A review of the main factors influencing the FT-IR-PLS abilities exemplified with petrochemical qualimetric applications

Quality control usually involves monitoring several variables directly related with industrial necessities using univariate tests. One powerful alternative is to link multivariate analytical techniques and multivariate chemometrics. In this way, Fourier Transform Infrared spectroscopy and Partial Least Squares regression are used to discuss and review several advantages and drawbacks encountered in using such combination in industrial facilities. Typical drawbacks are selection of data pretreatment, errors in reference methods, selection of calibration and validation sets and model-aging. This review is exemplified with petrochemical applications although other fields are also considered (mainly when dealing with data pretreatment).     

Comparison of methods for outlier detection and their effects on the classification results for a particular data base

One of the drawbacks for using linear discriminant analysis (LDA) is the presence of outliers. Some methods of detecting outliers are compared and applied to a particular data base. When multivariate methods (multinormal distribution procedure and Hawkins' procedure) were applied, the two subsets produced did not differ greatly. Assumptions needed for the application of LDA were evaluated for each subset. Classification ability, feature selection and prediction ability were considered for each subset. Results for each subset were quite different. Hawkins' procedure seems the better method for detecting outliers.     

Analysis of the interaction of radionuclides with solid phase in surface waters using laboratory model experiments: Methodical problems

Use of laboratory model experiments for analysis of interaction of radionuclides with surface water solids is connected with methodical problems concerning selection of solid phase, sampling, storage and pretreatment of solid phase, concentration of radionuclide, selection of liquid to solid ratio, adsorption of radionuclide on vessel walls, choice of experimental procedures and change in radionuclide speciation in the solid phase. These problems are discussed on the basis of literature data and of experiments with radiocobalt or radiocesium in a system river water-suspended solids or bottom sediments. Possible ways of solution of the problems are shown. Two modifications of batch method are described in detail and their merits or drawbacks evaluated. Comparison of the modifications shows their possible use as complementary methods for the study of radionuclide behaviour in surface waters.     

Kinetic analysis of cellulose pyrolysis: a short review

Since the 1950s, cellulose pyrolysis has been the subject of intense study, with kinetic analyses forming a major part of these studies. They represent useful tools for a better understanding of the physicochemical process and for the proper design of industrial pyrolysis units. Until recently, the methods most frequently used in these analyses were based on model-fitting, i.e. the fitting of the experimental data to a number of mathematical models. Nowadays, other methods, so-called “model-free” methods, are considered to be more suited. These are based on the principle that, at constant conversion, the reaction rate depends only on temperature. In its first part, this short review presents the particularities and drawbacks of the traditional model-fitting models. Subsequently, several main contributions in this field are listed and discussed. Finally, the more suited “model-free” (isoconversional) methods are explained and several main studies presented, as well as a comparison of this method with the model-fitting ones.     

Adsorption of phenolic compounds on low-cost adsorbents: A review

Adsorption techniques are widely used to remove certain classes of pollutants from wastewater. Phenolic compounds represent one of the problematic groups. Although commercial activated carbon is a preferred adsorbent for phenol removal, its widespread use is restricted due to the high cost. As such, alternative non-conventional adsorbents have been investigated. The natural materials, waste materials from industry and agriculture and bioadsorbents can be employed as inexpensive adsorbents. The review (i) presents a critical analysis of these materials; (ii) describes their characteristics, advantages and limitations; and (iii) discusses the various mechanisms involved. There are several issues and drawbacks concerned on the adsorption of phenolic compounds that have been discussed in this review article. It is evident from the review that low-cost adsorbents have demonstrated high removal capabilities for certain phenolic compounds. In particular, industrial waste might be a promising adsorbent for environmental and purification purposes.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.     

Gas sensors based on nanostructured materials

Gas detection is important for controlling industrial and vehicle emissions, household security and environmental monitoring. In recent decades many devices have been developed for detecting CO(2), CO, SO(2), O(2), O(3), H(2), Ar, N(2), NH(3), H(2)O and several organic vapours. However, the low selectivity or the high operation temperatures required when most gas sensors are used have prompted the study of new materials and the new properties that come about from using traditional materials in a nanostructured mode. In this paper, we have reviewed the main research studies that have been made of gas sensors that use nanomaterials. The main quality characteristics of these new sensing devices have enabled us to make a critical review of the possible advantages and drawbacks of these nanostructured material-based sensors.     

