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  4. Optimizing Near-Infrared Spectroscopy for On-line Grape Must Quality Assessment: Addressing the Impact of Suspended Solids
 
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2025
Conference Paper
Title

Optimizing Near-Infrared Spectroscopy for On-line Grape Must Quality Assessment: Addressing the Impact of Suspended Solids

Abstract
What distinguishes good wine from premium wine is the quality of the berry material. The sugar and acid content are the key parameters in this case. To measure these, a timeconsuming and costly labor analysis is usually required during delivery process to the cellar. Near-infrared (NIR) spectroscopy offers a faster alternative, but is also used in laboratories due to its size and integrability. The development of the miniaturization of NIR-spectrometer opens up new possibilities for integration. To explore this, such a spectrometer and a pump with particle filter were integrated into a harvester to measure the quality parameters online during harvesting. The filter size of the pump that delivers the grape must to the NIR-spectrometer for analysis is a critical parameter as suspended solids in the must affect accuracy. This study examined the filtration requirements to reduce this problem using 38 samples from four grape varieties, filtered in 9 steps from 1mm to 40μm and centrifuged as the final step considered as free of suspended solids resulting in 359 spectra. Chemometric analysis via PLSR showed that, for sugar prediction, a finer filter is needed, whereas a proper preprocessing effectively eliminates the influence of solids on acids
Author(s)
Gauweiler, Pascal
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Zheng, Xiaorong
Cornehl, Lucie
Kicherer, Anna
Gruna, Robin  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Mainwork
OCM 2025, 7th International Conference on Optical Characterization of Materials  
Conference
International Conference on Optical Characterization of Materials 2025  
Open Access
DOI
10.24406/publica-4521
File(s)
OCM_Tanksensor.pdf (887.11 KB)
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • NIR-Spectroscopy

  • Agriculture

  • Food Quality

  • Chemometrics

  • Vine

  • Grape Must

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