Hongsheng Tang

Bio: Hongsheng Tang is an academic researcher from Northwest University (China). The author has contributed to research in topics: Laser-induced breakdown spectroscopy & Random forest. The author has an hindex of 15, co-authored 26 publications receiving 668 citations.

Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods

Abstract: The laser induced breakdown spectroscopy (LIBS) technique coupled with a support vector machine (SVM) and partial least square (PLS) methods was proposed to perform quantitative and classification analysis of 20 slag samples. The characteristic lines (Ca, Si, Al, Mg and Ti) of LIBS spectra for slag samples can be identified based on the NIST database. At first, quantitative analysis of the major components (Fe2O3, CaO, SiO2, Al2O3, MgO and TiO2) in slag samples was completed by SVM with the full spectra as the input variable, and two parameters (kernel parameter of RBF-γ and σ2) of SVM were optimized by a grid search (GS) approach based on 5-fold cross-validation (CV). The performance of the SVM calibration model was investigated by 5-fold CV, and the prediction accuracy and root mean square error (RMSE) of SVM and PLS were employed to validate the predictive ability of the multivariate SVM calibration model in slag. The SVM model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. And then, two type of slag samples (open-hearth furnace slag and high titanium slag) were identified and classified by a partial least squares-discrimination analysis (PLS-DA) method with different input variables. Sensitivity, specificity and accuracy were calculated to evaluate the classification performance of the PLS-DA model for slag samples. It has been confirmed that the LIBS technique coupled with SVM and PLS methods is a promising approach to achieve the online analysis and process control of slag and even in the metallurgy field.

A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR)

Abstract: A novel method based on laser induced breakdown spectroscopy (LIBS) and random forest regression (RFR) was proposed for the quantitative analysis of multiple elements in fourteen steel samples. Normalized LIBS spectra of steel with characteristic lines (Si, Mn, Cr, Ni and Cu) identified by the NIST database were used as analysis spectra. Then, two parameters of RFR were optimized by out-of-bag (OOB) error estimation. The performance of the calibration model was investigated by different input variables (the whole spectral bands (220–800 nm) and spectra feature bands (220–400 nm)). In order to validate the predictive ability of the multiple element calibration RFR model in steel, we compared RFR with partial least-squares (PLS) and support vector machines (SVM) by means of prediction accuracy and root mean square error (RMSE). Thus, the RFR model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. This confirms that the LIBS technique coupled with RFR has good potential for use in the in situ rapid determination of multiple elements in steel and even in the field of metallurgy.

Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)

Abstract: Laser-induced breakdown spectroscopy (LIBS) integrated with random forest (RF) was developed and applied to the identification and discrimination of ten iron ore grades. The classification and recognition of the iron ore grade were completed using their chemical properties and compositions. In addition, two parameters of the RF were optimized using out-of-bag (OOB) estimation. Finally, support vector machines (SVMs) and RF machine learning methods were evaluated comparatively on their ability to predict unknown iron ore samples using models constructed from a predetermined training set. Although results show that the prediction accuracies of SVM and RF models were acceptable, RF exhibited better predictions of classification. The study presented here demonstrates that LIBS–RF is a useful technique for the identification and discrimination of iron ore samples, and is promising for automatic real-time, fast, reliable, and robust measurements.

Chemometrics in laser‐induced breakdown spectroscopy

Abstract: Laser‐induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of real‐time, online, and noncontact as well as enabling the simultaneous analysis of multiple elements. It has become a frontier analytical technique in spectral analysis. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by extracting useful information from a large amount of complex LIBS data remains the main problem for the LIBS technique. Chemometrics is a chemical subdiscipline of multi‐interdisciplinary methods; it offers advantages in data processing, signal analysis, and pattern recognition. It can solve some complicated problems that are difficult for traditional chemical methods. In this paper, we reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012‐2016).

Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines.

Abstract: The feasibility of steel materials classification by support vector machines (SVMs), in combination with laser-induced breakdown spectroscopy (LIBS) technology, was investigated Multi-classification methods based on SVM, the one-against-all and the one-against-one models, and a combination model, are applied to classify nine types of round steel Due to the inhomogeneity of steel composition, the data obtained using the one-against-all and one-against-one models were ambiguous and difficult to discriminate; whereas, the combination model, was able to successfully distinguish most of the ambiguous data and control the computation cost within an acceptable range The studies presented here demonstrate that LIBS-SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry

Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective

Abstract: Background The development of statistical software has enabled food scientists to perform a wide variety of mathematical/statistical analyses and solve problems. Therefore, not only sophisticated analytical methods but also the application of multivariate statistical methods have increased considerably. Herein, principal component analysis (PCA) and hierarchical cluster analysis (HCA) are the most widely used tools to explore similarities and hidden patterns among samples where relationship on data and grouping are until unclear. Usually, larger chemical data sets, bioactive compounds and functional properties are the target of these methodologies. Scope and approach In this article, we criticize these methods when correlation analysis should be calculated and results analyzed. Key findings and conclusions The use of PCA and HCA in food chemistry studies has increased because the results are easy to interpret and discuss. However, their indiscriminate use to assess the association between bioactive compounds and in vitro functional properties is criticized as they provide a qualitative view of the data. When appropriate, one should bear in mind that the correlation between the content of chemical compounds and bioactivity could be duly discussed using correlation coefficients.