Sample pretreatment strategies for total reflection X-ray fluorescence analysis: A tutorial review

In the last years, there has been a revival of total reflection X-ray fluorescence spectrometry (TXRF), which was firstly applied for analytical purposes in the late 80s. The aim of this work is to discuss and compare the current approaches for sample pretreatment including in situ microdigestion, slurry preparation, acid digestion, extraction, etc. prior to TXRF analysis. Advantages and drawbacks inherent to each of those procedures are considered. A comprehensive revision in the period January 2008–July 2013 about different sample preparation strategies prior to TXRF analysis apart from early pioneering reports dealing with sample pretreatment are included in the review. Non-conventional sample pretreatment approaches such as microflow online preconcentration, lab-on-a-chip, etc., are also discussed.Finally, future prospects in sample preparation prior to TXRF analysis are outlined.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.     

Evaluation of a spectral searching algorithm for the comparison of Raman band positions

Raman spectroscopic identification of unknown materials involves often the comparison of the spectrum of the unknown spectrum with previously recorded reference spectra or data from literature. However, when spectra with many Raman bands or spectra of mixtures are involved, searching can be quite complex. Different chemometrical approaches have been proposed, but these have some drawbacks. Therefore, in this paper a novel approach is proposed, which is based on a multivariate comparison of Raman band positions. Different similarity measures can be used and are evaluated with spectra of test samples that were recorded on different spectrometers, using different laser wavelengths. Moreover, this study evaluates the performances of this algorithm for identifying different compounds in mixtures, by using an iterative approach.     

基于人工智能和互联网时代的化合物毒性预测综述

随着商品中所含各种化合物的不断使用,人们日益关注其对人类及生态环境的安全危害。在过去的几年里,通过计算方法预测化合物毒性已经显示出极大的潜力。在此,总结了常用的机器学习和深度学习算法在建立毒性预测模型上的优缺点,并系统回顾了近三年发表的可免费访问的毒性预测网络服务器。此外,还讨论了基于人工智能和互联网时代下毒性预测所面临的机遇和挑战。希望指导人们合理的选择算法和网络服务器进行建模及化合物毒性评估。     

Application of microbioreactors in fermentation process development: a review

Biotechnology process development involves strain testing and improvement steps aimed at increasing yields and productivity. This necessitates the high-throughput screening of many potential strain candidates, a task currently mainly performed in shake flasks or microtiter plates. However, these methods have some drawbacks, such as the low data density (usually only end-point measurements) and the lack of control over cultivation conditions in standard shake flasks. Microbioreactors can offer the flexibility and controllability of bench-scale reactors and thus deliver results that are more comparable to large-scale fermentations, but with the additional advantages of small size, availability of online cultivation data and the potential for automation. Current microbioreactor technology is analyzed in this review paper, focusing on its industrial applicability, and directions for future research are presented.     

Palladium-assisted multicomponent synthesis of 2-aryl-4-aminoquinolines and 2-aryl-4-amino[1,8]naphthyridines

A palladium-mediated multicomponent domino reaction leading to 2-aryl-4-amino-quinolines and 2-aryl-4-amino[1,8]naphthyridines is reported. The scope of the reaction was examined using carbon monoxide, two 2-ethynyl-arylamines, four aryl iodides, and 10 primary amines as substrates. The selection of the appropriate catalytic system was achieved testing several palladium/phosphine systems and overrides previously reported drawbacks associated with the use of primary amines in related reactions. Moreover several features concerning the role of both palladium [(0) and (II)] and phosphines are reported.     

