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.     

Elemental composition changes in citrus affected by the CVC disease

The citrus variegated chlorosis (CVC) disease results in serious economical losses for the Brazilian citriculture. The influence of CVC disease on the elemental composition of citrus plants was investigated. Leaves of sweet orange varieties Hamlin, Pera Rio and Valencia were collected from healthy and CVC-affected trees for chemical characterization by instrumental neutron activation analysis (INAA). Significant differences between healthy and CVC-affected leaves were identified for Ca, Ce, Co, Eu, Fe, K, La, Na, Nd, Rb, Sc and Sm. Rare earth elements presented consistently higher mass fractions in the healthy leaves.     

Investigation of laser-induced breakdown spectroscopy and multivariate analysis for differentiating inorganic and organic C in a variety of soils

Laser-induced breakdown spectroscopy (LIBS) along with multivariate analysis was used to differentiate between the total carbon (C), inorganic C, and organic C in a set of 58 different soils from 5 soil orders. A 532 nm laser with 45 mJ of laser power was used to excite the 58 samples of soil and the emission of all the elements present in the soil samples was recorded in a single spectrum with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., combustion on a LECO-CN analyzer, to determine the true values for total C, inorganic C, and organic C concentrations. Our objectives were: 1) to determine the characteristic spectra of soils containing different amounts of organic and inorganic C, and 2) to examine the viability of this technique for differentiating between soils that contain predominantly organic and/or inorganic C content for a range of diverse soils. Previous work has shown that LIBS is an accurate and reliable approach to measuring total carbon content of soils, but it remains uncertain whether inorganic and organic forms of carbon can be separated using this approach. Total C and inorganic C exhibited correlation with rock-forming elements such as Al, Si, Fe, Ti, Ca, and Sr, while organic C exhibited minor correlation with these elements and a major correlation with Mg. We calculated a figure of merit (Mg/Ca) based on our results to enable differentiation between inorganic versus organic C. We obtained the LIBS validation prediction for total, inorganic, and organic C to have a coefficient of regression, r² = 0.91, 0.87, and 0.91 respectively. These examples demonstrate an advance in LIBS-based techniques to distinguish between organic and inorganic C using the full wavelength spectra.     

Finding the Most Significant Elements for the Classification of Organic Orange Leaves: A Data Mining Approach

Brazil is the world’s largest producer of oranges. The Brazilian conventional citrus crop requires repeated application of agrochemicals to achieve satisfactory levels of productivity. The organic citriculture is an alternative production system, which is environmentally friendly and offers a safe food to consumers. However, it is difficult to determine if a food or plant was cultivated in organic or conventional system by just common observation, which makes the customers of organic food market vulnerable against fraudulent entrepreneurs. In this study, we present a data mining approach for the study of Brazilian organic citrus leaves which can aid in the certification of authenticity of the citrus leaves. The elemental composition is determined by inductively coupled plasma-mass spectrometry (ICP-MS). We developed classification models based on support vector machines and artificial neural networks capable of predicting whether a citrus leaf is organic or conventional through analysis of the concentration levels of the 14 chemical elements (Al, Ba, Co, Cr, Cs, Cu, Fe, Mg, Mn, Ni, Rb, Si, Sr, and V) found in both types of leaves. Feature selection filter methods are used to determine the most relevant elements for the classification process. Our best model obtained was a support vector machine with approximately 88% prediction accuracy. The elements Mn, Mg, and Rb were evaluated as the most significant for the classification decision. This is the first paper which addresses the problem of classification of organic orange leaves based on chemical composition. The presented methodology is useful for attesting authenticity of organic citrus leaves and can be adapted for other organic food or substances.     

Development of extraction methods for speciation analysis of selenium in aqueous extracts of medicinal plants

Advanced extraction methods have been developed for direct speciation of dissolved inorganic and organic selenium (Se) species in aqueous extracts of medicinal plants (MPs). The inorganic species of Se (SeIV and SeVI) were separated from organic forms by adsorbing inorganic Se on alumina, while the organic Se was not retained. The retained inorganic Se species was eluted with 10 mL 0.2 M HCl. The total inorganic Se species was determined after prereduction of SeVI into SeIV with concentrated HCl. The SeIV in the eluent and total inorganic Se species were then complexed with diethyldithiocarbamate. The resultant complexes were entrapped in the nonionic surfactant Triton X-114. The total Se, organic Se, total inorganic Se, and SeIV species were determined by electrothermal atomic absorption spectrometry with a modifier. The SeVI concentration was obtained by subtracting SeIV from total inorganic Se contents. The main factors affecting the methodologies were investigated in detail. Under the optimized experimental conditions, the LOD for SeIV was 50 microg/L. Among dissolved inorganic and organic Se species in aqueous extracts of MPs, organic Se species were present in the range of 74-84%, SeIV 3.62-7.47%, and SeVI 12.4-18.57% of total Se contents.     

