FT NIR assisted Machine and Deep learning for determination of Acteoside, Aucubin and Catalpol contents of Plantago lanceolata

Cedric Camps, Xavier Simonnet, Valérie Bardot and Michel Dubourdeaux
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Xavier Simonnet: Agroscope, Institute for Plant Production Sciences IPS, Switzerland
Michel Dubourdeaux: Pileje Industrie, Naturopôle Nutrition Santé, Les Tiolans, F-03800 Saint-Bonnet-de-Rochefort, France

Agricultural Research & Technology: Open Access Journal, 2020, vol. 25, issue 2, 64-68

Abstract: Aucubin, acteoside and catalpol are important compounds present in this plant but their determination is time consuming, costly and necessitate the use of chemicals. In the present study we developed a method based on FT-NIR spectroscopy to determine aucubin, acteoside and catalpol from dry powders of Plantago lanceolata. FT-NIR spectra were processed in PLS and deep learning methods and the accuracies of models were calculated and compared. Aucubin, acteoside and catalpol contents were predicted with a root mean square error of 0.56, 0.25 and 0.18%, respectively by using PLS method. Deep learning did not allow to improve the accuracies but allowed obtaining similar accuracies. FT-NIR based method showed interesting results and is promising to develop a fully usable method for selection programs of plantain with high level contents of bioactive compounds.

Keywords: open access; Juniper publishers agriculture open access journals; agriculture peer reviewed journals; sustainable agriculture peer reviewed journals; agriculture research journal; agricultural sciences open access journal; open access journals of agriculture; agriculture journals impact factor; agriculture academic journals; agriculture journals by impact factor; agricultural engineering journals; juniper publishers review (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:adp:artoaj:v:25:y:2020:i:2:p:64-68

DOI: 10.19080/ARTOAJ.2020.25.556296

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