Evidence map›Paper›PMID 36900484›Full record

ArticleFoods (Basel, Switzerland)2023

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder.

Haohan Ding, David I Wilson, Wei Yu, Brent R Young, Xiaohui Cui

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Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Haohan DingScience Center for Future Foods, Jiangnan University, Wuxi 214122, China.ORCID 0000-0001-7921-6629
David I WilsonElectrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand.
Wei YuDepartment of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand.ORCID 0000-0002-3380-0114
Brent R YoungDepartment of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand.
Xiaohui CuiScience Center for Future Foods, Jiangnan University, Wuxi 214122, China.ORCID 0000-0001-6079-009X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower

Indexed as

3D image analysiscontour slice analysismilk powdersurface roughness

Identifiers

PMID36900484
PMCPMC10000610

What OpenQuestion holds

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table measurements read2
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.