Making wood inspection easier: FTIR spectroscopy and machine learning for Brazilian native commercial wood species identification.

The molecular structure of wood is mainly based on cellulose, lignin, and hemicellulose. However, low concentrations of lipids, phenolic compounds, terpenoids, fatty acids, resin acids, and waxes can also be found. In general, their color, smell, texture, quantity, and distribution of pores are used in human sensory analysis to identify native wood species, which may lead to erroneous classification, impairing quality control and inspection of commercialized wood. This study developed a fast and accurate method to discriminate Brazilian native commercial wood species using Fourier Transform Infrared Spectroscopy (FTIR) and machine learning algorithms. It not only solves the limitations of traditional methods but also goes beyond as it allows fast analyses to be obtained at low cost and high accuracy. In this work, we provide the identification of five Brazilian native wood species: Angelim-pedra (Hymenolobium petraeum Ducke), Cambara (Gochnatia polymorpha), Cedrinho (Erisma uncinatum), Champagne (Dipteryx odorata), and Peroba do Norte (Goupia glabra Aubl). The results showed the great potential of FTIR and multivariate analysis for wood sample classification; here, the Linear SVM differentiated the five wood species with an accuracy of 98%. The developed method allows industries, laboratories, companies, and control bodies to identify the nature of the wood product after being extracted and semi-manufactured.

Laser-Induced Breakdown Spectroscopy Associated with the Design of Experiments and Machine Learning for Discrimination of Brachiaria brizantha Seed Vigor.

Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-one-out cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination, regardless of the cultivar or the classification algorithm.

Metronidazole-loaded gold nanoparticles in natural rubber latex as a potential wound dressing.

Gold nanoparticles (AuNPs) have shown interesting properties and specific biofunctions, providing benefits and new opportunities for controlled release systems. In this research, we demonstrated the use of natural rubber latex (NRL) from Hevea brasiliensis as a carrier of AuNPs and the antibiotic metronidazole (MET). We prepared AuNP-MET-NRL and characterized by physicochemical, biological and in vitro release assays. The effect of AuNPs on MET release was evaluated using UV-Vis and Laser-Induced Breakdown Spectroscopy (LIBS) techniques. AuNPs synthesized by Turkevich and Frens method resulted in a spherical shape with diameters of 34.8 ± 5.5 nm. We verified that there was no emergence or disappearance of new vibrational bands. Qualitatively and quantitatively, we showed that the MET crystals dispersed throughout the NRL. The Young's modulus and elongation values at dressing rupture were in the range appropriate for human skin application. 64.70% of the AuNP-MET complex was released within 100 h, exhibiting a second-order exponential release profile. The LIBS technique allowed monitoring of the AuNP release, indicating the Au emission peak reduction at 267.57 nm over time. Moreover, the dressing displayed an excellent hemocompatibility and fibroblast cell viability. These results demonstrated that the AuNP-MET-NRL wound dressing is a promising approach for dermal applications.

Simultaneous quantification of seven multi-class organic molecules by single-shot dilution differential pulse voltammetric calibration.

The contamination of water sources by anthropogenic activities is a topic of growing interest in the scientific community. Therefore, robust analytical techniques for the determination and quantification of multiple substances are needed, which often require complex and time-consuming procedures. In this context, we describe a univariate calibration method to determine emerging multi-class contaminants in different water sources. The instrumental setup is composed of a lab-made glass electrochemical cell with three electrodes: Pt counter, Ag/AgCl reference, and BDD working electrodes. With this system, we were able to simultaneously quantify tert-butylhydroquinone, acetaminophen, estrone, sulfamethoxazole, enrofloxacin, caffeine, and ibuprofen by differential pulse voltammetry. Only two calibration solutions are required for the Single-shot Dilution Differential Pulse Voltammetric Calibration (SSD-DP-VC) method described here, which can significantly improve sample throughput. Two robust univariate calibration strategies were also applied and compared with SSD-DP-VC. The new method is simple, fast, and comparable with traditional calibration methods, showing similar precision and accuracy for all determinations evaluated.

