Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index

2018 ◽  
Vol 85 (1) ◽  
pp. 78-82
Author(s):  
Athanasios I Gelasakis ◽  
Rebecca Giannakou ◽  
Georgios E Valergakis ◽  
Paschalis Fortomaris ◽  
Antonios Kominakis ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Refractive Index ◽  
Milk Yield ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Milk Quality ◽  
Regression Equations ◽  
Initial Development ◽  
Total Solids ◽  
Solids Content

This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.

Relation Between Refractive Index, Specific Gravity, and Total Solids of Tomato Juice, Puree, and Paste

1967 ◽  
Vol 50 (3) ◽  
pp. 690-700
Author(s):  
Frank C Lamb
Keyword(s):  
Refractive Index ◽  
Specific Gravity ◽  
Tomato Juice ◽  
Regression Equations ◽  
Total Solids ◽  
Pectic Enzymes ◽  
Tomato Paste ◽  
Aoac Method ◽  
Solids Content ◽  
Separate Regression

Abstract Total solids by drying, refractive index, and specific gravity were determined on about 375 commercial samples of tomato juice, puree, and paste. Refractive index was determined with and without dilution of tomato paste; pectic enzymes were used to aid filtration and centrifugation. A new specific gravity bottle was used. The new AOAC method for total solids was compared with the former AOAC method on 115 samples. Variations from previous tables relating refractive index and total solids were of little significance up to 20% solids but were increasingly greater as solids increased above 20%. Data obtained in these studies showed lower values for total solids than the old tables in most instances. Separate regression equations had to be calculated for the solids content of the diluted and undiluted samples. Total solids by the official AOAC method was the most precise of the methods used. However, specific gravity and refractive index were both found to have satisfactory degrees of precision

scholarly journals PENGARUH LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN MAJENE

2020 ◽  
Vol 9 (2) ◽  
pp. 121
Author(s):  
Sri Indira Hartawati ◽  
Meutia A Sahur
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Work Environment ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Youth Sports ◽  
Multiple Linear Regression Analysis ◽  
Department Of Education ◽  
Test Reliability ◽  
Regression Equations

<p><em>This research was conducted at the Department of Education, Youth and Sports of Majene Regency with the title The Effect of Work Environment and Compensation on Employee Performance. The formulation of the problem used by researchers is How the influence of the Work Environment on Employee Performance at the Education and Youth Sports Office of Majene Regency, How the influence of Compensation on Employee Performance at the Education and Youth Sports Office of Majene Regency, which variables have more influence on Employee Performance at the Education and Youth Office Majene District Sports. The research method, namely the population and sample used in this study were all employees of the Department of Education and Youth Sports of Majene Regency, which amounted to about 50 people, while the analysis method used the Validity Test, Reliability Test, Multiple Linear Regression Analysis This analysis was used to determine how much influence it had. independent variables, namely: compensation (X1), and work environment (X2) on the dependent variable, namely Employee Performance (Y). Multiple linear regression equations, Partial Significance Test (t test) and Simultaneous Test (F test). The results obtained from this study are the work environment has a significant effect on employee performance at the Department of Education and Youth Sports of Majene Regency, compensation has an effect on employee performance at the Education and Youth Sports Office of Majene Regency. and the work environment has a more dominant influence on employee performance at the Department of Education and Youth Sport, Majene Regency.</em></p><p><strong><em>Keywords: </em></strong><em>Work Environment, Compensation, Employee Performance</em></p>

scholarly journals The chemical composition and technological properties of milk obtained from the morning and evening milking

2008 ◽  
Vol 56 (5) ◽  
pp. 187-198 ◽  
Author(s):  
Martin Skýpala ◽  
Gustav Chládek
Keyword(s):  
Protein Content ◽  
Milk Yield ◽  
Milk Fat ◽  
Protein Production ◽  
Milk Protein ◽  
Fat Content ◽  
Total Solids ◽  
Milk Protein Content ◽  
Solids Content ◽  
Morning Milk

