Computational optimization of the configuration of a spatially resolved spectroscopy sensor for milk analysis

Watté, Rodrigo; Aernouts, Ben; Beers, Robbe Van; Postelmans, Annelies; Saeys, Wouter

doi:10.1016/j.aca.2016.02.041

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…The sensor configuration which measures the diffuse signal resulting in the most accurate separation of the BOP can be found through light propagation calculations. Moreover, the bulk optical properties of wound tissue and serum samples obtained in this study can serve as an input for simulation studies to derive the optimal design of the optical illumination and detection in an implantable NIR glucose sensor . However, as biological tissues are generally very turbid, multiple scattering is dominant, and light propagation is fairly complex.…”

Section: Results and Discussionmentioning

confidence: 99%

Near-infrared bulk optical properties of goat wound tissue and human serum: consequences for an implantable optical glucose sensor

et al. 2015

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Near-infrared (NIR) spectroscopy offers a promising technological platform for continuous glucose monitoring in the human body. Moreover, these measurements could be performed in vivo with an implantable single-chip based optical sensor. However, a thin tissue layer may grow in the optical path of the sensor. As most biological tissues are highly scattering, they only allow a small fraction of the collimated light to pass, significantly reducing the light throughput. To quantify the effect of a thin tissue layer in the optical path, the bulk optical properties of serum and tissue samples grown on implanted dummy sensors were characterized using double integrating sphere and unscattered transmittance measurements. The estimated bulk optical properties were then used to calculate the light attenuation through a thin tissue layer. The combination band of glucose was found to be the better option, relative to the first overtone band, as the absorptivity of glucose molecules is higher, while the reduction in unscattered transmittance due to tissue growth is less. Additionally, as the wound tissue was found to be highly scattering, the unscattered transmittance of the tissue layer is expected to be very low. Therefore, a sensor configuration which measures the diffuse transmittance and/or reflectance instead was recommended. (a) Dummy sensor; (b) explanted dummy sensor in tissue lump; (c) removal of dummy sensor from tissue lump; and (d) 900 µm slices of tissue lump.

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Section: Results and Discussionmentioning

confidence: 99%

Near-infrared bulk optical properties of goat wound tissue and human serum: consequences for an implantable optical glucose sensor

et al. 2015

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“…From a practical point of view, µ s would be most easy to implement as it requires solely DIS measurements or spatially resolved reflectance spectroscopy. Watté, Aernouts, Van Beers, Postelmans, and Saeys (2016) et al already reported the in silico optimization of a spatially resolved reflectance sensor design for determining the BOP of milk. However, µ s was more noisy than µ s in this dataset and already resulted in larger errors in the estimated distribution width (Postelmans et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Milk homogenization monitoring: Fat globule size estimation from scattering spectra of milk

Postelmans

Aernouts

Jordens

et al. 2020

Innovative Food Science & Emerging Technologies

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The fat globule size distribution of raw milk and milk with an increasing degree of homogenization was estimated based on their bulk light scattering properties in the Vis/NIR wavelength range.The particle size distribution (PSD) was approximated as a lognormal distribution, of which the parameters were estimated simultaneously with the fat concentration. This resulted in a good agreement between the estimated PSDs and the reference PSDs obtained by laser diffraction in case of raw and strongly homogenized samples. The accuracy increased if a known fat concentration was incorporated, or when the scattering coefficient and anisotropy factor spectra were used simultaneously as input. For mildly homogenized samples, the lognormal distribution was unable to fit the bimodal PSD correctly and focused on the largest fat globules. In this case, the estimated PSDs provided still relatively accurate information on D90, D32 and the right distribution tail, which contains the largest fat globules. Industrial relevance:The presented estimation method demonstrates the potential of bulk scattering spectra for determining the PSD and concentration of scattering particles in turbid media. Further development of this technology can lead to new solutions for spectroscopic PSD determination, allowing on-line monitoring systems for a wide range of food and non-food products.

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“…When applied to solid samples, SRS typically utilizes a contactless optical configuration using lasers for illumination and cameras for detection (Aernouts et al, 2015b). SRS on liquids is usually performed with optical fibers that make direct contact with or are submerged into the sample (Crofcheck et al, 2002;Foschum et al, 2011;Watté et al, 2016). Fiber-optic SRS can be implemented in a reflectance probe design with different optical fibers serving as illumination and detection points.…”

Section: Introductionmentioning

confidence: 99%

Near-infrared spatially-resolved spectroscopy for milk quality analysis

Diaz-Olivares,

Gote,

Saeys

et al. 2024

Computers and Electronics in Agriculture

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Computational optimization of the configuration of a spatially resolved spectroscopy sensor for milk analysis

Cited by 14 publications

References 40 publications

Near-infrared bulk optical properties of goat wound tissue and human serum: consequences for an implantable optical glucose sensor

Near-infrared bulk optical properties of goat wound tissue and human serum: consequences for an implantable optical glucose sensor

Milk homogenization monitoring: Fat globule size estimation from scattering spectra of milk

Near-infrared spatially-resolved spectroscopy for milk quality analysis

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