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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning

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Author(s):
Bizzani, Marilia [1] ; Flores, Douglas William Menezes [2] ; Colnago, Luiz Alberto [3] ; Ferreira, Marcos David [3]
Total Authors: 4
Affiliation:
[1] State Univ Sao Paulo UNESP, Fac Pharmaceut Sci, Dept Food & Nutr, Rodovia Araraquara Jati, Km 1, BR-14801902 Araraquara, SP - Brazil
[2] Univ Sao Paulo, Luiz de Queiroz Sch Agr, Dept Agroind Food & Nutr LAN, Ave Pcidua Dias 11, 0R-13418900 Piracicaba, SP - Brazil
[3] Embrapa Instrumentat, Rua 15 Novembro 1452, 13561206 Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Food Chemistry; v. 332, DEC 1 2020.
Web of Science Citations: 0
Abstract

This study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices. (AU)

FAPESP's process: 19/13656-8 - Development and application of nuclear magnetic resonance in products and agricultural processes
Grantee:Luiz Alberto Colnago
Support Opportunities: Regular Research Grants
FAPESP's process: 13/23479-0 - Alternative non invasive systems for measurement of internal fruits quality
Grantee:Marcos David Ferreira
Support Opportunities: Regular Research Grants