scholarly journals Moisture Sorption Isotherms and Isotherm Model Performance Evaluation for Food and Agricultural Products

2020 ◽  
Author(s):  
Ndubisi A. Aviara
Keyword(s):  
Performance Evaluation ◽  
Model Performance ◽  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Agricultural Products ◽  
Isotherm Model ◽  
Model Performance Evaluation ◽  
Moisture Sorption Isotherms

scholarly journals Moisture sorption isotherms of castor beans. Part 2: Thermodynamic properties

2016 ◽  
Vol 20 (8) ◽  
pp. 757-762 ◽  
Author(s):  
André L. D. Goneli ◽  
Paulo C. Corrêa ◽  
Gabriel H. H. de Oliveira ◽  
Ana P. L. R. de Oliveira ◽  
Roberto C. Orlando
Keyword(s):  
Thermodynamic Properties ◽  
Water Sorption ◽  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Agricultural Products ◽  
Salt Solutions ◽  
Abstract Knowledge ◽  
Differential Enthalpy ◽  
Moisture Sorption Isotherms ◽  
Humidity Environment

ABSTRACT Knowledge on the water sorption within agricultural products is extremely important for decision making during post-harvest procedures. In order to improve this knowledge, thermodynamic properties regarding water sorption provide useful data. Thermodynamic properties of castor beans, related to sorption, were determined. Static gravimetric technique under different conditions of temperature (25, 35, 45 and 55 ± 1 °C) was used. Saturated salt solutions in the range of 37-87% ± 2% were utilized to create the required controlled humidity environment. After the sorption procedure, differential enthalpy, differential entropy and Gibbs free energy of this process were calculated and decreased with increased values of equilibrium moisture content.

scholarly journals Moisture sorption isotherms and heat of sorption of Algerian bay leaves (Laurus nobilis)

2015 ◽  
Vol 17 (4) ◽  
pp. 759-772 ◽  
Author(s):  
N Ouafi ◽  
H Moghrani ◽  
N Benaouada ◽  
N Yassaa ◽  
R Maachi ◽  
...  
Keyword(s):  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Laurus Nobilis ◽  
Heat Of Sorption ◽  
Moisture Sorption Isotherms

scholarly journals Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

2021 ◽  
Vol 4 (3) ◽  
pp. 251524592110268
Author(s):  
Roberta Rocca ◽  
Tal Yarkoni
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Model Evaluation ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Scientific Models ◽  
Practical Utility ◽  
Model Performance Evaluation ◽  
Unseen Data ◽  
Reliable Model

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models’ ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

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