Advanced recognition of explosives in traces on polymer surfaces using LIBS and supervised learning classifiers

The large similarity existing in the spectral emissions collected from organic compounds by laser-induced breakdown spectroscopy (LIBS) is a limiting factor for the use of this technology in the real world. Specifically, among the most ambitious challenges of today's LIBS involves the recognition of an organic residue when neglected on the surface of an object of identical nature. Under these circumstances, the development of an efficient algorithm to disclose the minute differences within this highly complex spectral information is crucial for a realistic application of LIBS in countering explosive threats. An approach cemented on scatter plots of characteristic emission features has been developed to identify organic explosives when located on polymeric surfaces (teflon, nylon and polyethylene). By using selected spectral variables, the approach allows to design a concise classifier for alerting when one of four explosives (DNT, TNT, RDX and PETN) is present on the surface of the polymer. Ordinary products (butter, fuel oil, hand cream, olive oil and motor oil) cause no confusion in the decisions taken by the classifier. With rates of false negatives and false positives below 5%, results demonstrate that the classification algorithm enables to label residues according to their harmful nature in the most demanding scenario for a LIBS sensor.     

Double pulse laser-induced breakdown spectroscopy of explosives: Initial study towards improved discrimination

Detecting trace explosive residues at standoff distances in real-time is a difficult problem. One method ideally suited for real-time standoff detection is laser-induced breakdown spectroscopy (LIBS). However, atmospheric oxygen and nitrogen contributes to the LIBS signal from the oxygen- and nitrogen-containing explosive compounds, complicating the discrimination of explosives from other organic materials. While bathing the sample in an inert gas will remove atmospheric oxygen and nitrogen interference, it cannot practically be applied for standoff LIBS. Alternatively, we have investigated the potential of double pulse LIBS to improve the discrimination of explosives by diminishing the contribution of atmospheric oxygen and nitrogen to the LIBS signal. These initial studies compare the close-contact (< 1 m) LIBS spectra of explosives using single pulse LIBS in argon with double pulse LIBS in atmosphere. We have demonstrated improved discrimination of an explosive and an organic interferent using double pulse LIBS to reduce the air entrained in the analytical plasma.     

In silico structural and functional characterization and phylogenetic study of alkaline phosphatase in bacterium,Rhizobium leguminosarum (Frank 1879)

Phosphorus is one of the primary macronutrient of plants, which is present in soil. It is essential for normal growth and development of plants. Plants use inorganic form of phosphate but organic form can also be assimilated with the help of soil inhabiting bacteria. Alkaline phosphatase is an enzyme present in Rizobium bacteria. This enzyme is responsible for solubilization and mineralization of organic phosphate and makes it readily available for plants. In the present study, nine different strains of Rhizobium leguminosarum were selected for a detailed computational structural and functional characterization and phylogenetic studies of alkaline phosphatase. Amino acid sequences were retrieved from UniProt and saved in FASTA format for use in analysis. Phylogenetic analysis of these strains was done by using MEGA7. 3D structure prediction was performed by using online server I-Tasser. Galaxy Web and 3D Refine were used for structure refinement. The refined structures were evaluated using two validation servers, QMEAN and SAVES. Protein-protein interaction analysis was done by using STRING. For detailed functional characterization, Cofactor, Coach, RaptorX, PSORT and MEME were used. Overall quality of predicted protein models was above 80%. Refined and validated models were submitted into PMDB. Seven out of nine strains were closely related and other two were distantly related. Protein-Protein interaction showed no significant co-expression among the interaction partners.     

