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dc.contributor.authorGama, Tsvakai
dc.contributor.authorWallace, Helen M
dc.contributor.authorTrueman, Stephen J
dc.contributor.authorTahmasbian, Iman
dc.contributor.authorBai, Shahla Hosseini
dc.date.accessioned2019-06-19T13:06:41Z
dc.date.available2019-06-19T13:06:41Z
dc.date.issued2018
dc.identifier.isbn978-94-62612-16-7
dc.identifier.issn0567-7572
dc.identifier.doi10.17660/ActaHortic.2018.1219.40
dc.identifier.urihttp://hdl.handle.net/10072/383984
dc.description.abstractThere is increasing awareness of the need to consume high-quality foods because of health concerns. Food safety and health awareness campaigns have provided an impetus for non-destructive and real-time methods for food quality assessment. Total nitrogen is used as an indicator of crude protein content in foods and we examined the potential of hyperspectral imaging to predict total nitrogen concentration in four brands of almonds purchased from commercial retailers. A hyperspectral imaging system in the wavelength range 400-1000 nm was used in the study. A partial linear squares regression (PLSR) model was developed, which predicted total nitrogen concentration with a determination coefficient (R2p) of 0.82 and a root mean error square of calibration (RMSEC) of 0.16. These results indicated that hyperspectral imaging has great potential to predict total nitrogen concentration of almond kernels.
dc.description.peerreviewedYes
dc.publisherInternational Society for Horticultural Science
dc.relation.ispartofconferencenameVII International Symposium on Almonds and Pistachios
dc.relation.ispartofconferencetitleActa Horticulturae
dc.relation.ispartofdatefrom2017-11-05
dc.relation.ispartofdateto2017-11-09
dc.relation.ispartoflocationAdelaide, Australia
dc.relation.ispartofpagefrom259
dc.relation.ispartofpageto264
dc.relation.ispartofvolume1219
dc.subject.fieldofresearchPost harvest horticultural technologies (incl. transportation and storage)
dc.subject.fieldofresearchHorticultural production
dc.subject.fieldofresearchPlant biology
dc.subject.fieldofresearchcode300806
dc.subject.fieldofresearchcode3008
dc.subject.fieldofresearchcode3108
dc.titleHyperspectral imaging for non-destructive prediction of total nitrogen concentration in almond kernels
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorHosseini-Bai, Shahla
gro.griffith.authorTrueman, Stephen J.
dc.subject.socioeconomiccode260501 Almonds


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    Contains papers delivered by Griffith authors at national and international conferences.

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