Near infrared spectroscopy as a tool for agricultural expertise: identification of tomato seedlings

• Autor(a) principal: Vinhandelli, Amanda Rodrigues
• Data de Publicação: 2022
• Outros Autores: Brito, Annelisa Arruda De, Faria, Raquel Cintra de, Campos, Luiz Fernandes Cardoso, Goulart, Gilberto Alessandre Soares, Teixeira, Gustavo Henrique de Almeida, Nascimento, Abadia dos Reis, Cunha Junior, Luís Carlos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta scientiarum. Technology (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61270
• topic: hybrids; PC-LDA; PLS-DA; traceability; Solanum lycopersycum L.; hybrids; PC-LDA; PLS-DA; traceability; Solanum lycopersycum L.

Tomatoes are one of the most prominent vegetables globally, with significant cultural and economic relevance in various nations, including Brazil. The term ‘safe food’ is becoming more popular as consumer preferences and supply chain dynamics become evolved in these processes. In light of these issues, the use of safety and quality management methods for fruits and vegetables have increased dramatically, with traceability being one of these solutions worth highlighting. When it comes to traceability, evaluation of tomato seedlings, plants, and fruits to identify groups or hybrids becomes particularly crucial throughout the marketing process, since the consumer of seedlings or fruit has difficulties recognizing whether that product truly belongs to the group indicated by the merchant. Thus, the potential of near infrared spectroscopy (NIRS) combined with the PC-LDA and PLS-DA algorithms was tested for the discrimination of two significant commercial groups, Salada and Saladete, as well as eleven cultivars belonging to these groups, which were tested for this purpose. The results show that, by using the PLS-DA model, the portable NIR equipment is capable of differentiating tomato seedlings in nurseries of the Salada and Saladete groups, with an accuracy of 99.7% and sensitivity of 100%. The technique showed to be efficient for individual models of tomato seedlings in the Salada group, with accuracy over 90% and sensitivity above 93% for all models. For the Saladete group's individual models, the technique proved effectiveness for the hybrids Parma, BS-110012, Giácomo, Guara, and Tyna.