Milk yield varies during lactation, following what is termed a lactation curve. ŽIŽLAVSKÝ and MIKŠÍK (1988) recorded changes in milk yield within a day, too. TEPLÝ et al. (1979) a KOUŘIMSKÁ et al. (2007) published variation within a day ± 1.10 kg in milk yield, ± 0.75 % in milk fat content and ± 0.20 % in milk protein content. Milk yield of cows can be expressed in many different ways, for instance, in kilograms per lactation or in kilograms per day. A practical parameter describing milk production is milk yield (kg) per milking.The object of experiment were 12 cows of Holstein cattle on the first lactation from the 100-day of lactation to 200-day of lactation. The samples of milk were collected from January to May 2007, once a month from the morning and evening milking (milking interval 12 h ± 15 min.). The following parameters were monitored: milk production – milk yield (kg), milk protein production (kg), milk fat production (kg); milk composition – milk protein content (%), milk fat content (%), lactose content (%), milk solids-not-fat content (%), milk total solids content (%); technological properties of milk – ti­tra­tab­le acidity (SH), active acidity (pH), rennet coagulation time (s), quality of curd (class) and somatic cell count as a parameter of udder health.Highly significant differences were found (P < 0.01) between morning milk yield (15.7 kg) and evening milk yield (13.8 kg), between morning milk protein production (0.51 kg) and evening milk protein production (0.45 kg) and between evening milk fat content (4.41 %) and morning milk fat content (3.95 %). A significant difference (P < 0.05) was found between morning milk total solids content (12.62 %) and evening milk total solids content (12.07 %). No significant differences were found between morning (M) and evening (E) values of the remaining parameters: milk fat production (M 0.62 kg; E 0.60 kg), milk protein content (M 3.24 %; E 3.27 %), milk lactose content (M 4.78 %; E 4.86 %), milk solids-not-fat content (M 7.69 %; E 7.71 %), somatic cell count (M 80 000/1 mL; E 101 000/1 mL), titratable aci­di­ty (M 7.75 SH; E 7.64 SH), active acidity (M pH 6.58; E pH 6.61), rennet coagulation time (M 189 s.; E 191 s.), quality of curd (M 1.60 class; E 1.57 class).

scholarly journals Cluster and regression analysis for predicting salinity in groundwater

2018 ◽  
Vol 192 ◽  
pp. 02007
Author(s):  
Phiraphat Aphiphan ◽  
Uma Seeboonruang ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Electrical Conductivity ◽  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Water Levels ◽  
Groundwater Salinity ◽  
Groundwater Levels

Groundwater salinity is a major problem particularly in the northeastern region of Thailand. Saline groundwater can cause widespread saline soil problem resulting in reducing agricultural productivity as in the Lower Nam Kam River Basin. In order to better manage the salinity problem, it is important to be able to predict the groundwater salinity. The objective of this research was to create a cluster-regression model for predicting the groundwater salinity. The indicator of groundwater salinity in this study was electrical conductivity because it was simple to measure in field. Ninety-eight parameters were measured including precipitation, surface water levels, groundwater levels and electrical conductivity. In this study, the highest groundwater salinity at 3 wells was predicted using the combined cluster and multiple linear regression analysis. Cross correlation and cluster analysis were applied in order to reduce the number of parameters to effectively predict the quality. After the parameter selection, multiple linear regression was applied and the modeling results obtained were R2 of 0.888, 0.918, and 0.692, respectively. This linear regression model technique can be applied elsewhere in the similar situation.

scholarly journals Towards reproducibility of traditional fermented sausages: Texture profile analyses and modelling

2020 ◽  
Vol 26 (1) ◽  
pp. 79-87
Author(s):  
Marija Jokanovic ◽  
Bojana Ikonic ◽  
Predrag Ikonic ◽  
Vladimir Tomovic ◽  
Tatjana Peulic ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Linear Regression Analysis ◽  
Sensory Characteristics ◽  
Small Scale ◽  
Regression Equations ◽  
Textural Characteristics ◽  
Fermented Sausages