Enzyme-Based Glucose Sensor: From Invasive to Wearable Device.

Abstract: Blood glucose concentration is a key indicator of patients' health, particularly for symptoms associated with diabetes mellitus. Because of the large number of diabetic patients, many approaches for glucose measurement have been studied to enable continuous and accurate glucose level monitoring. Among them, electrochemical analysis is prominent because it is simple and quantitative. This technology has been incorporated into commercialized and research-level devices from simple test strips to wearable devices and implantable systems. Although directly monitoring blood glucose assures accurate information, the invasive needle-pinching step to collect blood often results in patients (particularly young patients) being reluctant to adopt the process. An implantable glucose sensor may avoid the burden of repeated blood collections, but it is quite invasive and requires periodic replacement of the sensor owing to biofouling and its short lifetime. Therefore, noninvasive methods to estimate blood glucose levels from tears, saliva, interstitial fluid (ISF), and sweat are currently being studied. This review discusses the evolution of enzyme-based electrochemical glucose sensors, including materials, device structures, fabrication processes, and system engineering. Furthermore, invasive and noninvasive blood glucose monitoring methods using various biofluids or blood are described, highlighting the recent progress in the development of enzyme-based glucose sensors and their integrated systems.

Engineered Carbon-Nanomaterial-Based Electrochemical Sensors for Biomolecules

Abstract: The study of electrochemical behavior of bioactive molecules has become one of the most rapidly developing scientific fields. Biotechnology and biomedical engineering fields have a vested interest in constructing more precise and accurate voltammetric/amperometric biosensors. One rapidly growing area of biosensor design involves incorporation of carbon-based nanomaterials in working electrodes, such as one-dimensional carbon nanotubes, two-dimensional graphene, and graphene oxide. In this review article, we give a brief overview describing the voltammetric techniques and how these techniques are applied in biosensing, as well as the details surrounding important biosensing concepts of sensitivity and limits of detection. Building on these important concepts, we show how the sensitivity and limit of detection can be tuned by including carbon-based nanomaterials in the fabrication of biosensors. The sensing of biomolecules including glucose, dopamine, proteins, enzymes, uric acid, DNA, RNA, and H2O2 traditionally employs enzymes in detection; however, these enzymes denature easily, and as such, enzymeless methods are highly desired. Here we draw an important distinction between enzymeless and enzyme-containing carbon-nanomaterial-based biosensors. The review ends with an outlook of future concepts that can be employed in biosensor fabrication, as well as limitations of already proposed materials and how such sensing can be enhanced. As such, this review can act as a roadmap to guide researchers toward concepts that can be employed in the design of next generation biosensors, while also highlighting the current advancements in the field.

Disease Detection with Molecular Biomarkers: From Chemistry of Body Fluids to Nature-Inspired Chemical Sensors.

Abstract: This article aims to review nature-inspired chemical sensors for enabling fast, relatively inexpensive, and minimally (or non-) invasive diagnostics and follow-up of the health conditions. It can be achieved via monitoring of biomarkers and volatile biomarkers, that are excreted from one or combination of body fluids (breath, sweat, saliva, urine, seminal fluid, nipple aspirate fluid, tears, stool, blood, interstitial fluid, and cerebrospinal fluid). The first part of the review gives an updated compilation of the biomarkers linked with specific sickness and/or sampling origin. The other part of the review provides a didactic examination of the concepts and approaches related to the emerging chemistries, sensing materials, and transduction techniques used for biomarker-based medical evaluations. The strengths and pitfalls of each approach are discussed and criticized. Future perspective with relation to the information and communication era is presented and discussed.

Laser-induced breakdown spectroscopy (LIBS) for food analysis: A review

Abstract: Background Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopic technique which uses a focused pulsed laser beam to generate plasma from the material The plasma contains atoms, ions and free electrons which emit electromagnetic radiation as the plasma cools down The emitted light is resolved by a spectrometer to form a spectrum Recently, LIBS has become an emerging analytical technique for characterisation and identification of materials; its multi-elemental analysis, fast response, remote sensing, little to no sample preparation, low running cost and ease of use make LIBS a promising technique for the food sector Scope and approach The present article reviews the feasibility of LIBS for food analysis It presents recent progress and applications of LIBS as an efficient and reagent-free, at-line tool capable of replacing traditional time-consuming analytical methods for assessing the quality and composition of food products An overview of LIBS fundamentals, instrumentation and statistical data analysis is also provided Key findings and conclusions Although LIBS technology shows many advantages, challenges remain in terms of sample preparation, matrix effects, spectral pre-processing, model calibration and instrument development