scholarly journals Rapid Determination of Aesculin, Aesculetin and Fraxetin inCortex FraxiniExtract Solutions Based on Ultraviolet Spectroscopy

2011 ◽  
Vol 8 (s1) ◽  
pp. S225-S236 ◽  
Author(s):  
Ming Yang ◽  
Jia-Lei Chen ◽  
Xiu-Feng Shi ◽  
Hui-Jue Niu
Keyword(s):  
Root Mean Square Error ◽  
Reference Data ◽  
Rapid Determination ◽  
Ultraviolet Spectroscopy ◽  
Mean Square ◽  
Chemical Parameters ◽  
Ultraviolet Spectra ◽  
Calibration Models ◽  
The Relationship

To evaluate the application of ultraviolet spectroscopy for the rapid determination of aesculin, aesculetin and fraxetin inCortex fraxiniextract solutions, ultraviolet spectra ofCortex fraxiniextract solutions from different batches were collected in the spectral range from 200 nm to 400 nm. The relationship between ultraviolet spectra and chemical parameters displayed some non-linear characteristics. Thus, K-OPLS was proposed to establish the calibration models for the determination ofCortex fraxiniextract solutions between the reference data and ultraviolet spectra. The calibration results were achieved for the determination ofCortex fraxiniextract solutions. The coefficients of determination in calibration (R2) for aesculin, aesculetin and fraxetin were 0.989, 0.957 and 0.939, while in prediction (R2) were 0.982, 0.979 and 0.962, respectively. And the root-mean-square error of prediction (RMSEP) for aesculin, aesculet and fraxetin were 11.99, 3.02 and 1.59 μg/mL. The results demonstrated that ultraviolet spectroscopy could be used for the rapid determination of these three components inCortex fraxiniextract solutions.

scholarly journals Rapid and Simultaneous Measurement of Praeruptorin A, Praeruptorin B, Praeruptorin E, and Moisture Contents in Peucedani Radix Using Near-Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 103 (2) ◽  
pp. 504-512
Author(s):  
Yijuan Hu ◽  
Hongjian Zhang ◽  
Weiqing Liang ◽  
Pan Xu ◽  
Kelang Lou ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mean Square ◽  
Determination Coefficient ◽  
Moisture Contents ◽  
Calibration Models

Abstract Background: Peucedani Radix is a popular traditional Chinese medicine herb with a long history in China. Praeruptorin A (PA), praeruptorin B (PB), and praeruptorin E (PE) are usually taken as important quality indexes of Peucedani Radix. Objective: To establish a rapid method for simultaneous determination of PA, PB, PE, and moisture contents in Peucedani Radix using near-infrared (NIR) spectroscopy and chemometrics. Methods: One hundred twenty Peucedani Radix samples were analyzed with HPLC as a reference method. The NIR spectral scanning range was from 12000 cm−1 to 4000 cm−1. Partial least squares (PLS) regression algorithm was used to establish calibration models. Three variable selection methods were investigated, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and Monte Carlo uninformative variable elimination (MCUVE). The performances of the established models were evaluated by root-mean-square error (RMSEC) and determination coefficient (Rc2) of calibration set, root-mean-square error (RMSEP) and determination coefficient (Rp2) of prediction set, and residual predictive deviation (RPD). Results: A clear ranking of the performance of the calibration models could be as follows: CARS-PLS > MCUVE-PLS > VIP-PLS > Full-PLS. For CARS-PLS, Rp2, RMSEP, and RPD of the prediction set are as follows: 0.9204, 0.0860%, and 3.5850 for PA; 0.8011, 0.0431%, and 2.0868 for PB; 0.8043, 0.0367%, and 2.1569 for PE; and 0.9249, 0.3350%, and 3.6551 for moisture, respectively. Conclusions: The NIR spectroscopy combined with CARS-PLS calibration models could be used for rapid and accurate determination of PA, PB, PE, and moisture contents in Peucedani Radix samples.

