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Article

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Precision Agriculture Laboratory, Biosystems Engineering Department, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Av. Pádua Dias 11, 13418-900 Piracicaba, Brazil
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Author to whom correspondence should be addressed.
Academic Editor: Lammert Kooistra
Sensors 2021, 21(6), 2195; https://doi.org/10.3390/s21062195
Received: 23 January 2021 / Revised: 10 March 2021 / Accepted: 19 March 2021 / Published: 21 March 2021
(This article belongs to the Special Issue Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods)
Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies. View Full-Text
Keywords: chemometrics; proximal sensing; precision agriculture chemometrics; proximal sensing; precision agriculture
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MDPI and ACS Style

Corrêdo, L.d.P.; Maldaner, L.F.; Bazame, H.C.; Molin, J.P. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors 2021, 21, 2195. https://doi.org/10.3390/s21062195

AMA Style

Corrêdo LdP, Maldaner LF, Bazame HC, Molin JP. Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy. Sensors. 2021; 21(6):2195. https://doi.org/10.3390/s21062195

Chicago/Turabian Style

Corrêdo, Lucas d.P., Leonardo F. Maldaner, Helizani C. Bazame, and José P. Molin. 2021. "Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy" Sensors 21, no. 6: 2195. https://doi.org/10.3390/s21062195

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