scholarly journals Different rootstocks, irrigation, and nutritional management on the quality parameters of Montenegrina mandarins (Citrus deliciosa Tenore) cultivated in Vale do Caí region, South Brazil

2021 ◽  
Vol 42 ◽  
pp. e1065
Author(s):  
William Zanardi ◽  
Wendel P. Silvestre ◽  
Gabriel F. Pauletti
Keyword(s):  
Quality Parameters ◽  
Nutritional Management ◽  
Citrus Deliciosa ◽  
South Brazil

scholarly journals Characterization of Mandarin (Citrus deliciosa Ten.) Essential Oil. Determination of Volatiles, Non-Volatiles, Physico-Chemical Indices and Enantiomeric Ratios

2009 ◽  
Vol 4 (11) ◽  
pp. 1934578X0900401 ◽  
Author(s):  
Ivana Bonaccorsi ◽  
Paola Dugo ◽  
Alessandra Trozzi ◽  
Antonella Cotroneo ◽  
Giovanni Dugo
Keyword(s):  
Refractive Index ◽  
Essential Oils ◽  
Optical Rotation ◽  
Quality Parameters ◽  
Volatile Fraction ◽  
Rp Hplc ◽  
Physico Chemical ◽  
Citrus Deliciosa

An investigation of 27 samples of mandarin essential oils (Citrus deliciosa Tenore), industrially produced in Sicily during the 2007-2008 season, was performed to determine the composition of the volatile fraction by GC/FID and GC/MS-LRI, the enantiomeric distribution of some monoterpene hydrocarbons and linalol by Es-GC, the non-volatile oxygen heterocyclic components by RP-HPLC/PDA and the physicochemical indices (relative density, refractive index, optical rotation, residue on evaporation, and UV spectroscopic CD value). This study up-dates the information available in the literature on Sicilian mandarin (C. deliciosa Ten.) essential oils, and provides information on the composition and quality parameters for the evaluation of this product.

Monitoring of essential oils during storage by selected quality parameters

Planta Medica ◽  
2010 ◽  
Vol 76 (12) ◽  
Author(s):  
C Turek ◽  
S Ritter ◽  
F Stintzing
Keyword(s):  
Essential Oils ◽  
Quality Parameters

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Quality of ram spermatozoa separated with modified swim up method

2018 ◽  
Vol 33 (2) ◽  
pp. 62-70 ◽  
Author(s):  
A Hossain ◽  
MM Islam ◽  
F Naznin ◽  
RN Ferdousi ◽  
FY Bari ◽  
...  
Keyword(s):  
Semen Quality ◽  
Membrane Integrity ◽  
Quality Parameters ◽  
Fluid Medium ◽  
Normal Morphology ◽  
The Mean ◽  
Mass Activity ◽  
Fresh Semen ◽  
Artificial Vagina

Semen was collected from four rams, using artificial vagina and viability%, motility% and plasma membrane integrity% were measured. Fresh ejaculates (n = 32) were separated by modified swim-up separation using modified human tubal fluid medium. Four fractions of supernatant were collected at 15-minute intervals. The mean volume, mass activity, concentration, motility%, viability%, normal morphology and membrane integrity% (HOST +ve) of fresh semen were 1.0 ± 0.14, 4.1 ± 0.1 × 109 spermatozoa/ml, 85.0 ± 1.3, 89.4 ± 1.0, 85.5 ± 0.7, 84.7 ± 0.5 respectively. There was no significant (P>0.05) difference in fresh semen quality parameters between rams. The motility%, viability% and HOST +ve % of first, second, third and fourth fractions were 53.4 ± 0.5, 68.2 ± 0.3, 74.8 ± 0.3 and 65.5 ± 0.4; 55.5 ± 0.4, 66.2 ± 0.4, 74.5 ± 0.3 and 73.6 ± 0.3 and 66.7 ± 0.5, 66.8 ± 0.5, 65.2 ± 0.4 and 74.7 ± 0.5 respectively. The motility%, viability% and membrane integrity% of separated semen samples differed significantly (P<0.05) between four fractions. The mean motility% and viability% were significantly higher (P<0.05) in third fraction (74.8 ± 0.3%), whereas the mean HOST +ve% was significantly higher (P<0.05) in fourth fraction (74.7 ± 0.5). All quality parameters of separated spermatozoa were significantly (P<0.05) lower than that of fresh semen. The pregnancy rates were higher with fresh semen (71%) in comparison to that of separated sample (57%).Bangl. vet. 2016. Vol. 33, No. 2, 62-70

Investigation of Some Water Quality Parameters of Pond Water under Mymensingh Municipality

2015 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
F Zannat ◽  
MA Ali ◽  
MA Sattar
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Chemical Properties ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Urban Region ◽  
Mn Toxicity ◽  
Ph Values ◽  
Drinking Water Standard

A study was conducted to evaluate the water quality parameters of pond water at Mymensingh Urban region. The water samples were collected from 30 ponds located at Mymensingh Urban Region during August to October 2010. The chemical analyses of water samples included pH, EC, Na, K, Ca, S, Mn and As were done by standard methods. The chemical properties in pond water were found pH 6.68 to 7.14, EC 227 to 700 ?Scm-1, Na 15.57 to 36.00 ppm, K 3.83 to 16.16 ppm, Ca 2.01 to 7.29 ppm, S 1.61 to 4.67 ppm, Mn 0.33 to 0.684 ppm and As 0.0011 to 0.0059 ppm. The pH values of water samples revealed that water samples were acidic to slightly alkaline in nature. The EC value revealed that water samples were medium salinity except one sample and also good for irrigation. According to drinking water standard Mn toxicity was detected in pond water. Considering Na, Ca and S ions pond water was safe for irrigation and aquaculture. In case of K ion, all the samples were suitable for irrigation but unsuitable for aquaculture.J. Environ. Sci. & Natural Resources, 8(1): 85-89 2015

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