scholarly journals Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control

2018 ◽  
Vol 11 (06) ◽  
pp. 1850034
Author(s):  
Hongxia Huang ◽  
Yuanyuan Lv ◽  
Xiaoyi Sun ◽  
Shuangshuang Fu ◽  
Xuefang Lou ◽  
...  
Keyword(s):  
Quality Control ◽  
Chinese Medicine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Uv Spectroscopy ◽  
Uv Spectrophotometry ◽  
Mean Square ◽  
Tannin Content

A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient ([Formula: see text]) of 0.952, root mean square error of calibration (RMSEC) of 0.476[Formula: see text][Formula: see text]g/ml, root mean square error of validation (RMSEV) of 1.171[Formula: see text][Formula: see text]g/ml, and root mean square error of prediction (RMSEP) of 0.465[Formula: see text][Formula: see text]g/ml. Pattern recognition models using linear discriminant analysis (LDA) and [Formula: see text] nearest neighbor ([Formula: see text]-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

DETERMINATION OF CLIMATICALLY SUITABLE AREAS FOR SOYBEAN (Glycine max (L.) MERR.) PRODUCTION IN MANITOBA

1985 ◽  
Vol 65 (3) ◽  
pp. 511-522 ◽  
Author(s):  
R. B. BURNETT ◽  
G. W. FALK ◽  
C. F. SHAYKEWICH
Keyword(s):  
Glycine Max ◽  
Root Mean Square Error ◽  
Time Scale ◽  
Small Area ◽  
Daily Maximum ◽  
Mean Square ◽  
Weather Station ◽  
Stage Of Development ◽  
Glycine Max L

The effects of daily maximum and minimum temperature and photoperiod on the phenological development of soybean cultivars Maple Presto and McCall were studied under field conditions over the period 1979–1982. Experimental sites were located throughout the agricultural area of Manitoba. Data collected were used to derive a biometeorological time scale for each cultivar. For this purpose, the growth of the crop was divided into four stages: planting to emergence, emergence to flowering, flowering to beginning pod fill, and beginning pod fill to maturity. When the biometeorological time scale developed from the first 3 yr of the study was used to predict the duration of each stage of development during the fourth year, the largest root mean square error of estimation was 4.0 days. A biometeorological time scale developed from all 4 yr of data was applied to historical weather station data in order to determine the probability of maturing soybeans in Manitoba. According to the analyses, only a small area in central Manitoba south of 50° N latitude is capable of maturing the cultivar McCall 90% of the time. Most of the agricultural region can expect to mature the cultivar Maple Presto 90% of the time.Key words: Soybeans, climatically suitable areas

Waveband Selection for NIR Spectroscopic Analysis of Zn2+ in Soil

2013 ◽  
Vol 365-366 ◽  
pp. 737-740
Author(s):  
Li Jun Yao ◽  
Jie Mei Chen ◽  
Tao Pan
Keyword(s):  
Root Mean Square Error ◽  
Near Infrared ◽  
Spectroscopic Analysis ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Optimal Number ◽  
Moving Window ◽  
Mean Square ◽  
Selection For

Near-infrared (NIR) spectroscopy combined with moving window partial least squares (MWPLS) method was successfully applied to the waveband selection for the rapid chemical-free determination of Zn2+ in soil. Based on randomness and similarity, an effective approach was performed to obtain objective and practical models. The optimal MWPLS waveband was 1136-1252 nm, and the corresponding optimal number of PLS factors was 6. The validation root mean square error (V-SEP) and validation correlation coefficients (V-RP) of prediction were 15.658 mg kg-1 and 0.925, respectively. The Zn2+ prediction values of the validation samples are close to the measured values. The results provided a reliable NIR model and can serve as valuable references for designing the dedicated spectroscopic instruments.

