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Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses

Priyashantha, Hasitha and Höjer, Annika and Hallin Saedén, Karin and Lundh, Åse and Johansson, Monika and Bernes, Gun and Geladi, Paul and Hetta, Mårten (2020). Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses. Journal of Food Engineering. 264 , 109687
[Research article]

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Abstract

Spectroscopic measurements and imaging have great potential in rapid prediction of cheese maturity, replacing existing subjective evaluation techniques. In this study, 209 long-ripening hard cheeses were evaluated using a hyperspectral camera and also sensory evaluated by a tasting panel. A total of 425 NIR hyperspectral (NIR-HS) images were obtained during ripening at 14, 16, 18, and 20 months, until final sensorial approval of the cheese. The spectral data were interpreted as possible compositional changes between scanning occasions. Regression modelling by partial least squares (PLS) was used to explain the relationship between average spectra and cheese maturity. The PLS model was evaluated with whole cheeses (average spectrum), but also pixelwise, producing prediction images. Analysis of the images showed an increasing homogeneity of the cheese over the time of storage and ripening. It also suggested that maturation begins at the center and spreads to the outer periphery of the cheese.

Authors/Creators:Priyashantha, Hasitha and Höjer, Annika and Hallin Saedén, Karin and Lundh, Åse and Johansson, Monika and Bernes, Gun and Geladi, Paul and Hetta, Mårten
Title:Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses
Series Name/Journal:Journal of Food Engineering
Year of publishing :2020
Volume:264
Article number:109687
Number of Pages:9
ISSN:0260-8774
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Food Science
Keywords:cheese maturation, Principal component analysis, Partial least squares regression, Pixelwise image prediction
URN:NBN:urn:nbn:se:slu:epsilon-p-101215
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-101215
Additional ID:
Type of IDID
DOI10.1016/j.jfoodeng.2019.109687
Web of Science (WoS)000488312400007
Otherhttps://www.sciencedirect.com/science/article/pii/S026087741930319X
ID Code:24531
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
VH - Faculty of Veterinary Medicine and Animal Science
S - Faculty of Forest Sciences
Department:(NL, NJ) > Department of Molecular Sciences
(NL, NJ) > Dept. of Agricultural Research for Northern Sweden
(VH) > Dept. of Agricultural Research for Northern Sweden

(S) > Department of Forest Biomaterials and Technology
Deposited By: SLUpub Connector
Deposited On:14 Jun 2021 13:23
Metadata Last Modified:14 Jun 2021 13:32

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