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The Laser Induced Breakdown Spectroscopy (LIBS) is a promising technique for soil fertility analysis in a rapid and environmentally friendly way. This application requires the selection of an optimal modelling procedure capable of handling the high spectral resolution of LIBS. This work aimed at comparing different modelling methods of LIBS data for the determination of key fertility attributes in Brazilian tropical soils. A benchtop LIBS system was used for the analysis of 102 soil samples, prepared in the form of pressed pellets. Models for the prediction of clay, organic matter, pH, cation exchange capacity, base saturation, and the extractable nutrients P, K, Ca, and Mg were developed using univariate linear regression (ULR), multiple linear regression (MLR) and partial least squares regression (PLS). The following input data for PLS were used: (i) the full spectra from 200 to 540 nm (38,880 variables), and (ii) variables selected by the interval successive projections algorithm (iSPA). The multivariate models achieved satisfactory predictions [residual prediction deviation (RPD) > 1.40] for eight out of nine fertility attributes. However, the best performances were obtained for the PLS with the variable ranges selected by the iSPA, which achieved satisfactory predictions (RPD ≥ 1.44) for seven out of the nine soil attributes studied. The MLR method obtained lower prediction performance than the iSPA-PLS using only 21 variables. The iSPA-PLS approach allowed a reduction from 3 to 160-fold in the total of variables compared to the full LIBS spectra, making it efficient and accurate modelling method that uses reduced number of variables. Although LIBS technique proved to be efficient for predicting fertility attributes in tropical soils, further research is encouraged in order to reduce the amount of sample preparation conducted in this study.

Results of evaluations using optical evaluation methods may be correlated with tomato quality and maturation. In this context, the objective of this study was to evaluated the correlation between tomato colorimetric and physico-chemical variables, clustering them as a function of maturation stages, using multivariate analysis. The experiment was conducted using 150 fruits and three maturation stages (immature, light red and mature). The physico-chemical variables were evaluated through traditional methods. The colorimetric variables were assessed on images in RGB color model taken with a digital camera. The correlation between colorimetric and physico-chemical variables was analyzed using the Pearson’s coefficient. Principal components analysis and k-means clustering method was applied to three data set: RGB isolated variables; colorimetric variables calculated by relation between the RGB bands (colorimetric indexes); and physico-chemical variables. The colorimetric variables present higher explanatory capacity of the maturation variation than physico-chemical variables. The colorimetric indexes presented higher performance in clustering (accuracy of 0.98) tomatoes as a function of maturation. RESUMO Variáveis obtidas por métodos ópticos podem apresentar correlação com características relacionadas à qualidade e maturação de tomates. Neste contexto, objetivou-se com este estudo analisar a correlação entre as variáveis colorimétricas e físico-químicas de tomates, agrupando-os em função do estádio de maturação, utilizando análise multivariada. O experimento foi realizado utilizando 150 frutos em três estádios de maturação (imaturos, coloridos e maduros). As variáveis físico-químicas foram obtidas por meio de métodos tradicionais. As variáveis colorimétricas foram obtidas a partir de uma câmera digital no espaço de cor RGB. A análise da correlação entre as variáveis colorimétricas e físico-químicas foi realizada por meio do coeficiente de Pearson. A análise de componentes principais associada à técnica de agrupamento k-means foi aplicada em três grupos de dados: variáveis RGB isoladas; variáveis colorimétricas calculadas pela relação entre as bandas RGB (índices colorimétricos); e variáveis físico-químicas. As variáveis colorimétricas apresentam maior capacidade explicativa da variação da maturação quando comparadas às variáveis físico-químicas. O conjunto de índices colorimétricos apresentou o maior desempenho no agrupamento (exatidão global = 0,98) dos tomates em função da maturação.