Mapping of lead,magnesium and copper accumulation in plant tissues by laser-induced breakdown spectroscopy and laser-ablation inductively coupled plasma mass spectrometry

Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) were utilized for mapping the accumulation of Pb, Mg and Cu with a resolution up to 200 μm in a up to cm × cm area of sunflower (Helianthus annuus L.) leaves. The results obtained by LIBS and LA-ICP-MS are compared with the outcomes from Atomic Absorption Spectrometry (AAS) and Thin-Layer Chromatography (TLC). It is shown that laser-ablation based analytical methods can substitute or supplement these techniques mainly in the cases when a fast multi-elemental mapping of a large sample area is needed.     

Feasibility of Separation and Quantification of Inorganic Arsenic Species Using Ion-Exchange Membranes and Laser-Induced Breakdown Spectroscopy

Analytical methods for the determination of inorganic arsenic species have attracted much attention due to the high toxicity of these compounds and related legislative regulations for food. A new method for the separation and quantitation of As(III) and As(V) was developed using ion-exchange membranes and laser-induced breakdown spectroscopy (LIBS). Using the anion-exchange polymer membrane, As(V) was selectively collected on the membrane, and As(III) was filtered through the membrane. The separated As(V) on the membrane was directly determined by LIBS. The As(III) in the filtrate was subsequently oxidized to As(V) and collected by the membrane for LIBS analysis. The detection limit for As(V) was estimated to be 10?mg/kg. The recovery efficiencies for the arsenic species as standards were in the range of 97–99%. This method was applied for the analysis As-spiked water certified reference materials, and the results showed that the recovery for As(V) was 98.9%. This new speciation method is cost-effective, simple, and low labor-intensive for the quantitation of inorganic arsenic.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Identification and discrimination of Pseudomonas aeruginosa bacteria grown in blood and bile by laser-induced breakdown spectroscopy

Pseudomonas aeruginosa bacteria colonies have been analyzed by laser-induced breakdown spectroscopy using nanosecond laser pulses. LIBS spectra were obtained after transferring the bacteria from a nutrient-rich culture medium to a nutrient-free agar plate for laser ablation. To study the dependence of the LIBS spectrum on growth and environmental conditions, colonies were cultured on three different nutrient media: a trypticase soy agar (TSA) plate, a blood agar plate, and a medium chosen deliberately to induce bacteria membrane changes, a MacConkey agar plate containing bile salts. Nineteen atomic and ionic emission lines in the LIBS spectrum, which was dominated by inorganic elements such as calcium, magnesium and sodium, were used to identify and classify the bacteria. A discriminant function analysis was used to discriminate between the P. aeruginosa bacteria and two strains of E. coli: a non-pathogenic environmental strain and the pathogenic strain enterohemorrhagic E. coli 0157:H7 (EHEC). Nearly identical spectra were obtained from P. aeruginosa grown on the TSA plate and the blood agar plate, while the bacteria grown on the MacConkey plate exhibited easily distinguishable differences from the other two. All P. aeruginosa samples, independent of initial growth conditions, were readily discriminated from the two E. coli strains.     

Determination of silicon in plant materials by laser-induced breakdown spectroscopy

In spite of the importance of Si for improving the productivity of many important crops, such as those from the Poaceae family (e.g. sugar cane, maize, wheat, rice), its quantitative determination in plants is seldom carried out and restricted to few laboratories in the world. There is a survey of methods in the literature, but most of them are either laborious or difficult to validate in view of the low availability of reference materials with a certified Si mass fraction. The aim of this study is to propose a method for the direct determination of Si in pellets of plant materials by laser-induced breakdown spectroscopy (LIBS). The experimental setup was designed by using a Q-switched Nd:YAG laser at 1064 nm (5 ns, 10 Hz) and the emission signals were collected by lenses into an optical fiber coupled to an Echelle spectrometer equipped with an intensified charge-coupled device. Experiments were carried out with leaves from 24 sugar cane varieties, with mass fractions varying from ca. 2 to 10 g kg^− 1 Si. Pellets prepared from cryogenically ground leaves were used as test samples for both method development and validation of the calibration model. Best results were obtained when the test samples were interrogated with laser fluence of 50 J cm^− 2 (750 μm spot size) and measurements carried out at Si I 212.412 nm emission line. The results obtained by LIBS were compared with those from inductively coupled plasma optical emission spectrometry after oven-induced alkaline digestion, and no significant differences were observed after applying the Student's t-test at 95% confidence level. The trueness of the proposed LIBS method was also confirmed from the analysis of CRM GBW 07603 (Bush branches and leaves).     

