Analyzing Early Mentioning of Past Buzzwords for Determination of Bloggers’ Buzzword Prediction Ability

2014 ◽  
pp. 353-367
Author(s):  
Seiya Tomonaga ◽  
Shinsuke Nakajima ◽  
Yoichi Inagaki ◽  
Reyn Nakamoto ◽  
Jianwei Zhang
Keyword(s):  
Prediction Ability

scholarly journals Genetic Algorithms-Based Approach for Wavelength Selection in Simultaneous Spectrofluorimetric Determination of Ofloxacin and Riboflavin in biological samples by Partial Least-Squares

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Farah Assadian ◽  
Ali Niazi
Keyword(s):  
Least Squares ◽  
Simultaneous Determination ◽  
Partial Least Squares ◽  
Relative Standard Error ◽  
Good Reliability ◽  
Prediction Ability ◽  
Relative Standard ◽  
Orthogonal Signal Correction ◽  
Mean Square Errors

This study shows that genetic algorithm (GA) is a suitable method for selecting wavelengths for partial least squares (PLS) calibration of mixtures with almost identical spectra without loss of prediction capacity employing spectrofluorimetric method. A training set of mixtures containing different concentrations of ofloxacin (OFL) and riboflavin (B2) were prepared to be used as calibration set to check the prediction ability of GA-PLS models due to spectral overlapping of these constituents. Each model was validated using a validation set and then real samples were analyzed. Linear calibration curves were obtained in the 0.5-5.0 and 2.0-10.0 µg mL-1 range for ofloxacin and riboflavin, respectively. To preprocess the data matrices, the orthogonal signal correction (OSC) was used and the analysis results were statistically compared. The methods accuracy for simultaneous determination of ofloxacin and riboflavin, were evaluated by the root mean square errors of prediction (RMSEP) which were 0.0868 and 0.158 for ofloxacin and riboflavin, respectively, and relative standard error of prediction (RSEP) which were 2.738 and 2.846 for ofloxacin and riboflavin, respectively using OSC-GA-PLS models. This procedure allows the simultaneous determination of OFL and B2 in human urine and serum samples with good reliability of the determination.

Improvement of prediction ability of PLS models employing the wavelet packet transform: A case study concerning FT-IR determination of gasoline parameters

Talanta ◽  
2007 ◽  
Vol 71 (3) ◽  
pp. 1136-1143 ◽  
Author(s):  
Rodrigo Neves Figueiredo dos Santos ◽  
Roberto Kawakami Harrop Galvão ◽  
Mario Cesar Ugulino Araujo ◽  
Edvan Cirino da Silva
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Prediction Ability ◽  
Ft Ir

scholarly journals A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Quanxi Feng ◽  
Huazhou Chen ◽  
Hai Xie ◽  
Ken Cai ◽  
Bin Lin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Near Infrared ◽  
Animal Feed ◽  
Calibration Model ◽  
Moving Window ◽  
Prediction Ability ◽  
Efficient Strategy ◽  
Optimization Framework ◽  
Calibration Models

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable’s population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.

scholarly journals The Provenance of Slovenian Milk Using 87Sr/86Sr Isotope Ratios

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1729
Author(s):  
Staša Hamzić Gregorčič ◽  
Nives Ogrinc ◽  
Russell Frew ◽  
Marijan Nečemer ◽  
Lidija Strojnik ◽  
...  
Keyword(s):  
Bovine Milk ◽  
Isotope Ratios ◽  
86Sr Ratio ◽  
Discrimination Power ◽  
Prediction Ability ◽  
Analytical Protocol ◽  
Production Areas ◽  
Substantial Heterogeneity ◽  
Group 1

This work presents the first use of Sr isotope ratios for determining the provenance of bovine milk from different regions of Slovenia. The analytical protocol for the determination of 87Sr/86Sr isotope ratio was optimised and applied to authentic milk samples. Considerable variability of 87Sr/86Sr ratios found in Slovenian milk reflects the substantial heterogeneity of the geological background of its origin. The results, although promising, cannot discount possible inter-annual or annual variation of the Sr isotopic composition of milk. The 87Sr/86Sr ratios of groundwater and surface waters are in good correlation with milk, indicating that the Sr isotopic fingerprint in milk is reflective of cow drinking water. The 87Sr/86Sr ratio has the potential to distinguish between different milk production areas as long as these areas are characterised by geo-lithology. Discriminant analysis (DA) incorporating the elemental composition and stable isotopes of light elements showed that 87Sr/86Sr ratio together with δ13Ccas and δ15Ncas values have the main discrimination power to distinguish the Quaternary group (group 6) from the others. Group 1 (Cretaceous: Carbonate Rocks and Flysch) is associated with Br content, 1/Sr and δ18Ow values. The overall prediction ability was found to be 63.5%. Pairwise comparisons using OPLS-DA confirmed that diet and geologic parameters are important for the separation.

