Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data

Binetti, Giulio; Coco, Laura Del; Ragone, Rosa; Zelasco, Samanta; Perri, Enzo; Montemurro, Cinzia; Valentini, Raffaele; Naso, David; Fanizzi, Francesco Paolo; Schena, Francesco Paolo

doi:10.1016/j.foodchem.2016.09.041

Cited by 48 publications

(27 citation statements)

References 32 publications

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“…The aim of the project was to efficiently recover new germplasm in the territory, and to genetically characterize it. Simple sequence repeat (SSR) markers were chosen, as they are still considered highly reliable in the identification of varieties of different crops [19][20][21][22], including olive [23][24][25][26], population genetics [4][5][6], and product traceability [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Recovery, Assessment, and Molecular Characterization of Minor Olive Genotypes in Tunisia

Debbabi

Miazzi

Elloumi³

et al. 2020

Plants

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Olive is one of the oldest cultivated species in the Mediterranean Basin, including Tunisia, where it has a wide diversity, with more than 200 cultivars, of both wild and feral forms. Many minor cultivars are still present in marginal areas of Tunisia, where they are maintained by farmers in small local groves, but they are poorly characterized and evaluated. In order to recover this neglected germplasm, surveys were conducted in different areas, and 31 genotypes were collected, molecularly characterized with 12 nuclear microsatellite (simple sequence repeat (SSR)) markers, and compared with 26 reference cultivars present in the Tunisian National Olive collection. The analysis revealed an overall high genetic diversity of this olive’s germplasm, but also discovered the presence of synonymies and homonymies among the commercialized varieties. The structure analysis showed the presence of different gene pools in the analyzed germplasm. In particular, the marginal germplasm from Ras Jbal and Azmour is characterized by gene pools not present in commercial (Nurseries) varieties, pointing out the very narrow genetic base of the commercialized olive material in Tunisia, and the need to broaden it to avoid the risk of genetic erosion of this species in this country.

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Section: Introductionmentioning

confidence: 99%

Recovery, Assessment, and Molecular Characterization of Minor Olive Genotypes in Tunisia

Debbabi

Miazzi

Elloumi³

et al. 2020

Plants

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“…Monovarietal oils were extracted from olives originating from the same fresh product lots of those used for table olive production using a mini olive press (Spremioliva C30 milling machine; Toscana, Enologica Mori, Tavernelle Val di Pesa, Italy) as described previously by Binetti et al . The olive oils were stored in dark glass bottles at −20 °C under a nitrogen atmosphere until the analyses were performed.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of bioactive compounds in black table olives fermented with selected microbial starters

Durante

Tufariello

Tommasi³

et al. 2017

J Sci Food Agric

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“…The authentication and origin of extra-virgin olive oil cultivars constitutes a challenge and the development of efficient and accurate methods to perform it is difficult due to a wide range of variables that influence the olive oil properties (e.g., multiplicity of varieties, pedo-climatic aspects, as well as production and storage conditions). In their study Binetti et al [52] applied ANN on several analytical datasets to achieve that goal, with accuracies over 99% in some cases.…”

Section: E Classification and Quality Controlmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Guiné¹,

Dets²

2019

ijfe

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 -Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.

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Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data

Cited by 48 publications

References 32 publications

Recovery, Assessment, and Molecular Characterization of Minor Olive Genotypes in Tunisia

Recovery, Assessment, and Molecular Characterization of Minor Olive Genotypes in Tunisia

Evaluation of bioactive compounds in black table olives fermented with selected microbial starters

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

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