A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning.

Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

Discrimination of Genetically Very Close Accessions of Sweet Orange (Citrus sinensis L. Osbeck) by Laser-Induced Breakdown Spectroscopy (LIBS).

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost-benefit tool in citrus breeding programs.

FTIR spectroscopy with machine learning: A new approach to animal DNA polymorphism screening.

Technological advances in recent decades, especially in molecular genetics, have enabled the detection of genetic DNA markers associated with productive characteristics in animals. However, the prospection of polymorphisms based on DNA sequencing is still expensive for the reality of many food-producing regions around the world, such as Brazil, demanding more accessible prospecting methods. In the present study, the Fourier transform infrared spectroscopy (FTIR) and machine learning algorithms were used to identify single nucleotide polymorphism (SNP) in animal DNA. The fragments of bovine DNA with well-known polymorphisms were used as a model. The DNA fragments were produced and genotyped by PCR-RFLP and classified according to the genotype (homozygous or heterozygous). FTIR spectra of DNA fragments were analyzed by principal component analysis (PCA) and machine learning algorithms. The best results exhibited 75-95% accuracy in the classification of bovine genotypes. Therefore, FTIR spectroscopy and multivariate analysis can be used as an alternative tool for prospecting polymorphisms in animal DNA. The method can contribute with studies to identify genetic markers associated with animal production and indirectly with food production itself, and reduce pressure on available natural resources.

Intraspecific differentiation of sandflies specimens by optical spectroscopy and multivariate analysis.

Lutzomyia longipalpis and Lutzomyia cruzi are the main sandflies species involved in the transmission of Leishmania infantum protozoan in Brazil. The morphological characteristics can be used for species identification of males specimens, while females are indistinguishable. Although, sandflies identification is essential to understand vectorial capacity, and susceptibility to infectious agents or insecticides, there is a lack of new strategies for specimen identification. In this study, Fourier transform infrared photoacoustic spectroscopy combined with multivariate analysis identified intraspecific differences between Lutzomyia populations. Successfully group clustering was achieved by principal component analysis. The main differences observed can be related to the protein content of the specimens. A classification with 100% accuracy was obtained using machine learning approach, allowing the identification of sandflies specimens.

Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms.

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.

Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms.

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented. Batches with high and low vigor soybean seeds were analyzed. Support vector machine (SVM), K-nearest neighbors (KNN), and discriminant analysis were applied to the raw spectral and reduced-dimensionality data from PCA (principal component analysis). Proteins, fatty acids, and amides were identified as the main molecules responsible for the discrimination of the batches. The cross-validation tests pointed out that high vigor soybean seeds were successfully discriminated from low vigor ones with an accuracy of 100%. These findings indicate FTIR spectroscopy associated with multivariate analysis as a new alternative approach to discriminate seed vigor.

Portland Cement/Acrocomia Aculeata Endocarp Bricks: Thermal Insulation and Mechanical Properties.

In the last few decades, Portland/residue composites have been researched due to their technological and environmental advantages. In this study, residues of Acrocomia aculeata (Jacq.) Lodd endocarp (AE) were introduced in the Portland cement-soil (PC) matrix in different concentrations (0, 5, 10, 15, 20, and 50 wt%) to produce PC/AE bricks. The characterization of the microstructures of the bricks indicate agglomerates of AE particles with increased humidity in small regions distributed throughout the matrix. Mid-infrared and laser-induced breakdown spectroscopy, along with thermogravimetry, indicated that AE contained mainly lignin and cellulose, as well as inorganic chemical elements such as Mg and Si. X-ray studies revealed that AE did not affect the crystallographic properties of the Portland/AE bricks. The findings indicate that the use of AE improved the thermal insulation capability of the composites with a small impact on the compressive strength.

Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms.

The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named "meteor-wrongs". In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.