Miniaturized NIR spectroscopy for the determination of main carbohydrates in syrup

NIR news ◽  
2017 ◽  
Vol 28 (2) ◽  
pp. 3-6 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Christian W. Huck
Keyword(s):  
Regression Models ◽  
Partial Least Squares Regression ◽  
Nir Spectroscopy ◽  
High Accuracy ◽  
Attractive Alternative ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Portable Spectrometers ◽  
Insight Into

This article gives an insight into the advances of miniaturized spectrometers on their way to success dealing with the determination of main carbohydrates in syrups. To evaluate the performances of such portable spectrometers, a comparison of the prediction ability, applying Partial Least Squares Regression models, was carried out. Spectra were recorded using a commercially available handheld and a conventional benchtop spectrometer. This work shows that NIR spectroscopy (both benchtop and handheld) can be used for the determination of the main carbohydrates sucrose, glucose and fructose in syrups. Measuring these main constituents in syrups is of great importance in the producing industries. Usually, these sugars are determined via enzymatic hydrolysis with high accuracy, but this method is expensive, time- and resource-consuming. These drawbacks brought up the question of possible alternatives which, if possible, are not restricted to a laboratory environment. NIR spectroscopy fulfills those requirements and was therefore considered to be an attractive alternative for this application.

scholarly journals Quantitative Analysis of the Cholesterol-Lowering Drugs Ezetimibe and Simvastatin in Pure Powder, Binary Mixtures, and a Combined Dosage Form by Spectrophotometry, Chemometry, and High-Performance Column Liquid Chromatography

2010 ◽  
Vol 93 (6) ◽  
pp. 1844-1855 ◽  
Author(s):  
Hayam Mahmoud Lotfy ◽  
Amal Mahmoud Aboul Alamein ◽  
Maha Abdel Monem Hegazy
Keyword(s):  
Least Squares ◽  
High Performance ◽  
Dosage Form ◽  
Least Squares Method ◽  
Hplc Method ◽  
Isosbestic Point ◽  
Prediction Ability ◽  
Spectrophotometric Methods ◽  
Significant Difference

Abstract Simple, accurate, sensitive, and precise UV spectrophotometric, chemometric, and HPLC methods were developed for simultaneous determination of a two-component drug mixture of ezetimibe (EZ) and simvastatin (SM) in laboratory-prepared mixtures and a combined tablet dosage form. Four spectrophotometric methods were developed, namely, ratio spectra derivative, ratio subtraction, isosbestic point, and mean centering of ratio spectra. The developed chemometric-assisted spectrophotometric method was the concentration residual augmented classical least-squares method; its prediction ability was assessed and compared to the conventional partial least-squares method. The developed HPLC method used an RP ZORBAX C18 column (5 m particle size, 250 4.6 mm id) with isocratic elution. The mobile phase was acetonitrilepH 3.5 phosphate buffer (40 60, v/v) at a flow rate of 1.0 mL/min, with UV detection at 230 nm. The accuracy, precision, and linearity ranges of the developed methods were determined. The developed methods were successfully applied for determination of EZ and SM in bulk powder, laboratory-prepared mixtures, and a combined dosage form. The results obtained were compared statistically with each other and to those of a reported HPLC method; there was no significant difference between the proposed methods and the reported method regarding both accuracy and precision.

scholarly journals Determination of Iron in Vegetable Oil by Fourier Transform Mid-Infrared Spectroscopy

2018 ◽  
Vol 10 (3) ◽  
pp. 81
Author(s):  
Pedro S. Panero ◽  
Francisco S. Panero ◽  
Jose C. S. Oliveira ◽  
João S. Panero ◽  
Anderson L. Ramos ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Vegetable Oil ◽  
Attenuated Total Reflection ◽  
Data Set ◽  
Prediction Ability ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
The Mean

The potential of Fourier transform mid-infrared spectroscopy with attenuated total reflection (FTIR-ATR) for the quantification of iron (Fe) in vegetable oil extracted from the fruit of the moriche palm (buriti) [Mauritia flexuosa] was evaluated. This green method enables direct measurements without previous sample handling. Twenty-five buriti samples were collected in Roraima (Brazil). The statistical models were developed using the technique of partial least squares (PLS) analysis and the data set was divided into two parts: one used for calibration (n = 20) and one used for testing (n = 5). First, the model was calibrated and cross-validated with the calibration data set so that the model was validated with the test data set to verify its prediction ability. To obtain reference data, the samples were analyzed by X-ray fluorescence (EDXRF). The coefficient of determination (R2) was 0.9965 and the mean square error of prediction (RMSEP) obtained for iron (Fe) was 0.8067 (in ppm). The results showed that the prediction ability can be considered good for large quantification of iron intervals in vegetable oil, and the mean relative errors were less than ±7%. This indicated that the green method for the determination of iron (Fe) in vegetable oil by Fourier transform mid-infrared spectroscopy with attenuated total reflection can be used as an alternative method to the classic methods of analysis, because it does not use reagents harmful to the environment or operator, does not generate harmful waste, uses a fast technique, and there is minimal manipulation of the sample.

Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square

2011 ◽  
Vol 345 ◽  
pp. 128-133
Author(s):  
Hong Zhi Gao ◽  
Qi Peng Lu ◽  
Fu Rong Huang
Keyword(s):  
Human Serum ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Sample Set ◽  
Cholesterol Measurement

In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.

scholarly journals Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches

2020 ◽  
Vol 98 (11) ◽  
Author(s):  
Wilson Barragán-Hernández ◽  
Liliana Mahecha-Ledesma ◽  
William Burgos-Paz ◽  
Martha Olivera-Angel ◽  
Joaquín Angulo-Arizala
Keyword(s):  
Fatty Acids ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Ground Meat ◽  
Prediction Ability

Abstract This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.

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