Differences between elemental composition of orange juices and leaves from organic and conventional production systems

Summary A comprehensive sampling of organic and conventional oranges was carried out in Bebedouro, an important citrus producing region of Brazil. The soils, leaves and fruits of the variety Valencia (Citrus sinensis [L.]Osbeck) budded on Rangpur lime (Citrus limonia Osbeck) were analyzed. The chemical characterization was accomplished by instrumental neutron activation analysis (INAA). Indications for a difference between organic and conventional orange juices and leaves were obtained by applying univariate and multivariate statistical analysis. There were differences between samples for Br, Co, Cs, La and Rb from both systems.     

Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves

In recent years, Huanglongbing (HLB) also known as citrus greening has greatly affected citrus orchards in Florida. This disease has caused significant economic and production losses costing about $750/acre for HLB management. Early and accurate detection of HLB is a critical management step to control the spread of this disease. This work focuses on the application of mid-infrared spectroscopy for the detection of HLB in citrus leaves. Leaf samples of healthy, nutrient-deficient, and HLB-infected trees were processed in two ways (process-1 and process-2) and analyzed using a rugged, portable mid-infrared spectrometer. Spectral absorbance data from the range of 5.15-10.72 μm (1942-933 cm⁻¹) were preprocessed (baseline correction, negative offset correction, and removal of water absorbance band) and used for data analysis. The first and second derivatives were calculated using the Savitzky-Golay method. The preprocessed raw dataset, first derivatives dataset, and second derivatives dataset were first analyzed by principal component analysis. Then, the selected principal component scores were classified using two classification algorithms, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN). When the spectral data from leaf samples processed using process-1 were used for data analysis, the kNN-based algorithm yielded higher classification accuracies (especially nutrient-deficient leaf class) than that of the other spectral data (process-2). The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm. Moreover, among different types of datasets, preprocessed raw dataset resulted in higher classification accuracies than first and second derivatives datasets. The spectral peak in the region of 9.0-10.5 μm (952-1112 cm⁻¹) was found to be distinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrates the applicability of mid-infrared spectroscopy for HLB detection in citrus.     

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p?=?0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.     

Radioiodination and biodistribution of isolated lawsone compound from Lawsonia inermis (henna) leaves extract

Lawsonia inermis (henna) is one of the most effective medicinal plants and it has been using for treatment of wounds and burns for centuries. The using of Henna leaves is very popular for cosmetic as well as medicine in many countries. Henna leaves contain lots of different compounds and lawsone (LW) is the main one. In current study, extraction with bidistillated water of henna leaves was performed and LW was isolated by using high performance liquid chromatography system. Chemical structure of LW was evaluated by nuclear magnetic resonance method. LW was radiolabeled with iodine-131 (¹³¹I) radionuclide which is well known for nuclear imaging and therapy in nuclear medicine by utilizing iodogen method. The yield of radiolabeling of LW (¹³¹I-LW) was calculated as 92.70 ± 4.312 % (n = 10) by thin layer radio chromatography. Its in vivo biological activity was investigated by biodistribution studies which were performed by using healthy female and male Balb/C mice. According to results of biodistribution, uptake of ¹³¹I labeled LW compound in uterus, breast and ovary for female mice and prostate in male mice was higher than other organs in the body.     

Nondestructive discrimination of ivories and prediction of their specific gravity by Fourier-transform Raman spectroscopy and chemometrics

Fourier-transform (FF) Raman spectroscopy and chemometrics were used for nondestructive analysis of ivories. The discrimination of five kinds of ivories, two subspecies of African elephant, mammoth, hippopotamus, and sperm whale, was investigated, and a calibration model for predicting their specific gravity was developed. FT-Raman spectra were measured in situ for them and chemometrics analyses were carried out for the 3050-350 cm(-1) region. The five kinds of ivories were clearly discriminated from each other on the scores plots of two or three principal components (PCs) obtained by principal component analysis (PCA). The loadings plot for PC 1 shows that the discrimination relies on the content ratio of organic collagenous protein and inorganic hydroxyapatite of ivories. The loadings plot for PC 2 shows that bands due to the CH3 and CH2 stretching modes of the protein also play a role in the discrimination. Using partial least squares regression (PLSR), we developed a calibration model that predicts the specific gravity of the ivories from the FT-Raman spectra. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.980 and 0.024, respectively.