The aim of this study was to develop a quantitative method to determine phosphorus in fertilizers of different matrix compositions using the laser induced breakdown spectroscopy (LIBS) technique. The LIBS spectra were acquired on 26 samples of organic and inorganic fertilizers by using a low cost, portable, gated CCD system in the atmospheric environment. Inductively coupled plasma optical emission spectroscopy (ICP-OES) was used as the reference technique. A method was developed to remove the outlier spectra and perform the baseline correction and peak normalization. By applying the proposed corrections, the linear correlation between LIBS and ICP increased from R = 0.76 to R = 0.95. An average error of 15% found in cross-validation of LIBS quantification appeared feasible for P quantification in fertilizers. Two reference samples with different matrix compositions were also analyzed, and the absolute error in the quantification was below 5%. Further, no significant fluctuation was found in P quantification when LIBS was performed over 150 days.

The C cycle in the Brazilian forests is very important, mainly for issues addressed to climate changes and soil management. Assessing and understanding C dynamics in Amazonian soils can help scientists to improve models and anticipate scenarios. New methods that allow soil C measurements in situ are a crucial approach for this kind of region, due to the costs for collecting and sending soil samples from the rainforest to the laboratory. Laser-induced breakdown spectroscopy (LIBS) is a multielemental atomic emission spectroscopy technique that employs a highly energetic laser pulse for plasma production and requires neither sample preparation nor the use of reagents. As LIBS takes less than 10 s per sample measurement, it is considered a promising technique for in situ soil analyses. One of the limitations of portable LIBS systems, however, is the common overlap of the emission lines that cannot be spectrally resolved. In this study a method was developed capable of separating the Al interference from the C emission line in LIBS measurements. Two typical forest Brazilian soils rich in Al were investigated: a spodosol (Amazon Forest) and an oxisol (Atlantic Forest). Fifty-three samples were collected and analyzed using a low-resolution LIBS apparatus to measure the intensities of C lines. In particular, two C lines were evaluated, at 193.03 and 247.86 nm. The line at 247.86 nm showed very strong interference with Fe and Si lines, which made quantitative analysis difficult. The line at 193.03 nm showed interference with atomic and ionic Al emission lines, but this problem could be solved by applying a correction method that was proposed and tested in this work. The line at 247.86 was used to assess the proposed model. The strong correlation (Pearson’s coefficient $R=0.91$) found between the LIBS values and those obtained by a reference technique (dry combustion by an elemental analyzer) supported the validity of the proposed method.

Laser-induced breakdown spectroscopy (LIBS) for atomic multi-elementary analyses, and Fourier transform infrared spectroscopy (FTIR) for molecular identification, are often suggested as the most versatile spectroscopic techniques. The present work aimed to evaluate the performance of both techniques, LIBS and FTIR, combined with principal component analysis (PCA) and machine learning (ML) algorithms in the detection of the composition analysis and differentiation of four different types of rice, white, brown, black, and red. The two techniques were primarily used to obtain the elemental and molecular qualitative characterization of rice samples. Then, LIBS and FTIR data sets were subjected to PCA and supervised ML analysis to investigate which main chemical features were responsible for nutritional differences for the white (milled) and colored rice samples. In particular, PCA data analysis suggested that protein, fatty acids, and magnesium were the highest contributors to the sample’s differentiation. The ML analysis based on this information yielded a 100% level of accuracy, sensitivity, and specificity on sample classification. In conclusion, LIBS and FTIR coupled with multivariate analysis were confirmed as promising tools alternative to traditional analytical techniques for composition analysis and differentiation when subtle chemical variations were observed.