxicity Test in Cynomolgus Monkeys For 98 Days with Repeated Intravenous Infusion of Cynomolgus Umbilical Cord Mesenchymal Stem Cells.

BACKGROUND/AIMS: Stem cell-based therapy is attractive in many clinical studies, but current data on the safety of stem cell applications remains inadequate. This study observed the safety, immunological effect of cynomolgus monkey umbilical cord mesenchymal stem cells (mUC-MSCs) injected into cynomolgus monkeys, in order to evaluate the safety of human umbilical cord mesenchymal stem cells (hUC-MSCs) prepared for human clinical application. METHODS: Eighteen cynomolgus monkeys were divided into three groups. Group 1 is control group, Group 2 is low-dose group, Group 3 is high-dose group. After repeated administrations of mUC-MSCs, cynomolgus monkeys were observed for possible toxic reactions. RESULTS: During the experiment, no animal died. There were no toxicological abnormalities in body weight, body temperature, electrocardiogram, coagulation and pathology. In the groups 2 and 3, AST and CK transiently increased, and serum inorganic P slightly decreased. All animals were able to recover at 28 days after the infusion was stopped. In the groups 2 and 3, CD3+ and IL-6 levels significantly increased, and recovery was after 28 days of infusion. There were no obvious pathological changes associated with the infusion of cells in the general and microscopic examinations. CONCLUSIONS: The safe dosage of repeated intravenous infusion of mUC-MSCs in cynomolgus monkeys is 1.0 × 107/kg, which is 10 times of that in clinical human use.

Research progress on identification and quality control of Cervi Cornu Pantotrichum / 中国中药杂志

Cervi Cornu Pantotrichum, as a traditional Chinese medicine, has great potential for development. However, the identification and quality control system is not perfect, leading to the market chaos and chronic slow growth in deep processing of Cervi Cornu Pantotrichum. This paper gives an overview of present situation in identification and quality control system of the Cervi Cornu Pantotrichum, and analyzes present problems. Based on these results, the feasibility study scheme in identification and quality control system for Cervi Cornu Pantotrichum would be then put forward, providing ideas to establish its comprehensive evaluation system.

Comparison of seven water quality assessment methods for the characterization and management of highly impaired river systems.

In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.