Literature DB >> 19216869

Artificial neural networks in foodstuff analyses: Trends and perspectives A review.

Federico Marini1.   

Abstract

Artificial neural networks are a family of non-linear computational methods, loosely inspired by the human brain, that have found application in an increasing number of fields of analytical chemistry and specifically of food control. In this review, the main neural network architectures are described and examples of their application to solve food analytical problems are presented, together with some considerations about their uses and misuses.

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Year:  2009        PMID： 19216869     DOI： 10.1016/j.aca.2009.01.009

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN： 0003-2670            Impact factor:   6.558


  7 in total

1.  Application of gas sensor arrays in assessment of wastewater purification effects.

Authors:  Łukasz Guz; Grzegorz Łagód; Katarzyna Jaromin-Gleń; Zbigniew Suchorab; Henryk Sobczuk; Andrzej Bieganowski
Journal:  Sensors (Basel)       Date:  2014-12-23       Impact factor: 3.576

2.  Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

Authors:  Rocio Berenice Domínguez; Laura Moreno-Barón; Roberto Muñoz; Juan Manuel Gutiérrez
Journal:  Sensors (Basel)       Date:  2014-09-24       Impact factor: 3.576

Review 3.  Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods.

Authors:  Werickson Fortunato de Carvalho Rocha; Charles Bezerra do Prado; Niksa Blonder
Journal:  Molecules       Date:  2020-07-02       Impact factor: 4.411

4.  Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics.

Authors:  Xanthoula Eirini Pantazi; Anastasia L Lagopodi; Afroditi Alexandra Tamouridou; Nathalie Nephelie Kamou; Ioannis Giannakis; Georgios Lagiotis; Evangelia Stavridou; Panagiotis Madesis; Georgios Tziotzios; Konstantinos Dolaptsis; Dimitrios Moshou
Journal:  Sensors (Basel)       Date:  2022-08-10       Impact factor: 3.847

5.  A flow-injection mass spectrometry fingerprinting scaffold for feature selection and quantitation of Cordyceps and Ganoderma extracts in beverage: a predictive artificial neural network modelling strategy.

Authors:  Chee Wei Lim; Siew Hoon Tai; Sheot Harn Chan
Journal:  AMB Express       Date:  2012-08-13       Impact factor: 3.298

6.  Exploring the Scientific Interest for Olive Oil Origin: A Bibliometric Study from 1991 to 2018.

Authors:  Astrid Maléchaux; Yveline Le Dréau; Jacques Artaud; Nathalie Dupuy
Journal:  Foods       Date:  2020-05-01

7.  Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks.

Authors:  Yoshio Makino; Yumi Kousaka
Journal:  Foods       Date:  2020-05-02
  7 in total

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