scholarly journals Effect of incorporating roasted sesame (Sesamum indicum) seeds on the quality parameters of chicken nuggets

Food and Life ◽  
2021 ◽  
Author(s):  
Shan Nawarathne ◽  
Dinesh Jayasena ◽  
Prabhathma Rathnayake ◽  
Manjula Senavirathna ◽  
Damith Udayanga ◽  
...  
Keyword(s):  
Quality Parameters ◽  
Sesamum Indicum ◽  
Chicken Nuggets

scholarly journals Assessment of Agroeconomic Indicators of Sesamum indicum L. as Influenced by Application of Boron at Different Levels and Plant Growth Stages

Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6699
Author(s):  
Salwinder Singh Dhaliwal ◽  
Vivek Sharma ◽  
Arvind Kumar Shukla ◽  
Vibha Verma ◽  
Sanjib Kumar Behera ◽  
...  
Keyword(s):  
Oil Content ◽  
Application Rate ◽  
Quality Parameters ◽  
Sesamum Indicum ◽  
Cost Ratio ◽  
Growth Stages ◽  
Water Spray ◽  
Maximum Increase ◽  
Capsule Formation ◽  
B Uptake

To achieve the nutritional target of human food, boron (B) has been described as an essential mineral in determining seed and theoretical oil yield of Sesamum indicum L. The research to increase its cultivation is garnering attention due to its high oil content, quality and its utilization for various purposes, which include human nutrition as well as its use in the food industry. For this, a two-year field experiment was performed at PAU, Punjab, India to determine the effect of different concentrations of foliar-applied B (20, 30 and 40 mg L−1) and different growth stages of crop, i.e., we measured the effects on agroeconomic indicators and certain quality parameters of sesame using different concentrations of B applied at the flowering and capsule formation stages as compared to using water spray and untreated plants. Water spray did not significantly affect the studied parameters. However, B application significantly increased the yield, uptake, antioxidant activity (AOA) and theoretical oil content (TOC) compared to those of untreated plants. The maximum increase in seed yield (26.75%), B seed and stover uptake (64.08% and 69.25%, respectively) as well as highest AOA (69.41%) and benefit to cost ratio (B:C ratio 2.63) was recorded when B was applied at 30 mg L−1 at the flowering and capsule formation stages. However, the maximum sesame yield and B uptake were recorded when B was applied at a rate of 30 mg L−1. A significant increase in TOC was also recorded with a B application rate of 30 mg L−1. For efficiency indices, the higher values of boron agronomic efficiency (BAE) and boron crop recovery efficiency (BCRE) were recorded when B was applied at 20 mg L−1 (5.25 and 30.56, respectively) and 30 mg L−1 (4.96 and 26.11, respectively) at the flowering and capsule formation stages. In conclusion, application of B @ 30 mg L−1 at the flowering and capsule formation stages seemed a viable technique to enhance yield, B uptake and economic returns of sesame.

scholarly journals Evaluation of Bioagents and Biofertilizers for the Managament of Seed and Seedling Diseases of Sesamum indicum (Sesame)

2013 ◽  
Vol 2 (3) ◽  
pp. 179-186 ◽  
Author(s):  
Venturla Bharathi ◽  
Ravuru Sudhakar ◽  
K. Parimala ◽  
Vishnuvardhan A. Reddy
Keyword(s):  
Andhra Pradesh ◽  
Seed Quality ◽  
Pathogenic Fungi ◽  
Germination Percentage ◽  
Quality Parameters ◽  
Sesamum Indicum ◽  
Potato Dextrose Agar ◽  
Seedling Mortality ◽  
Sesame Seeds ◽  
Agar Media

The study was carried out to evaluate the response of biopeticides and biofertilizers on seed mycoflora and seed quality parameters of Sesame (Sesamum  indicum  L.). Untreated Sesame seeds were collected from farmers of Nizamabad and Karimanagar districts of Andhra Pradesh in India and discolored seeds were separated and treated with biofertilizers and biopesticides alone and in combination form. The seed mycoflora of Sesame seeds were screened by using Potato dextrose agar (PDA) medium and czaepek dox agar media. The results indicate that maximum numbers of fungi were recorded on PDA. The untreated seeds were found to be associated with maximum percent incidence of mycoflora and minimum population was recorded in the treatment of Trichoderma + Pseudomonas formulation followed by Azat obacter + Trichoderma, Pseudomonas and Azatobacter in the decreasing order of efficacy. This study also showed relation of biofertilizers and biopesticides and seed mycoflora on seed germination. Germination percentage was maximum in the treatment Trichoderma + Pseudomonas formulation, Azatobacter + Trichoderma, Pseudomonas and Azatobacter recording 96%, 94%, 90% and 88%, respectively. In the control, germination percentage was minimum compared with other treatments. Seeds treated with the mixed formulation were found beneficial in reducing the pathogenic fungi and decreasing seedling mortality.

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.

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