J.O. Caceres

Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

Identification and discrimination of bacterial strains of same species exhibiting resistance to antibiotics using laser induced breakdown spectroscopy (LIBS) and neural networks (NN) algorithm is reported. The method has been applied to identify 40 bacterial strains causing hospital acquired infections (HAI), i.e. Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Salmonella typhimurium, Salmonella pullurum and Salmonella salamae. The strains analyzed included both isolated from clinical samples and constructed in laboratory that differ in mutations as a result of their resistance to one or more antibiotics. Small changes in the atomic composition of the bacterial strains, as a result of their mutations and genetic variations, were detected by the LIBS-NN methodology and led to their identification and classification. This is of utmost importance because solely identification of bacterial species is not sufficient for disease diagnosis and identification of the actual strain is also required. The proposed method was successfully able to discriminate strains of the same bacterial species. The optimized NN models provided reliable bacterial strain identification with an index of spectral correlation higher than 95% for the samples analyzed, showing the potential and effectiveness of the method to address the safety and social-cost HAI-related issue.

Application of laser-induced breakdown spectroscopy (LIBS) and neural networks to olive oils analysis.

The adulteration and traceability of olive oils are serious problems in the olive oil industry. In this work, a method based on laser-induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification, quality control, traceability, and adulteration detection of extra virgin olive oils. Instant identification of the samples is achieved using a spectral library, which was obtained by analysis of representative samples using a single laser pulse and treatment by NNs. The samples used in this study belong to four countries. The study also included different regions of each country. The results obtained allow the identification of the oils tested with a certainty of more than 95%. Single-shot measurements were enough for clear identification of the samples. The method can be developed for automatic real-time, fast, reliable, and robust measurements, and the system can be packed into portable form for non-specialist users.

Classification of red wine based on its protected designation of origin (PDO) using Laser-induced Breakdown Spectroscopy (LIBS).

This work reports on a simple and fast classification procedure for the quality control of red wines with protected designation of origin (PDO) by means of Laser Induced Breakdown Spectroscopy (LIBS) technique combined with Neural Networks (NN) in order to increase the quality assurance and authenticity issues. A total of thirty-eight red wine samples from different PDO were analyzed to detect fake wines and to avoid unfair competition in the market. LIBS is well known for not requiring sample preparation, however, in order to increase its analytical performance a new sample preparation treatment by previous liquid-to-solid transformation of the wine using a dry collagen gel has been developed. The use of collagen pellets allowed achieving successful classification results, avoiding the limitations and difficulties of working with aqueous samples. The performance of the NN model was assessed by three validation procedures taking into account their sensitivity (internal validation), generalization ability and robustness (independent external validation). The results of the use of a spectroscopic technique coupled with a chemometric analysis (LIBS-NN) are discussed in terms of its potential use in the food industry, providing a methodology able to perform the quality control of alcoholic beverages.

Qualitative and quantitative analysis of milk for the detection of adulteration by Laser Induced Breakdown Spectroscopy (LIBS)

The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R2) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product.

Solving the spectroscopy interference effects of beta-carotene and lycopene by neural networks.

In this study a new computerized approach and linear models (LMs) to solve the UV/vis spectroscopy interference effects of beta-carotene with lycopene analysis by neural networks (NNs) are considered. The data collected (absorbance values) obtained by UV/vis spectrophotometry were transferred into an NN-trained computer for modeling and prediction of output. Such an integrated NN/UV/vis spectroscopy approach is capable of estimating beta-carotene and lycopene concentrations with a mean prediction error 50 times lower than that calculated by the LM/UV/vis spectroscopy approach (without any previous physicochemical knowledge of the process to be modeled).

Neural Network Analysis of Spectroscopic Data of Lycopene and β-Carotene Content in Food Samples Compared to HPLC-UV-Vis

In this study a neural network (NN) model was designed to predict lycopene and beta-carotene concentrations in food samples, combined with a simple and fast technique, such as UV-vis spectroscopy. The measurement of the absorbance at 446 and 502 nm of different beta-carotene and lycopene standard mixtures was used to optimize a neural network based on a multilayer perceptron (MLP) (learning and verification process). Then, for validation purposes, the optimized NN has been applied to determine the concentration of both compounds in food samples (fresh tomato, tomato concentrate, tomato sauce, ketchup, tomato juice, watermelon, medlar, green pepper, and carrots), comparing the NN results with the known values of these compounds obtained by analytical techniques (UV-vis and HPLC). It was concluded that when the MLP-NN is used within the range studied, the optimized NN is able to estimate the beta-carotene and lycopene concentrations in food samples with an adequate accuracy, solving the UV-vis interference of beta-carotene and lycopene.

