Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses Article Swipe
Giorgia Stocco
,
Laura G. Gómez‐Mascaraque
,
Gaurav Kr Deshwal
,
Jordi Cruz
,
Arnaud Molle
,
Valentina Pizzamiglio
,
Paolo Berzaghi
,
Georgi Gergov
,
Claudio Cipolat‐Gotet
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fnut.2024.1327301
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fnut.2024.1327301
The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950–1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.
Related Topics
Concepts
Raman spectroscopy
Analytical Chemistry (journal)
Linear discriminant analysis
Partial least squares regression
Chemometrics
Food science
Near-infrared spectroscopy
Texture (cosmology)
Chemistry
Pattern recognition (psychology)
Mathematics
Artificial intelligence
Chromatography
Statistics
Computer science
Optics
Physics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnut.2024.1327301
- https://www.frontiersin.org/articles/10.3389/fnut.2024.1327301/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 6
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391573019
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391573019Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fnut.2024.1327301Digital Object Identifier
- Title
-
Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheesesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-02-06Full publication date if available
- Authors
-
Giorgia Stocco, Laura G. Gómez‐Mascaraque, Gaurav Kr Deshwal, Jordi Cruz, Arnaud Molle, Valentina Pizzamiglio, Paolo Berzaghi, Georgi Gergov, Claudio Cipolat‐GotetList of authors in order
- Landing page
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https://doi.org/10.3389/fnut.2024.1327301Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fnut.2024.1327301/pdf?isPublishedV2=FalseDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fnut.2024.1327301/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
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Raman spectroscopy, Analytical Chemistry (journal), Linear discriminant analysis, Partial least squares regression, Chemometrics, Food science, Near-infrared spectroscopy, Texture (cosmology), Chemistry, Pattern recognition (psychology), Mathematics, Artificial intelligence, Chromatography, Statistics, Computer science, Optics, Physics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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