Nutritional characterization of healthy and Aphelenchoides besseyi infected soybean leaves by laser-induced breakdown spectroscopy (LIBS)

Autor(es)

Ranulfi, Anielle C. ^{[1, 2]} ; Senesi, Giorgio S. ^[3] ; Caetano, Jonas B. ^{[2, 4]} ; Meyer, Mauricio C. ^[5] ; Magalhaes, Aida B. ^[2] ; Villas-Boas, Paulino R. ^[2] ; Milori, Debora M. B. P. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Inst Phys, POB 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Embrapa Instrumentat, POB 741, BR-13561206 Sao Carlos, SP - Brazil

^[3] Ist Nanotecnol NANOTEC, CNR, PLasMI Lab, Via Amendola 122-D, I-70126 Bari - Italy

^[4] Univ Fed Sao Carlos, Phys Dept, Rodovia Washington Luis KM 235, BR-13365905 Sao Carlos, SP - Brazil

^[5] Embrapa Soybean, POB 231, BR-86001970 Londrina, PR - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Microchemical Journal; v. 141, p. 118-126, SEP 2018.
• Citações Web of Science: 8

Resumo

Soybean and its derivatives are one of the most valuable and traded agricultural commodities worldwide. The major problem faced by the producers is the reduction of soybean yield due to diseases. In Brazil, the green stem and foliar retention (GSFR) was recently described as affecting soybean plants and causing concerns. Unfortunately, no effective methods of early diagnosis and treatments are known. In an attempt to better investigate the plant changes caused by GSFR infection, soybean leaves collected from healthy and sick plants of two varieties from two different places of Brazil were evaluated comparatively for their content of the three macronutrients Ca, K and Mg by laser-induced breakdown spectroscopy (LIBS). Atomic absorption spectrometry (AAS) was used as the reference technique. In general, the relative simplicity of LIBS instrumentation and the minimal sample preparation required makes it a valuable tool for agriculture application, including nutritional investigation and disease diagnosis of plant samples. The Pearson coefficients obtained for the correlation between LIBS and AAS data were close to 0.80 for the three nutrients analyzed. The results obtained by applying the Student t-test and Principal Component Analysis (PCA) to experimental data allowed to discern between healthy and sick plant leaves. LIBS data analyzed by the classification via regression (CVR) method associated with Partial Least Square Regression (PLSR) yielded success rates higher than 80% in class differentiation. This study demonstrates the possibility of using LIBS as a convenient analytical tool to discern between healthy and GSFR infected plants by analyzing the three macronutrient Ca, K and Mg, thus providing an early GSFR diagnostic tool. (AU)

• Processo FAPESP: 13/07276-1 - CEPOF - Centro de Pesquisa em Óptica e Fotônica
• Beneficiário: Vanderlei Salvador Bagnato
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs