Orange juice classification with a biologically based neural network

Dettmar, Hans P.; Barbour, Garth S.; Blackwell, Kim T.; Vogl, Thomas P.; Alkon, Daniel L.; Fry, Fred S.; Totah, Joseph E.; Chambers, Terrence L.

doi:10.1016/0097-8485(95)00015-1

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…Subsequently ICP-MS data has been used to differentiate orange juices from six or more different countries. 4,5 This application has been extended to studies of wine vintage, when trace metal data has been combined with 18 O, 2 H and 13 C data to discriminate between wines grown in the same year but different regions in the same country. [6][7][8] Multivariate analysis of metal data has also been used to classify honey type, 9 geographical origin of potatoes 10 and origin of macadamia and pistachio nuts as part of a programme to prevent imports from countries on which sanctions have been imposed.…”

Section: Introductionmentioning

confidence: 99%

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry

Branch

Burke

Evans³

et al. 2003

J. Anal. At. Spectrom.

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Section: Introductionmentioning

confidence: 99%

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry

Branch

Burke

Evans³

et al. 2003

J. Anal. At. Spectrom.

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“…The use of AI techniques in studying the composition of food products and testing their originality dates back to the 1990s. Dettmar et al used the ANN technique to identify the region of origin of fruit from a set of 16 variables characterizing samples of orange juice [23]. The effectiveness of the applied calculation technique was 92.5%.…”

Section: Ai In Food Composition Studymentioning

confidence: 99%

Artificial Intelligence in Nutrients Science Research: A Review

Sak

Suchodolska

2021

Nutrients

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Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

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“…The DYSTAL199 network has been successfully used to classify orange juice as either adulterated or unadulterated. 200 A learning vector quantizer (LVQ) network has been used to identify multiple analytes from optical sensor array data.201 A wavelet ANN has been applied to the inclusion of P-cyclodextrin with benzene derivatives,202 and a Practical Considerations in Solving Problems with Neural Networks 101 potential function based ANN has been used to classify complex chemical patterns. 203 Genetic algorithms204 have been combined with backpropagation networks to produce optimal structural descriptors and to optimize network training.…”

Section: Miscellaneous Networkmentioning

confidence: 99%

Section: Miscellaneous Networkmentioning

confidence: 99%

Artificial Neural Networks and Their use in Chemistry

Peterson¹

2000

Reviews in Computational Chemistry

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INTRODUCTIONChemists are being confronted with a confounding array of data at an ever increasing pace. The advent of new experimental techniques, the development of cheaper, faster, and more precise instrumentation, and the availability of desktop computing power that only a decade ago would have filled a small house have all contributed to this situation. Medicinal chemists using combinatorial chemistry methods have generated huge libraries of chemical compounds that must be assessed for pharmaceutical activity. Spectroscopists search and analyze huge databases of spectra. Computational chemists generate vast numbers of points describing potential energy surfaces in n-dimensional spaces.The issue facing chemists today is not how to generate data, which not so long ago was actually quite difficult and time-consuming, but how to extract useful information from the data generated. As a consequence, a branch of chemistry known as chemometrics began to evolve in the late 1960s.l Chemometrics is broadly concerned with the extraction of useful information from chemical data. Until about 1990, chemometrics primarily involved appli-

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Orange juice classification with a biologically based neural network

Cited by 16 publications

References 4 publications

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry

Artificial Intelligence in Nutrients Science Research: A Review

Artificial Neural Networks and Their use in Chemistry

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Orange juice classification with a biologically based neural network

Cited by 16 publications

References 4 publications

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with13C,15N mass spectrometry

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with13C,15N mass spectrometry

Artificial Intelligence in Nutrients Science Research: A Review

Artificial Neural Networks and Their use in Chemistry

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A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry

A preliminary study in determining the geographical origin of wheat using isotope ratio inductively coupled plasma mass spectrometry with¹³C,¹⁵N mass spectrometry