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23 results for “shelf life”
Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period<br> of storage (days) for which the processed cheese has been in the shelf can be determined based on<br> sensory score (Fig. 5).</p>
Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The Neural Network Toolbox under MATLAB software was used for developing the TDNN<br> models. Training pattern of TDNN models is presented in Fig.2.</p>
Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for<br> training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate<br> count, yeast & mould count, and spore count were taken as input parameters, and sensory score as<br> output parameter for developing TDNN single and multilayer models (Fig.1).</p>
Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Immunogenicity and Safety of Tetravalent Dengue Vaccine (TDV) at the End of Shelf Life in Healthy Adults
ClinicalTrials.gov study NCT03771963. IPD Sharing: YES. Countries: 1. Publications: 2.
Census of Marine Life: Pacific Ocean Shelf Tracking
The Pacific Ocean Shelf Tracking (POST) Project furthers understanding of the behaviour of marine animals through the operation of a large-scale ocean telemetry and data management system. POST serves as an accessible research tool for academe, resource agencies and the public. Long-term monitoring of marine animals contributes to the conservation and stewardship of marine resources.
ListeriaPredict Webinar - Application of novel predictive microbiology techniques to shelf-life studies on Listeria monocytogenes
<p>An EFSA funded ListeriaPredict Webinar - Application of novel predictive microbiology techniques to shelf-life studies on Listeria monocytogenes</p>
Data from: Spatial structuring and life history connectivity of Antarctic silverfish along the southern continental shelf of the Weddell Sea
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Data from: Shelf life and quality of tomato (Lycopersicon esculentum Mill.) fruits as affected by neem leaf extract dipping and beeswax coating
<p>The data was generated to investigate the effect of Beeswax (BW) coating and Neem leaf extract (NLE) dipping on the shelf life and quality of tomatoes (<em>Lycopersicon esculentum</em> Mill.) over a storage period of 36 days. A factorial combination of four levels of Neem plant extract (control, 15%, 20% and 25%) and four levels of beeswax coating (control, 3%, 6% and 9%) storage treatments with three replications were applied on fully matured green tomatoes in the study. The treatments were arranged in a randomized complete block design. The average storage room air temperature and relative humidity varied from 15.2 ºC to 20.4 ºC and 55.53% to 69.46% RH during 36 days of storage period at Haramaya University from February to April 2020. Data were recorded on 4, 8, 12, 16, 20, 24, 28, 32, and 36 days after storage. Data on physiological loss in weight, chemical compositions (total soluble solids, pH, titratable acidity, and ascorbic acid), decay (%), percentage marketability, and shelf life were assessed at an interval of four days during 36 days of storage period under ambient conditions.</p>
Wildfire extends the shelf-life of elk nutritional resources regardless of fire severity
<p>Large-scale, high severity wildfires are increasingly frequent across the western United States. Fire severity affects the amount of vegetation removed and helps dictate what, where, and how many plants regenerate postfire, potentially altering the available habitat and nutritional landscape for wildlife. To evaluate the effects of fire severity on summer nutritional resources for elk (Cervus canadensis), we collected field data and remotely sensed information in years two and three after a large-scale wildfire to compare forage quality and quantity across forest types and fire severities within the summer range of one elk population in west-central Montana. To understand the landscape level effects of fire severity on nutritional resources, we developed predictive forage quality and quantity models. We used these models to predict nutritional resources across the landscape for four scenarios representing different fire severity patterns (i.e., an unburned landscape, a landscape burned only at low severity, a landscape burned only at high severity, and the observed landscape burned at a mixed severity). Shortly after the wildfire, summer forage quality and herbaceous forage quantity increased in both mesic and dry mixed conifer forests regardless of fire severity. Summer shrub forage quantity was greater in unburned mesic and dry forests, and there was no difference between fire severities in dry forests. Low severity burned mesic forests had significantly greater shrub forage quantity compared to high severity burned mesic forests. The three predicted fire scenarios had the highest percentage of the summer range with adequate forage quality which increased throughout the summer. In contrast, the predicted unburned landscape had the lowest percentage of adequate forage quality which decreased throughout the summer. Wildfire extended the duration in which elk can access high quality forage in the summer in years two and three postfire. Therefore, shortly after a large-scale wildfire, elk may be better able to meet their nutritional requirements which may positively impact elk body condition, reproductive performance, and survival.</p>
Trophallergen Prick Tests (PT) : Influence of Food Sample Shelf Life on the Reproducibility of PT Results
ClinicalTrials.gov study NCT06872996. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Data from: Shelf life and quality of tomato (Lycopersicon esculentum Mill.) fruits as affected by neem leaf extract dipping and beeswax coating
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Wildfire extends the shelf-life of elk nutritional resources regardless of fire severity
Open the record for dataset details and reuse information.
Shelf Life
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A Study of an Ad26.RSV.PreF-based Regimen at the End of Shelf-life in Adults Aged 60 to 75 Years
ClinicalTrials.gov study NCT05101486. IPD Sharing: YES. Countries: 1. Publications: 0.
Provitamin A-enriched Golden Cassava generated through metabolic engineering has reduced dry matter, elevated oil content and enhanced shelf-life
GEO Series GSE100319. Manihot esculenta. 9 samples. Type: Expression profiling by high throughput sequencing.
Examining preharvest genetic and morphological factors contributing to lettuce (Lactuca sativa L.) shelf life
GEO Series GSE226302. Lactuca sativa. 9 samples. Type: Expression profiling by high throughput sequencing.
ENTact™ Septal Stapler Shelf Life Extension
ClinicalTrials.gov study NCT00957502. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Health Related Quality of Life Effects of Off-the-shelf Computer Gaming in Alzheimer and Related Disorders Populations
ClinicalTrials.gov study NCT01416012. IPD Sharing: Not stated. Countries: 3. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.