Determination of Loganin in Qiju Dihuang Pills by Near-Infrared Spectroscopy Technique

2013 ◽  
Vol 807-809 ◽  
pp. 1978-1983 ◽  
Author(s):  
Cai Xia Xie ◽  
Hai Yan Gong ◽  
Jian Ying Liu ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

To establish a rapid analytical method for Loganin in Qiju Dihuang Pills (condensed) by Near-infrared Diffuse Reflectance Technique. Collecting NIR spectra by NIR Diffuse Reflectance Spectroscopy, the partial least square calibration model was built. The correlation coefficients (R2) and the root-mean-square error of cross-validation (RMSECV) were 0.99764 and 0.09340, respectively. In the external validation,coefficients of determination (r2) between NIRS and HPLC values was 0.97348,the root-mean-square error of prediction (RMSEP) was 0.08491. The results showed that the method was rapid, accurate, and could be applied to the fast determination of Loganin in Qiju Dihuang Pills (condensed).

scholarly journals Rapid determination of hyaluronic acid concentration in fermentation broth with near-infrared spectroscopy

2014 ◽  
Vol 07 (06) ◽  
pp. 1450012 ◽  
Author(s):  
Qin Dong ◽  
Hengchang Zang ◽  
Lixuan Zang ◽  
Aihua Liu ◽  
Yanli Shi ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Fermentation Process ◽  
Near Infrared ◽  
Fermentation Broth ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Promising Tool ◽  
Calibration Models ◽  
Order Polynomial

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

Comparison of Calibrations Modeling for Determination of Protein of Mushroom Base on Middle Infrared Spectroscopy

2011 ◽  
Vol 460-461 ◽  
pp. 357-362 ◽  
Author(s):  
Peng Cheng Nie ◽  
Yan Yang ◽  
Yong He
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Latent Variables ◽  
Nonlinear Modeling ◽  
Mean Square ◽  
Linear Modeling ◽  
Fast Determination ◽  
Validation Set ◽  
Middle Infrared

Middle infrared spectroscopy combined with chemometrics was investigated for the fast determination of protein of mushroom. 140 samples (35 for each variety) were selected randomly for the calibration set, whereas, 40 samples for the validation set. After some spectrum preprocessing, linear modeling method (PLS) and nonlinear modeling LS-SVM were constructed. Different latent variables were used as inputs of LS-SVM. The optimal models were obtained with 8 LVs based on LS-SVM. The correlation coefficient,,root mean square error of prediction for the best prediction by LV-LS-SVM were 0.9275, 0.25961. The result indicated that middle infrared spectroscopy combined with LV-LS-SVM could be applied as a high precision and fast way for determination protein of mushroom.

scholarly journals Four meta-analyses across 164 studies on atypical footedness prevalence and its relation to handedness

2020 ◽  
Author(s):  
Julian Packheiser ◽  
Judith Schmitz ◽  
Gesa Berretz ◽  
David Carey ◽  
Silvia Paracchini ◽  
...  
Keyword(s):  
Social Factors ◽  
Large Scale ◽  
Reference Data ◽  
Population Level ◽  
Classification Systems ◽  
Point Estimate ◽  
Moderating Factors ◽  
Meta Analyses ◽  
The Relationship

Human lateral preferences, such as handedness and footedness, have interested researchers for decades due to their pronounced asymmetries at population level. While there are good estimates on the prevalence of handedness in the population, there is, to this, day no large-scale estimation on the prevalence of footedness. Furthermore, the relationship between footedness and handedness still remains elusive. Here, we conducted meta-analyses with four different classification systems for footedness on 145,150 individuals across 164 studies including new data from the ALSPAC cohort. The aims of the study were to determine a reliable point estimate of footedness, to study the association between footedness and handedness, and to investigate moderating factors influencing footedness. We showed that the prevalence of atypical footedness ranges between 12.10% using the most conservative criterion of left-footedness to 23.7% including all left- and mixed-footers as a single non-right category. As many as 60.1% of left-handers were left-footed whereas only 3.2% of right-handers were left-footed. Males were 4.1% more often non-right-footed compared to females. Individuals with psychiatric and neurodevelopmental disorders exhibited a higher prevalence of non-right-footedness. Furthermore, the presence of mixed-footedness was higher in children compared to adults as well as in experienced athletes compared to the general population. Finally, we showed that footedness is only marginally influenced by cultural and social factors, which have been shown to play a crucial role in the determination of handedness. Overall, this study provides new and useful reference data for laterality research. Furthermore, the data suggest that footedness is a valuable phenotype for the study of lateral motor biases, its underlying genetics and neurodevelopment.

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