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Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study

Priyashantha, Hasitha and Höjer, Annika and Hallin Saedén, Karin and Lundh, Åse and Johansson, Monika and Bernes, Gun and Geladi, Paul and Hetta, Mårten (2021). Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study. Food Control. 130 , 108316
[Research article]

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Abstract

Near-infrared (874–1734 nm) hyperspectral (NIR-HS) imaging, coupled with chemometric tools, was used to explore the relationship between spectroscopic data and cheese maturation. A predictive tool to determine the end-date of cheese maturation (E-index, in days) was developed using a set of 425 NIR-HS images acquired during industrial-scale cheese production. The NIR-HS images were obtained by scanning the cheeses at 14, 16, 18 and 20 months of ripening, before a final sensorial assessment in which all cheeses were approved by 20 months. Regression modelling by partial least squares (PLS) was used to explore the relationship between average spectra and E-index. The best PLS model achieved 69.6% accuracy in the prediction of E-index when standard normal variate (SNV) correction and mean centring pre-processing were applied. Thus, NIR-HS image modelling can be useful as a complementary tool to optimise the logistics/efficiency of cheese ripening facilities by rapid and non-destructive prediction of the end-date of ripening for individual cheeses. However, the commercial application will require future improvements in the predictive capacity of the model, e.g. for larger datasets and repetitive scans of cheeses on random occasions.

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:Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study
Series Name/Journal:Food Control
Year of publishing :2021
Volume:130
Article number:108316
Number of Pages:7
ISSN:0956-7135
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 ripening, partial least squares regression, long-ripening cheese, NIR hyperspectral imaging, predictive tool, non-destructive technique
URN:NBN:urn:nbn:se:slu:epsilon-p-112278
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-112278
Additional ID:
Type of IDID
DOI10.1016/j.foodcont.2021.108316
ID Code:24527
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 09:43
Metadata Last Modified:14 Jun 2021 09:51

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