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Potential of Hyperspectral Imaging to Monitor Cheese Ripening

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Presentation on theme: "Potential of Hyperspectral Imaging to Monitor Cheese Ripening"— Presentation transcript:

1 Potential of Hyperspectral Imaging to Monitor Cheese Ripening
Colm O’Donnell , Ming Zhao School of Biosystems and Food Engineering University College Dublin

2 Laboratory Hyperspectral Imaging (HSI) Systems
Line scanning--push-broom HSI system Point to point scanning--point mapping HSI system NIR Vis-NIR Wavelength range: nm (7 nm increments) nm (5 nm increments) Mode: Reflectance HSI- Vis-NIR Raman Shift range: cmˉ¹ (2 cmˉ¹ increments) Mode: Reflectance Confocal Raman Microscopy

3 HSI data analysis

4 Cooleeney cheese ripening study
Cooleeney cheese, a soft mould ripened cheeses made using cow's milk normally with 8-10 weeks ripening age. During the ripening, white rinds generated and fully covered the surface area and finally turned into light brown colour; texture and flavour gradually changed from the surface towards the centre of the cheese. HSI studies can give complementary information in nutritional values, texture and colour changes, flavour sensoring, bacterial growth, etc. for quality control purposes. 10 cm Day 2 Day 6 Day 9 Day 30 Day 60 RGB images of Remote capture digital camera

5 Potential of HSI-NIR System
Spectra in near Infrared (NIR) wavelength range ( nm) contain information of important chemical compounds (i.e. protein, fatty acids, other aromatic compounds and water in cheese). During ripening, information of aromatic compounds and protein content changes related to flavour changes would be used to assist sensory analysis. Information of moisture content changes can be useful references for conditional settings in cheese ripening room. HSI imaging gives possiblities to visualize minor differences of cheese colour and texture changes which cannot be caught by naked eyes.

6 HSI-NIR on Cooleeney cheese
HSI-NIR imaging in Day 2, 6, 9 and 30 Day 2 Day 6 Day 9 Day 30 Imaging refold using SNV+ Savitzky Golay 1st derivative pre-treated spectral data ( nm) Imaging refold using raw spectral data ( nm)

7 HSI-NIR on Cooleeney cheese
The mean spectrum of each sample from Day 2, 6, 9 and 30 Day 2 Day 6 Day 9 Day 30

8 HSI-NIR on Cooleeney cheese
A HSI imaging solution to separate the ripened and unripened parts of the centre cut of a Day 30 sample The separation is based on principal component (PC) score distribution and the calculation of imaging thresholds. Full image The white rind (yellow) The unripened Part The ripened part

9 HSI-NIR on Cooleeney cheese
A HSI imaging solution to separate the ripened and unripened parts of the centre cut of a Day 30 sample The PCA score plot (PC1 & PC5)

10 HSI-NIR on Cooleeney cheese
A HSI imaging solution to separate the ripened and unripened parts of the centre cut of a Day 30 sample The comparison of related mean spectra comparison

11 HSI-VisNIR on Cooleeney cheese
Visiable and near Infrared (VisNIR) wavelength range ( nm) can be used to inspect colour related information of sample.

12 HSI-VisNIR on Cooleeney cheese
Visiable and near Infrared (VisNIR) wavelength range ( nm) can be used to inspect colour related information of sample. Day 2 Day 6 Day 9 Hyperspectral Imaging is based on full spatial pixels and SNV+1der. pre-treated spectral data refolding.

13 Confocal Raman microscopy on Cooleeney cheese
Microscopy couples a Raman spectrometer allowing high magnification visualisation of a sample and Raman analysis with a microscopic laser spot.  During ripening, the information of cheese micro-structure may relate to texture and chemical changes. 10 μm 10 μm 10 μm Day 2 Day 6 Day 9

14 Confocal Raman microscopy on Cooleeney cheese
The mean spectrum of selected point-mapping area of Day 2, Day 6 and Day 9. Day 2 Day6 Day9 cmˉ¹ shift range mainly relate to the secondary and tertiary structure of proteins ( i.e. amide I and amide III bands)

15 Conclusions HSI systems can give complementary information about cheese ripening stages. Prediction models (e.g.PLSR, MLR) can be developed using the spectral data and their related chemical references; other models ( e.g. PCA, PLS-DA ) can also be developed for quality control.

16 Thanks for your attention !


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