Contents lists available at ScienceDirect Microchemical Journal journal homepage: www.elsevier.com/locate/microc Nutritional characterization of healthy and Aphelenchoides besseyi infected soybean leaves by laser-induced breakdown spectroscopy (LIBS) Anielle C. Ranulfi a,b , Giorgio S. Senesi c, ⁎ , Jonas B. Caetano a,d , Maurício C. Meyer e , Aida B. Magalhães a , Paulino R. Villas-Boas a , Débora M.B.P. Milori a a Embrapa Instrumentation, PO Box 741, 13561-206 São Carlos, SP, Brazil b São Carlos Institute of Physics, University of São Paulo, PO Box 369, 13560-970 São Carlos, SP, Brazil c CNR, Istituto di Nanotecnologia (NANOTEC) - PLasMI Lab, Bari 70126, Bari, Italy d Physics Department, Federal University of São Carlos, Rodovia Washington Luís, km 235, 13365-905 São Carlos, SP, Brazil e Embrapa Soybean, PO Box 231, 86001-970, Londrina, PR, Brazil ARTICLE INFO Keywords: Soybean Green stem and foliar retention (GSFR) Nutritional evaluation Laser-induced breakdown spectroscopy Leaf diagnosis ABSTRACT 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 in- vestigate 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 be- tween 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 diag- nostic tool. 1. Introduction Nowadays soybean and its derivatives, including oil, animal feed, protein for human diet and biofuel, are among the most valuable and traded agricultural commodities worldwide, representing over 10% of the total value of agricultural trade [1,2]. In the last 40 years the pro- duction demand of soybean grain has increased by over 5 times mainly due to the world population growth, supply availability and main- tenance of stock levels at a reasonable price [1,3]. As soybean is one of the main animal feed grain and protein meal, the increased production is related mainly to livestock production, besides manufacturing of vegetable oils [4]. Thus, to face the increasing global soybean demand it is prime to increase its yield and decrease its losses, without increase the soybean cropped areas significantly [1]. Currently, USA (31.3%), Brazil (27.6%) and Argentina (17.4%) are the main soybean producers totalizing more than 76% of all soybean production in the harvest 2014/2015 [5]. According to the Agricultural Projections to 2025 of the United States Department of Agriculture (USDA), the USA soybean exports are expected to rise, although Brazil would remain the world leader in soybeans exportation. Nowadays, soybean is cultivated in 57% of the total Brazilian cropped area, and until 2025 it is expected to achieve the greatest increase (more than 1.8%) [6] in an attempt to compensate losses due to diseases [7]. Thus, the development of new agricultural technologies able to render the production process more efficient and improve the yields is a unan- imously recognized need in order to respond to the worldwide demand of increasing soybean production with no further increase of cropped areas. https://doi.org/10.1016/j.microc.2018.05.008 Received 24 February 2018; Received in revised form 7 May 2018; Accepted 7 May 2018 ⁎ Corresponding author at: CNR - Istituto di Nanotecnologia (NANOTEC) - PLasMI Lab, Via Amendola 122/D, 70126 Bari, Italy. E-mail address: giorgio.senesi@nanotec.cnr.it (G.S. Senesi). Microchemical Journal 141 (2018) 118–126 Available online 08 May 2018 0026-265X/ © 2018 Elsevier B.V. All rights reserved. T