Abstract
The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars (Sesamum indicum) and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis—PCA, hierarchical cluster analysis—HCA, k-nearest neighbor—KNN and the flexible independent modeling of class analogy—SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.
Highlights
The seed Sesamum indicum L. is known in Brazil as sesame, belonging to the Pedalium family; it is an annual or perennial herbaceous plant
Sesame is considered as the ninth most cultivated oilseed in the world and an oilseed of great economic importance. It is grown in more than 70 countries, especially on the Asian and African continents, with India, Myanmar and China accounting for 51.96% of world production [3,4]
These results demonstrate that the construction of a database using machine learning and near-infrared spectroscopy (NIR) spectroscopy can make the seed authentication process much faster than traditional methods and much less expensive for companies and research laboratories, as well as for inspection bodies using portable NIRs, as well as being in line with the world policy of sustainable development and green chemistry
Summary
The seed Sesamum indicum L. is known in Brazil as sesame, belonging to the Pedalium family; it is an annual or perennial herbaceous plant. In order to obtain results from the interactions with the analyte (seeds and grains), the development of chemometric models is required [12,17] These techniques refer to mathematical models and methods and, among these, multivariate statistics, which consider the correlation between many variables analyzed simultaneously, allowing the extraction of a much greater amount of information. The techniques for evaluating the quality of seeds and grains, quickly and non-destructively, can be applied for their selection and classification, mainly because it is a food matrix, whose composition presents a high variability, being influenced by the variety or cultivar, climatic conditions, soil and industrial processing [10,13]. Some scientists performed the classification of cultivars through NIR icnlimseaetdics ccounltdivitaiotends.inSotmhee ssacimenetissotisl paenrdfotrhmeesdamthee ccllaimssaifiticcactioonndoitfiocnuslt,ivwahriscthhrsooumgehcNalIlR “inNsIReepdhsecnuolttyivpaintegd”.in the same soil and the same climatic conditions, which some call “NIRTphheerenfootryep, inthgi”s. study aims to demonstrate the classification of sesame cultivars throuTghheirneftoacret ,steheids sstfurdomy atihmesstaomdeemsooinl satnradtecltihmeactliacscsoifincdaittiioonnos,f usessinagmtehceuultnivsaurpsetrhvriosuegdh pinattatecrtnsereecdosgfnroitmionthteecshanmiqeuseosi,lPaCnAd aclnimd HatCicAc.oAnddidtiiotinosn,aullsyi,ntghethdeevuenlsouppmeervnitsoefdspesaatmteren sreeecdogcnuilttiiovnarteacnhdnsiqtruaeins,cPlaCsAsifiacnadtioHnCmAo.dAeldsdwiteiorenaalnlya,lythzeedduevsienlgopthmeemntaochf isneesalemarensineegd tceuclhtinviaqruaesndKNstrNaianncdlaSsIsMifiCcaAti,oinn mspoedcetrlas winetrheeannaealyrz-iendfruarseindgrethgeiomnsa,cihninwehlaetarwneincgotuelcdhcnailqlu“eNsIKRNgeNnoatnydpiSnIgM”.CA, in spectra in the near-infrared regions, in what we could call “NIR genotyping”
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