Chemometric approach in the development of the colorimetric method for the estimation of food colorants in meat products

The aim of this research was to develop a novel colorimetric method based on mathematical models, by multiple linear regression (MLR), from the CIE L*a*b* measurements and data of the HPLC determination of food colorants. Calibration set of 10 production batches of finely grinded cooked sausage with food colorants added was manufactured in industrial conditions as follows: one control batch and 9 products with various quantities of added food colorants: E120 (3.4, 7.5 and 12.5 mg/kg), E 124 (5.0, 15.0, 25.0 mg/kg) and E 129 (5.0, 15.0, 25.0 mg/kg). The estimation of the added food colorants was assessed by measuring L*, a*, b* parameters of cross-section. The quantification of food colorants was achieved by HPLC-PDA. Food colorants were extracted from meat products using Accelerated Solvent Extraction (ASE). Quantification of food colorants was achieved in the range from 1 to 100 mg / kg, and recovery values were from 76.15% to 107.04%, for E 120, from 97.61% to 101.03%, for E 124 and from 99.91% to 101.67%, for E 129. Correlation of the results obtained using HPLC and colorimetric measuring data was assessed by Multiple Linear Regression (MLR). The results from colorimetric and chromatographic determinations in four experimental batches (three batches with different quantities of food colorants and one control batch) were used for calibration. Coefficients of determination (R2) for linear models in experimental batches were 0.954, for E 124, 0.987, for E 120 and 0.993, for E 129. Correlation functions of food colorant quantities and corresponding L*a*b* values were established. The obtained mathematical models were tested for the estimation of the content of dyes in 21 samples of finely grinded cooked sausages purchased in retail stores. Food colorants were confirmed in 20 samples (95.24 %), and one sample (4.76 %) did not contain any of these compounds. Out of the positive samples, sixteen samples (80.00 %) contained E 120, while four samples (20.00 %) contained E 129. Food colorant E124 was not established in any of the analyzed samples. Colorimetric CIE L*a*b* method might be used during sensory evaluation of meat products for the assessment of the added food colorants.

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Non-destructive evaluation of the leaf area of garlic crop using mathematical models

Bioscience Journal ◽

10.14393/bj-v36n5a2020-42344 ◽

2020 ◽

Vol 36 (5) ◽

Author(s):

Felipe Augusto Reis Gonçalves ◽

Marcelo de Paula Senoski ◽

Thiago Picinatti Raposo ◽

Leonardo Angelo de Aquino ◽

Maria Elisa de Sena Fernandes

Keyword(s):

Linear Regression ◽

Mathematical Models ◽

Leaf Area ◽

Dry Matter ◽

Linear Models ◽

Coefficient Of Determination ◽

Length Width ◽

Number Of Leaves ◽

Non Destructive

Growth measurements such as leaf area (LA) and dry matter (DM) are important in experiments about plants population, fertilization, irrigation and others parameters of cultivation, in garlic crop. The LA and DM are commonly defined as destructive, lengthy and cause loss of plants in the experimental units. The objective of this study was to fit mathematical models using linear models that estimate the leaf area and dry matter of garlic plants - variety Ito. For that, garlic plants were collected at 30, 45, 60, 75, 90, 115 and 120 days after planting. The measurements of width (W), length (L) of leaves, LA, DM, pseudostem diameter (PD), number of leaves per plant (NL) and height (H) were determined in each time. The models were fitted to estimate the LA or DM as function of the variables W, L, L*W, PD and LA. The statistical analysis of the linear regression, coefficient of determination of the linear regression (R2), root mean square error (RMSE), modified concordance index (d1) and the BIAS index were verified to determine the most representative models. It`s possible to estimate the LA and the leaf DM of garlic plants using the variables: length, width, pseudostem diameter and height of plants.

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Colorimetric Determination of Hydroxyproline as Measure of Collagen Content in Meat and Meat Products: NMKL Collaborative Study

Journal of AOAC INTERNATIONAL ◽

10.1093/jaoac/73.1.54 ◽

1990 ◽

Vol 73 (1) ◽

pp. 54-57 ◽

Cited By ~ 13

Author(s):

Kurt Kolar

Keyword(s):

Collaborative Study ◽

Colorimetric Method ◽

Meat Products ◽

Acid Oxidation ◽

Colorimetric Determination ◽

Mean Values ◽

Collaborative Studies ◽

Freeze Dried ◽

Analytical Result

Abstract A colorimetric method for the determination of hydroxyproline as a measure of collagen in meat and meat products has been collaboratively studied in 18 laboratories. The method includes hydrolysis with sulfuric acid, oxidation with chloramine- T, and formation of a reddish purple complex with 4- dimethylaminobenzaldehyde. Five frozen and 3 freeze-dried samples were tested, ranging in content from 0.11 to 0.88% and from 0.39 to 4.0% hydroxyproline, respectively. The mean values of 2 identical samples were 0.245 and 0.251 %. The average recovery from a spiked sample was 96.1 %. The hydroxyproline content of a known sample (a mixture of 2 samples in the ratio 5:2) was calculated to 1.42%, which agrees well with the analytical result, 1.40%. In comparison with other collaborative studies, based on the ISO analytical method, the repeatability and reproducibility of this method agree well with the other results. This method was accepted as an official NMKL method by all national Committees, and has been adopted official first action by AOAC as an NMKLAOAC method.

