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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.)

Selection of industrial tomatoes using TD-NMR data and computational classification methods

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Borba, Karla R. [1] ; Oldoni, Fernanda C. A. [1] ; Monaretto, Tatiana [2, 3] ; Colnago, Luiz A. [3] ; Ferreira, Marcos D. [3]
Total Authors: 5
[1] Sao Paulo State Univ UNESP, Dept Food & Nutr, Sch Pharmaceut Sci, Km 1, BR-14801902 Araraquara, SP - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Chem, Trabalhador Sao Carlense Ave 400, BR-13566590 Sao Carlos, SP - Brazil
[3] Embrapa Instrumentat, XV Novembro 1452, BR-13561206 Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Microchemical Journal; v. 164, MAY 2021.
Web of Science Citations: 0

Tomato processing chain has a world economic relevance for the food industry and the agribusiness, providing ready-to-eat products and raw material for other production chains. The product quality is depending on control of some fruit attributes, such as color, soluble solids content (SSC), and defects. The aim of this study was to develop accurate and nondestructive classification models according to the tomato maturation stage, SSC, and presence of defects using Time-Domain Nuclear Magnetic Resonance (TD-NMR) associated with computational classification methods. Each class showed different decay times. Green tomatoes showed a shorter decay signal than red tomatoes, mainly due to the relaxation signal being related to the water mobility in different vegetable tissue compartments. Classification models resulted in great accuracy performances, the best accuracy for each classification were: maturity index: 97% (SVM); SSC: 100% (SVM and kNN); presence of defects: 90% (PLS-DA). These results show that CPMG decays associated with computational methods can be used in the tomato processing industry to classify tomato samples. These classification models showed the potential of TD-NMR technique in a high-throughput screening application before the processing. (AU)

FAPESP's process: 20/07017-0 - Development of non-destructive methods for assessing the quality of intact food by NMR in the time domain
Grantee:Tatiana Monaretto
Support type: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 19/13656-8 - Development and application of nuclear magnetic resonance in products and agricultural processes
Grantee:Luiz Alberto Colnago
Support type: Regular Research Grants