The aim of this study was to investigate textural characteristics of three traditional dry fermented sausages (Sremski kulen, Lemeski kulen and Petrovsk? klob?sa) manufactured in different small-scale facilities in northern Serbia, and to correlate them with physicochemical and sensory characteristics. The sample sausages were supplied by different local traditional producers. The textural characteristics were correlated with physicochemical and sensory characteristics using multiple linear regression analysis and principal component analysis. Differences in physicochemical characteristics reflected even more notable differences in texture characteristics. Regarding regression equations, obtained results showed that moisture content was significant for hardness, springiness and cohesiveness. Hardness was also influenced by fat content, while chewiness was influenced by protein content. Principal component analysis separated samples of Petrovsk? klob?sa, as the group with the most reproducible analysed characteristics. Obtained results of statistical analyses should provide knowledge for possible improvements of the traditional production, in a way that these sausages could be produced in different facilities with consistent textural characteristics.

scholarly journals A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

2011 ◽  
Vol 51 (2) ◽  
pp. 191-204 ◽  
Author(s):  
Brian J. Cerruti ◽  
Steven G. Decker
Keyword(s):  
Linear Regression Analysis ◽  
Model Performance ◽  
Forecast Model ◽  
Electrical Equipment ◽  
Multiple Linear Regression Analysis ◽  
Skill Score ◽  
Electric Utility ◽  
Public Utility ◽  
Regression Equations ◽  
Surface Observations

AbstractA generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.

scholarly journals DFT Based QSAR Study of Enzyme Ribonucleoside Diphosphate Reductase

2010 ◽  
Vol 7 (3) ◽  
pp. 953-961 ◽  
Author(s):  
Mohiuddin Ansari ◽  
Ahmad Khalid Raza Khan ◽  
Suhail Ahmad Khan
Keyword(s):  
Total Energy ◽  
Chemical Potential ◽  
Linear Regression Analysis ◽  
Dft Method ◽  
Multiple Linear Regression Analysis ◽  
Heat Of Formation ◽  
Regression Equations ◽  
Quantum Chemical Descriptors ◽  
Qsar Study ◽  
Ribonucleoside Diphosphate Reductase

Quantum chemical descriptors such as heat of formation, energy of HOMO, total energy, absolute hardness and chemical potential in different combinations have been used to develop QSAR models of inhibitors of enzyme ribonucleoside diphosphate reductase, RDR. The inhibitors are mainly derivatives of 1-formylisoquinoline thiosemicarbazone and 2-formylpyridine thiosemicarbazone. The values of various descriptors have been evaluated with the help of Win MOPAC 7.21 software using DFT method. Multiple linear regression analysis has been made with the help of above mentioned descriptors using the same software. Regression equations have been found to be successful models as indicated by the regression coefficientr2having the value as high as 0.96 and cross validation coefficientrCV2having the value approaching 0.95. The value of these two coefficients is indicative of high order of reliability for the proposed prediction. The results obtained are also validated on account of the closeness of observed and predicted inhibitory activities. The best combination of descriptors is heat of formation, total energy and energy of HOMO. Thus the prediction of suitability of inhibitors of the enzyme RDR can be made with the help of the best regression equation.

Quantitative control of flowering time in wheat cultivars by vernalization and photoperiod sensitivities

1973 ◽  
Vol 24 (5) ◽  
pp. 657 ◽  
Author(s):  
JR Syme
Keyword(s):  
Environmental Changes ◽  
Day Treatment ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Regression Equations ◽  
Wheat Cultivars ◽  
Sowing Time ◽  
P Values ◽  
Long Day ◽  
Cultivar Variation

Vernalization sensitivity (V) and photoperiod sensitivity (P) were measured in a range of wheat cultivars by the hastening of development brought about by seed vernalization and long-day treatment, respectively. Using multiple linear regression analysis V and P accounted for 77-94 % of cultivar variation in the time from sowing to ear emergence, or to anthesis, over 19 sowings at three sites. From the regression equations, the optimum combination of P and V for adaptation to variable sowing time was estimated. In two cases this was predicted to be a wheat with high V and low P values. Seasonal changes in the coefficients of the multiple regression equations were related to environmental changes in temperatures and photoperiod.

scholarly journals A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Guanjun Zhang ◽  
Qin Qin ◽  
Zheng Chen ◽  
Zhonghao Bai ◽  
Libo Cao
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Statistical Significance ◽  
Head Injuries ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Design Stage ◽  
Regression Equations ◽  
Feature Parameters ◽  
Front End

Background. The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods. Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results. The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions. The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians’ heads and provide styling designers with technical guidance regarding their artistic creations.

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