Lycopene: A Review of Chemical and Biological Activity Related to Beneficial Health Effects

Abstract This work focuses on the developments related to lycopene, a natural carotenoid and bioactive compound, particularly with reference to its chemistry and biological activity and its potential health effects. The formation of free radicals or other compounds in the body that are able to oxidize lipids, proteins, and DNA (also known as oxidative stress) is one of the major risk factors for chronic diseases. There is considerable evidence that lycopene has a protective effect against cardiovascular disease, hypertension, atherosclerosis, skin damage, and certain types of cancer such as prostate, breast, lung, and others. Because of this, the presence of lycopene in the diet is considered to be of great value. Dietary lycopene may increase the lycopene level in the body and act as an antioxidant. It may trap reactive oxygen species resulting in an increase in the overall antioxidant potential or a reduction in the oxidative damage to lipids (lipoproteins, membrane lipids), proteins (important enzymes), and DNA (genetic material), thereby lowering the oxidative stress. Alternatively, the increase in serum lycopene level may regulate gene functions, with the enhancement of intercellular communication (responsible for cell growth), modulating hormonal and immune response, regulating metabolism, and thus lowering the risk of chronic diseases. These mechanisms may also be interrelated and may act simultaneously to provide health benefits. Lycopene is quickly absorbed from different food sources (mainly tomato products) and distributed to corporal tissues where it maintains its antioxidant properties. This absorption varies depending on various factors such as food source, food processing, and other components in the diet. The human body is unable to synthesize carotenoids, such as lycopene, so a suitable diet intake is necessary to reach the adequate levels. In this review, the new developments in lycopene analysis by spectroscopic and chromatographic techniques along with mathematical modeling are also considered. These advances have made it possible to evaluate and determine the biological activity of lycopene in natural products. All this knowledge about the chemistry and biological activity of lycopene will be very helpful for the food industry, providing new opportunities in the field of food product development.

Laser induced breakdown spectroscopy for the discrimination of Candida strains.

The present study reports the evaluation of Laser Induced Breakdown Spectroscopy (LIBS) and Neural Networks (NN) for the discrimination of different strains of various species of Candida. This genus of yeast was selected due to its medical relevance as it is commonly found in cases of fungal infection in humans. Twenty one strains belonging to seven species of Candida were included in the study. Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) was employed as a complementary technique to provide information about elemental composition of Candida cells. The use of LIBS spectra in combination with optimized NN models provided reliable discrimination among the distinct Candida strains with a high spectral correlation index for the samples analyzed, without any false positive or false negative. Therefore, this study indicates that LIBS-NN based methodology has the potential to be used as fast fungal identification or even diagnostic method.

Quantitative Analysis of Roman Archeological Ceramics by Laser-Induced Breakdown Spectroscopy

ABSTRACT In recent decades, numerous analytical techniques have been used for the analysis of archeological samples. Laser-induced breakdown spectroscopy (LIBS) is a promising technique due to its practically nondestructive nature and minimal sample preparation. In this work, LIBS was used for the qualitative and quantitative elemental analyses of pottery manufactured in ancient settlements of Rome. The qualitative study showed that the ceramics were composed of Fe, Ca, and Mg. For quantitative analysis, calibration curves of Fe, Ca, and Mg were constructed with reference samples of each element in a KBr matrix with zinc as an internal standard. The results obtained by LIBS were compared with values obtained by atomic absorption.

Time-resolved study of the plasma produced from animal muscle tissue using a Nd:YAG laser

This paper reports studies on time-resolved laser induced breakdown spectroscopy (LIBS) of plasmas induced by Nd:YAG laser pulses on a sample lyophilized from swine muscle tissue. Samples were measured under vacuum conditions (0.1 Pa), and the laser was operated at 1064 nm, with 6–9 ns laser pulses. The behavior of specific neutral and ionized emission lines of K, Mg, Mg+, H, and O as well as molecular bands such as CN (B2Σ+–X2Σ+), C2 (d3Πg–a3Πu), NH (A3Π–X3Σ−) and N2 (c3Πu–B3Πg) was characterized. Spectroscopic diagnostics were used to determine the time-resolved electron density and excitation temperatures. This work complements our previous studies by comparing the different mechanisms of plasma formation depending on the source of excitation, both Nd:YAG and transversely excited atmospheric (TEA) CO2 lasers. Plasma parameters, electron densities and vibrational temperatures of CN were measured from the temporal analysis of Nd:YAG induced plasma and were compared with the TEA-CO2 induced plasma results. In addition, complementary Scanning Electron Microscopy (SEM) combined with Energy-Dispersive X-ray (EDX) spectroscopy and LIBS has been performed to assess sample homogeneity.