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dc.contributor.authorWilson, Den_NZ
dc.contributor.authorDing, Hen_NZ
dc.contributor.authorLi, Ben_NZ
dc.contributor.authorBoiarkina, Ien_NZ
dc.contributor.authorYu, Wen_NZ
dc.contributor.authorYoung, Ben_NZ
dc.date.accessioned2020-08-04T01:27:36Z
dc.date.available2020-08-04T01:27:36Z
dc.date.copyright2020-09-01en_NZ
dc.identifier.citationFoods, 9(8), 1024. doi:10.3390/foods9081024
dc.identifier.issn2304-8158en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/13573
dc.description.abstractThe chemical and physical properties of instant whole milk powder (IWMP), such as morphology, protein content, and particle size, can affect its functionality and performance. Bulk density, which directly determines the packing cost and transportation cost of milk powder, is one of the most important functional properties of IWMP, and it is mainly affected by physical properties, e.g., morphology and particle size. This work quantified the relationship between morphology and bulk density of IWMP and developed a predictive model of bulk density for IWMP. To obtain milk powder samples with different particle size fractions, IWMP samples of four different brands were sieved into three different particle size range groups, before using the simplex-centroid design (SCD) method to remix the milk powder samples. The bulk densities of these remixed milk powder samples were then measured by tap testing, and the particles’ shape factors were extracted by light microscopy and image processing. The number of variables was decreased by principal component analysis and partial least squares models and artificial neural network models were built to predict the bulk density of IWMP. It was found that different brands of IWMP have different morphology, and the bulk density trends versus the shape factor changes were similar for the different particle size range groups. Finally, prediction models for bulk density were developed by using the shape factors and particle size range fractions of the IWMP samples. The good results of these models proved that predicting the bulk density of IWMP by using shape factors and particle size range fractions is achievable and could be used as a model for online model-based process monitoring.
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/2304-8158/9/8/1024
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.subjectInstant whole milk powder; Bulk density; Morphology; Principal component analysis; Partial least squares; Artificial neural networks
dc.titleEffects of Morphology on the Bulk Density of Instant Whole Milk Powderen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.3390/foods9081024en_NZ
pubs.elements-id388151
aut.relation.journalFoodsen_NZ


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