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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PROFITABILITAS BANK UMUM SYARIAH DI INDONESIA

Jurnal Ekonomi Syariah Teori dan Terapan ◽

10.20473/vol6iss20198pp1607-1619 ◽

2020 ◽

Vol 6 (8) ◽

pp. 1607

Author(s):

Dian Anggraini ◽

Imron Mawardi

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Commercial Bank ◽

Confidence Level ◽

Linear Regression Analysis ◽

Sampling Technique ◽

Multiple Linear Regression Analysis ◽

Coefficient Of Determination

The purpose of this study is to analyze the factors that affect the profitability of Sharia Commercial Bank in Indonesia. The sampling technique used is saturated sampling, so the population and sample is the Sharia Commercial Bank periode October 2014-March 2018 which amounted to 42 months. This study uses a quantitative approach with multiple linear regression analysis technique.The result showed that with 95% confidence level partially variable Financing to Deposit Ratio have significant effect to profitability, while variable Natural Uncertainty Contract Financing, Non Performing Financing, and Equity have no significant effect on profitability of Sharia Commercial Bank in Indonesia. Nevertheless, Natural Uncertainty Contract Financing, Non Performing Financing, Financing to Deposit Ratio and Equity simultaneously have significant effect to the profitability of Sharia Commercial Bank in Indonesia. With the coefficient of determination of 33,2%.Keywords: Natural Uncertainty Contract, Non Performing Financing, Financing to Deposit Ratio, Equity

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Unbiased Least Squares Regression Coefficients for Multiple Linear Regression Mathematical Models

10.1145/3482632.3484099 ◽

2021 ◽

Author(s):

Yuanchun Guo

Keyword(s):

Linear Regression ◽

Mathematical Models ◽

Multiple Linear Regression ◽

Least Squares ◽

Regression Coefficients ◽

Least Squares Regression

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Modeling factor as the cause of traffic accident losses using multiple linear regression approach and generalized linear models

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/235/1/012030 ◽

2019 ◽

Vol 235 ◽

pp. 012030 ◽

Cited By ~ 2

Author(s):

H Fitrianti ◽

Y P Pasaribu ◽

P Betaubun

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Generalized Linear Models ◽

Traffic Accident ◽

Linear Models ◽

Regression Approach

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Multiple Linear Regression Model of Meningococcal Disease in Ukraine: 1992–2015

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5105120 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Hennadii Mokhort

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Meningococcal Disease ◽

Total Population ◽

Linear Models ◽

National Level ◽

Multiple Linear Regression Model ◽

National Database ◽

Linear Regression Models ◽

Susceptible Population

Estimating the rates of invasive meningococcal disease (IMD) from epidemiologic data remains critical for making public health decisions. In Ukraine, such estimations have not been performed. We used epidemiological data to develop a national database. These data were used to estimate the population susceptible to IMD and identify the prevalence of asymptomatic carriers of N. meningitidis using simple epidemiological models of meningococcal disease that may be used by the national policy makers. The goal was to create simple, easily understood analysis of patterns of the infection within Ukraine that would capture the major features of the infection dynamics. Studies used nationally reported data during 1992–2015. A logic model identified the prevalence of carriage and the proportion of the population susceptible to IMD as key drivers of IMD incidence. Multiple linear regression models for all ages (total population) and for children ≤14 years old were fit to national-level data. Linear models with the incidence of IMD as an outcome were highly associated with carriage and estimated susceptible population in both total population and children (R2 = 0.994 and R2 = 0.978, respectively). The susceptibility rate to IMD in the study total population averaged 0.0034 ± 0.0009% annually. At the national level, IMD can be characterized by the simple interaction between the prevalence of asymptomatic carriage and the proportion of the susceptible population. IMD association with prevalence rates of carriage and the proportion of susceptible population is sufficiently strong for national-level planning of intervention strategies for IMD.

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Fear of falling in older adults living at home: associated factors

Revista da Escola de Enfermagem da USP ◽

10.1590/s1980-220x2016223703215 ◽

2017 ◽

Vol 51 (0) ◽

Cited By ~ 5

Author(s):

Luciano Magalhães Vitorino ◽

Carla Araujo Bastos Teixeira ◽

Eliandra Laís Vilas Boas ◽

Rúbia Lopes Pereira ◽

Naiana Oliveira dos Santos ◽

...

Keyword(s):

Older Adults ◽

Linear Regression ◽

Multiple Linear Regression ◽

Older Adult ◽

Linear Models ◽

Fear Of Falling ◽

Multiple Linear Regression Model ◽

Cross Sectional ◽

Modifiable Factors ◽

At Home

Abstract OBJECTIVE To identify the factors associated with the fear of falling in the older adultliving at home. METHOD Cross-sectional study with probabilistic sampling of older adultenrolled in two Family Health Strategies (FHS). The fear of falling was measured by the Brazilian version of the Falls Efficacy Scale-International and by a household questionnairethat contained the explanatory variables. Multiple Linear Regression using the stepwise selection technique and the Generalized Linear Models were used in the statistical analyses. RESULTS A total of170 older adultsparticipated in the research, 85 from each FHS. The majority (57.1%) aged between 60 and 69; 67.6% were female; 46.1% fell once in the last year. The majority of the older adults(66.5%) had highfear of falling. In the final multiple linear regression model, it was identified that a higher number of previous falls, female gender, older age, and worse health self-assessment explained 37% of the fear of falling among the older adult. CONCLUSION The findings reinforce the need to assess the fear of falling among the older adultliving at home, in conjunction with the development and use ofstrategies based on modifiable factors by professionalsto reduce falls and improve health status, which may contribute to the reduction of the fear of falling among the older adult.

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