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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

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Autor(es):
da Silva, Clissia Barboza [1] ; Oliveira, Nielsen Moreira [2] ; Amaral de Carvalho, Marcia Eugenia [3] ; de Medeiros, Andre Dantas [4] ; Nogueira, Marina de Lima [3] ; dos Reis, Andre Rodrigues [5]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Ctr Nucl Energy Agr CENA, BR-13416000 Piracicaba, SP - Brazil
[2] Univ Sao Paulo, Dept Crop Sci, Coll Agr Luiz de Queiroz ESALQ, BR-13416000 Piracicaba, SP - Brazil
[3] Univ Sao Paulo, Dept Genet, Coll Agr Luiz de Queiroz ESALQ, BR-13416000 Piracicaba, SP - Brazil
[4] Fed Univ Vicosa UFV, Dept Agron, BR-36570900 Vicosa, MG - Brazil
[5] Sao Paulo State Univ, Sch Sci & Engn, Dept Biosyst Engn, UNESP, BR-17602496 Tupa, SP - Brazil
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: SCIENTIFIC REPORTS; v. 11, n. 1 SEP 8 2021.
Citações Web of Science: 0
Resumo

In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds. (AU)

Processo FAPESP: 18/03802-4 - EMU concedido no processo 2017/15220-7: sistema de imagem VideoMeterLab
Beneficiário:Clíssia Barboza da Silva
Linha de fomento: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
Processo FAPESP: 18/03793-5 - EMU concedido no processo 2017/15220-7: sistema de imagem SeedReporter câmera spectral & colour
Beneficiário:Clíssia Barboza da Silva
Linha de fomento: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
Processo FAPESP: 17/15220-7 - Métodos de análise de imagens não destrutivos para avaliação da qualidade de sementes
Beneficiário:Clíssia Barboza da Silva
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
Processo FAPESP: 18/01774-3 - Métodos de análise de imagens não destrutivos para avaliação da qualidade de sementes
Beneficiário:Clíssia Barboza da Silva
Linha de fomento: Bolsas no Brasil - Apoio a Jovens Pesquisadores