Determination of Pb in soils by double-pulse laser-induced breakdown spectroscopy assisted by continuum wave-diode laser-induced fluorescence.

Laser-induced breakdown spectroscopy (LIBS) has attracted a lot of attention due to its potential to rapidly identify and quantify any chemical element with minimal sample preparation. Despite continuous improvements, the sensitivity of this technique still remains a challenge. In order to increase LIBS intensity, a laser-induced fluorescence (LIF) system can be coupled with LIBS to re-excite a transition of the element in the plasma by employing very expensive optical parametric oscillators (OPO). In this work, a homemade tunable continuum wave-diode laser (CW-DL) has been developed and coupled to a double pulse (DP) LIBS system to enhance the sensitivity of Pb detection in a soil sample at the transition 6s26p2-P32→6s26p7s-P31 at 405.78 nm. Before sample analysis, the production of no scattered light by the plasma was ascertained, and the optimal temperature of 10,000 K was estimated for this transition, feasible to be achieved in DP-LIBS systems. An increase of approximately 100% for the Pb I transition at 405.78 nm was obtained by DP-LIBS-CW-DL-LIF with respect to the DP-LIBS system alone. This result opens a new promising line of research to improve LIBS sensitivity using the CW-DL approach.

Semiquantitative analysis of mercury in landfill leachates using double-pulse laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is showing to be a promising, quick, accurate, and practical technique to detect and measure metal contaminants and nutrients in urban wastes and landfill leachates. Although conventional LIBS presents some limitations, such as low sensitivity, when used in the single pulse configuration if compared to other spectroscopic techniques, the use of the double-pulse (DP) configuration represents an adequate alternative. In this work DP LIBS has been applied to the qualitative and quantitative analysis of mercury (Hg) in landfill leachates. The correlation analysis performed between each intensified charge-coupled device pixel and the Hg concentration allowed us to choose the most appropriate Hg emission line to be used for its measure. The normalization process applied to LIBS spectra to correct physical matrix effects and small fluctuations increased from 0.82 to 0.98 the linear correlation of the calibration curve between LIBS and the reference data. The limit of detection for Hg estimated using DP LIBS was 76 mg Kg-1. The cross validation (leave-one-out) analysis yielded an absolute average error of about 21%. These values showed that the calibration models were close to the optimization limit and satisfactory for Hg quantification in landfill leachate.

Double pulse laser induced breakdown spectroscopy: A potential tool for the analysis of contaminants and macro/micronutrients in organic mineral fertilizers.

Organic fertilizers are obtained from waste of plant or animal origin. One of the advantages of organic fertilizers is that, from the composting, it recycles waste-organic of urban and agriculture origin, whose disposal would cause environmental impacts. Fast and accurate analysis of both major and minor/trace elements contained in organic mineral and inorganic fertilizers of new generation have promoted the application of modern analytical techniques. In particular, laser induced breakdown spectroscopy (LIBS) is showing to be a very promising, quick and practical technique to detect and measure contaminants and nutrients in fertilizers. Although, this technique presents some limitations, such as a low sensitivity, if compared to other spectroscopic techniques, the use of double pulse (DP) LIBS is an alternative to the conventional LIBS in single pulse (SP). The macronutrients (Ca, Mg, K, P), micronutrients (Cu, Fe, Na, Mn, Zn) and contaminant (Cr) in fertilizer using LIBS in SP and DP configurations were evaluated. A comparative study for both configurations was performed using optimized key parameters for improving LIBS performance. The limit of detection (LOD) values obtained by DP LIBS increased up to seven times as compared to SP LIBS. In general, the marked improvement obtained when using DP system in the simultaneous LIBS quantitative determination for fertilizers analysis could be ascribed to the larger ablated mass of the sample. The results presented in this study show the promising potential of the DP LIBS technique for a qualitative analysis in fertilizers, without requiring sample preparation with chemical reagents.

Quantification of total carbon in soil using laser-induced breakdown spectroscopy: a method to correct interference lines.

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.