scholarly journals Evaluation of Calibration Equations by Using Regression Analysis: An Example of Chemical Analysis

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 447
Author(s):  
Hsuan-Yu Chen ◽  
Chiachung Chen
Keyword(s):  
Regression Analysis ◽  
Higher Order ◽  
Data Sets ◽  
Polynomial Equations ◽  
Prediction Ability ◽  
Calibration Equation ◽  
Response Data ◽  
Constant Variance ◽  
Order Polynomial ◽  
Calibration Equations

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

Archiving of Food Samples from Restaurants and Caterers—Quantitative Profiling of Outbreaks of Foodborne Salmonellosis in Japan

2004 ◽  
Vol 67 (9) ◽  
pp. 2024-2032 ◽  
Author(s):  
FUMIKO KASUGA ◽  
MASAMITSU HIROTA ◽  
MASAMICHI WADA ◽  
TOSHIHIKO YUNOKAWA ◽  
HAJIME TOYOFUKU ◽  
...  
Keyword(s):  
Dose Response ◽  
Storage System ◽  
Epidemiological Data ◽  
Food Samples ◽  
Food Storage ◽  
Disease Rate ◽  
Data Sets ◽  
Response Data ◽  
Quantitative Profiling

The Ministry of Health, Labor and Welfare (former MHW) of Japan issued a Directive in 1997 advising restaurants and caterers to freeze portions of both raw food and cooked dishes for at least 2 weeks. This system has been useful for determining vehicle foods at outbreaks. Enumeration of bacteria in samples of stored food provide data about pathogen concentrations in the implicated food. Data on Salmonella concentrations in vehicle foods associated with salmonellosis outbreaks were collected in Japan between 1989 and 1998. The 39 outbreaks that occurred during this period were categorized by the settings where the outbreaks took place, and epidemiological data from each outbreak were summarized. Characteristics of outbreak groups were analyzed and compared. The effect of new food-storage system on determination of bacterial concentration was evaluated. Freezing and nonfreezing conditions prior to microbial examination were compared in the dose-response relationship. Data from outbreaks in which implicated foods had been kept frozen suggested apparent correlation between the Salmonella dose ingested and the disease rate. Combined with results of epidemiological investigation, quantitative data from the ingested pathogen could provide complete dose-response data sets.

scholarly journals On the existence of higher order polynomial lattices based on a generalized figure of merit

2007 ◽  
Vol 23 (4-6) ◽  
pp. 581-593 ◽  
Author(s):  
Josef Dick ◽  
Peter Kritzer ◽  
Friedrich Pillichshammer ◽  
Wolfgang Ch. Schmid
Keyword(s):  
Figure Of Merit ◽  
Higher Order ◽  
Order Polynomial

Calibration of pneumotachographs using a calibrated syringe

2003 ◽  
Vol 95 (2) ◽  
pp. 571-576 ◽  
Author(s):  
Yongquan Tang ◽  
Martin J. Turner ◽  
Johnny S. Yem ◽  
A. Barry Baker
Keyword(s):  
Calibration Method ◽  
Linear Range ◽  
Data Sets ◽  
Constant Flow ◽  
Calibration Constant ◽  
Third Order ◽  
Polynomial Coefficients ◽  
Calibration Curves ◽  
Order Polynomial ◽  
Better Than

Pneumotachograph require frequent calibration. Constant-flow methods allow polynomial calibration curves to be derived but are time consuming. The iterative syringe stroke technique is moderately efficient but results in discontinuous conductance arrays. This study investigated the derivation of first-, second-, and third-order polynomial calibration curves from 6 to 50 strokes of a calibration syringe. We used multiple linear regression to derive first-, second-, and third-order polynomial coefficients from two sets of 6–50 syringe strokes. In part A, peak flows did not exceed the specified linear range of the pneumotachograph, whereas flows in part B peaked at 160% of the maximum linear range. Conductance arrays were derived from the same data sets by using a published algorithm. Volume errors of the calibration strokes and of separate sets of 70 validation strokes ( part A) and 140 validation strokes ( part B) were calculated by using the polynomials and conductance arrays. Second- and third-order polynomials derived from 10 calibration strokes achieved volume variability equal to or better than conductance arrays derived from 50 strokes. We found that evaluation of conductance arrays using the calibration syringe strokes yields falsely low volume variances. We conclude that accurate polynomial curves can be derived from as few as 10 syringe strokes, and the new polynomial calibration method is substantially more time efficient than previously published conductance methods.

scholarly journals Features of сalibration еequations for IR scanners in determining the amino acid composition of soy proteins

2020 ◽  
Author(s):  
С.Е. НИЗКИЙ ◽  
Г.А. КОДИРОВА ◽  
Г.В. КУБАНКОВА
Keyword(s):  
Amino Acids ◽  
Liquid Chromatography ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
High Performance ◽  
Near Infrared ◽  
Soybean Proteins ◽  
Calibration Equation ◽  
Calibration Equations

Из 20 аминокислот, входящих в состав растительных белков, 17 лучше всего определяются с помощью высокоэффективной жидкостной хроматографии. Но эта технология затратна по времени, в том числе из-за подготовки проб, что делает ее малопригодной при проведении массовых анализов, например при оценке селекционного материала. В этом случае наиболее приемлемы технологии, основанные на сканировании в ближнем инфракрасном диапазоне излучения. Несмотря на то что ИК-сканеры способны по одному калибровочному уравнению выявлять большое количество компонентов, необходима постоянная коррекция при определении состава аминокислот и приведении его в процентное соотношение. В статье рассматриваются варианты создания калибровочных уравнений для расчета аминокислотного состава белков сои с помощью компьютерных программ (Nir 42, ISI), обеспечивающих работу ИК-сканеров типа NIR-4250 или FOSS NIRSystem 5000. Установлено, что при создании калибровочных уравнений содержание каждой аминокислоты наиболее корректно выражать в абсолютных единицах (г на 100 г белка), а не относительных (%). 17 of the 20 amino acids, included in the composition of plant proteins, are most effectively determined using liquid chromatography. The technology of high-performance liquid chromatography is to a certain extent costly in time, among other things because of sample preparation that makes it unsuitable for mass analysis, for example, when evaluating a breeding material. In this case, the technology based on scanning in the near infrared radiation band are the most acceptable. Despite the fact that IR scanners are able to determine a sufficiently large number of components on the basis of one calibration equation, a constant correction is required when determining the composition of amino acids and reducing it to a percentage ratio. The options for creating calibration equations for determining the amino acid composition of soybean proteins for computer programs (Nir 42, ISI), which provide the operation of IR scanners, such as NIR-4250 or FOSS NIRSystem 5000 are considered in the article. It was found that when creating calibration equations, it is most correct to set for each amino acid its mass content (g per 100 g of protein), and not the relative portion (in %).

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (<i>P</i>&lt;.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; <i>P</i>&lt;.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; <i>P</i>=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; <i>P</i>=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, <i>P</i>&lt;.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; <i>P</i>&lt;.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; <i>P</i>=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

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