Registered Designation of Origin Areas of Fermented Food Products Defined by Microbial Phenotypes and Artificial Neural Networks

1999 ◽  
Vol 65 (10) ◽  
pp. 4484-4489 ◽  
Author(s):  
M. F. S. Lopes ◽  
C. I. Pereira ◽  
F. M. S. Rodrigues ◽  
M. P. Martins ◽  
M. C. Mimoso ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Raw Materials ◽  
Food Products ◽  
Fermented Food ◽  
Fermented Foods ◽  
Fermented Sausage ◽  
Local Environments ◽  
Artificial Neural ◽  
Counterfeit Products

ABSTRACT Cheese produced from raw ewes’ milk andchouriço, a Portuguese dry fermented sausage, are still produced in a traditional way in certain regions of Portugal by relying on colonization by microbial populations associated with the raw materials, equipment, and local environments. For the purpose of describing the product origins and types of these fermented foods, metabolic phenotypes can be used as descriptors of the product as well as to determine the presence of compounds with organoleptic value. The application of artificial neural networks to the metabolic profiles of bacterial isolates was assayed and allowed the separation of products from different regions. This method could then be used for the Registered Designation of Origin certification process of food products. Therefore, besides test panel results for these traditionally produced food products, another tool for validating products for the marketplace is available to the producers. The method can be improved for the detection of counterfeit products.

Optimisation of the addition of carrot dietary fibre to a dry fermented sausage (sobrassada) using artificial neural networks

Meat Science ◽  
2013 ◽  
Vol 94 (3) ◽  
pp. 341-348 ◽  
Author(s):  
Valeria S. Eim ◽  
Susana Simal ◽  
Carmen Rosselló ◽  
Antoni Femenia ◽  
José Bon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dietary Fibre ◽  
Fermented Sausage ◽  
Dry Fermented Sausage ◽  
Artificial Neural

Predicting Survival of Escherichia coli O157:H7 in Dry Fermented Sausage Using Artificial Neural Networks

2008 ◽  
Vol 71 (1) ◽  
pp. 6-12 ◽  
Author(s):  
A. PALANICHAMY ◽  
D. S. JAYAS ◽  
R. A. HOLLEY
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Model ◽  
Allyl Isothiocyanate ◽  
Escherichia Coli O157 ◽  
Fermented Sausage ◽  
E Coli ◽  
Dry Fermented Sausage ◽  
Artificial Neural

The Canadian Food Inspection Agency required the meat industry to ensure Escherichia coli O157:H7 does not survive (experiences ≥ 5 log CFU/g reduction) in dry fermented sausage (salami) during processing after a series of foodborne illness outbreaks resulting from this pathogenic bacterium occurred. The industry is in need of an effective technique like predictive modeling for estimating bacterial viability, because traditional microbiological enumeration is a time-consuming and laborious method. The accuracy and speed of artificial neural networks (ANNs) for this purpose is an attractive alternative (developed from predictive microbiology), especially for on-line processing in industry. Data from a study of interactive effects of different levels of pH, water activity, and the concentrations of allyl isothiocyanate at various times during sausage manufacture in reducing numbers of E. coli O157:H7 were collected. Data were used to develop predictive models using a general regression neural network (GRNN), a form of ANN, and a statistical linear polynomial regression technique. Both models were compared for their predictive error, using various statistical indices. GRNN predictions for training and test data sets had less serious errors when compared with the statistical model predictions. GRNN models were better and slightly better for training and test sets, respectively, than was the statistical model. Also, GRNN accurately predicted the level of allyl isothiocyanate required, ensuring a 5-log reduction, when an appropriate production set was created by interpolation. Because they are simple to generate, fast, and accurate, ANN models may be of value for industrial use in dry fermented sausage manufacture to reduce the hazard associated with E. coli O157:H7 in fresh beef and permit production of consistently safe products from this raw material.

scholarly journals Application of artificial neural networks in the design of biomedical materials

2021 ◽  
Vol 233 ◽  
pp. 02003
Author(s):  
Yu Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Raw Materials ◽  
Material Design ◽  
Research Field ◽  
The Body ◽  
Biomedical Science ◽  
Biomedical Materials ◽  
Normal Human ◽  
Artificial Neural

Biomedical science is a scientific field that includes the intersection of multiple technologies, combining the theoretical methods of biology, medicine and engineering. Biomedical materials are now a branch of the body that studies materials that are adapted to the body’s functioning to ensure normal human activity. Because of its closely related to human activities, it has become an important research field in our time. Therefore, the purpose of this paper is to explore the application of artificial intelligence in the design of biological science materials. Therefore, in the case of using high-quality materials, the material design is improved and optimized by using artificial neural networks under the basis of adverse rejection reactions to the properties of raw materials. The experimental results show that artificial neural network can be better connected and reaction, which is beneficial to improve sensitivity and use emergency measures to deal with it.

Fabrication and Wear Behavior Investigation of the Carbon/Epoxy Composites Based on Wood Using Artificial Neural Networks

2011 ◽  
Vol 413 ◽  
pp. 95-102 ◽  
Author(s):  
Hossein Vafaeenezhad ◽  
Seyed Mojtaba Zebarjad ◽  
Jalil Vahdati Khaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wear Behavior ◽  
Raw Materials ◽  
Normal Load ◽  
Carbon Matrix ◽  
Cost Effective ◽  
Quercus Suber ◽  
Wear Properties ◽  
Artificial Neural

Since wood is the main component of the applied raw materials, it can be used as matrix in carbon composites, also it can be taken into consideration as a cost effective advanced application and have this potential to suppress many expensive fabrication and finishing procedures. Wood samples from Oak tree (Quercus suber) were heated at different temperatures to produce porous carbon templates. Subsequently, the Carbonized wood was infiltrated with an epoxy in order to fabricate the final carbon/epoxy composite. Scanning electron microscopy was used to elucidate parameters affecting on microstructure and wear properties of products. In this context, artificial neural networks (ANN) and design of experiments method (DOE) was implemented to analyze the wear performance of a new class of cellulose based composites. This work indicates that epoxy shows good reinforcement characteristics as it improves the sliding wear resistance of the carbon matrix and that factors like carbonization temperature, sliding distance and normal load are the important factors affecting the wear behaviors.

scholarly journals Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7625
Author(s):  
Izabela Rojek ◽  
Dariusz Mikołajewski ◽  
Piotr Kotlarz ◽  
Krzysztof Tyburek ◽  
Jakub Kopowski ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
3D Printing ◽  
Material Selection ◽  
Raw Materials ◽  
Tensile Force ◽  
Patient Specific ◽  
Print Quality ◽  
Artificial Neural

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.

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