The application to wastewaters of chemometric approaches to handling problems of highly complex matrices

This overview covers current chemometric methodologies using second-order advantage to solve problems of analyzing highly complex matrices. Among the existing algorithms, it focuses on those most frequently used (e.g., the standard for second-order approaches to data analysis, PARAFAC (parallel factor analysis), and MCR-ALS (multivariate curve resolution alternating least squares), as well as the most recently implemented BLLS (bilinear least-squares), and U-PLS/RBL (unfolded partial least squares/residual bilinearization)). All of these are based on linear models. The overview also covers ANN/RBL (artificial neural networks followed by residual bilinearization), which achieves the second-order advantage in systems involving non-linear behavior. In addition, the overview deals with the drawbacks of these approaches, as well as other drawbacks that are inherent in the analytical techniques to question.     

Adsorbent materials commonly used in air analysis for adsorptive enrichment and thermal desorption of volatile organic compounds

A review is given dealing with commonly used adsorbent materials in ambient air analysis of volatile organic compounds (VOCs). The adsorbents covered in the paper are selected in consideration of their compatibility with thermal desorption. Initially, we discuss the requirements that an adsorbent should fulfill, and useful parameters for the selection and evaluation of an appropriate material. Then, the most important materials are presented considering their properties, advantages, and drawbacks. A few applications are given, but a complete review of sampling techniques and applications dealing with adsorptive enrichment and thermal desorption is beyond the scope of this paper.     

Sample preparation techniques based on combustion reactions in closed vessels — A brief overview and recent applications

In this review, a general discussion of sample preparation techniques based on combustion reactions in closed vessels is presented. Applications for several kinds of samples are described, taking into account the literature data reported in the last 25 years. The operational conditions as well as the main characteristics and drawbacks are discussed for bomb combustion, oxygen flask and microwave-induced combustion (MIC) techniques. Recent applications of MIC techniques are discussed with special concern for samples not well digested by conventional microwave-assisted wet digestion as, for example, coal and also for subsequent determination of halogens.     

Recent Advances in the Development of Sustainable Composite Materials used as Membranes in Microbial Fuel Cells

MFC can have dual functions; they can generate electricity from industrial and domestic effluents while purifying wastewater. Most MFC designs comprise a membrane which physically separates the cathode and anode compartments while keeping them electrically connected, playing a significant role in its efficiency. Popular commercial membranes such as Nafion, Hyflon and Zifron have excellent ionic conductivity, but have several drawbacks, mainly their prohibitive cost and non-biodegradability, preventing the large-scale application of MFC. Fabrication of composite materials that can function better at a much lower cost while also being environment-friendly has been the endeavor of few researchers over the past years. The current review aims to apprise readers of the latest trends of the past decade in fabricating composite membranes (CM) for MFC. For emphasis on environmental-friendly CM, the review begins with biopolymers, moving on to the carbon-polymer, polymer-polymer, and metal-polymer CM. Lastly, critical analysis towards technology-oriented propositions and realistic future directives in terms of strengths, weakness, opportunities, challenges (SWOC analysis) of the application of CM in MFC have been discussed for their possible large-scale use. The focus of this review is the development of hybrid materials as membranes for fuel cells, while underscoring the need for environment-friendly composites and processes.     

Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches

Wavelet regression is a very promising technique for modern multivariate calibration and calibration transfer. Multiscale analysis of wavelet scales provides a connection between wavelet regression and data fusion. In this paper, current wavelet regression methods are reviewed from the novel perspective of data fusion. Illustrated by analysis of a public domain near-infrared dataset, the advantages and drawbacks of these methods are examined. For wavelet regression, the non-uniformity of the wavelet components, the multiscale nature of the signal, and the prevention of information leakage are crucial issues that will be addressed.     

A multivariate chemometric approach to fluorescence spectroscopy

A multivariate approach to the solution of problems often encountered in the spectrofluorometry of natural samples, utilising information from whole spectra is presented. (a) Piecewise direct standardisation is implemented and employed to transfer emission spectra measured with two different xenon lamps of different ages as if the spectra were measured with the same lamp. (b) It has been shown using a multivariate analysis approach that it is possible to use the raw data points instead of the smoothed data based on an algorithm included in the instrument software by the manufacturer. (c) It is documented that Raman scattering does not hamper the performance of multivariate calibration; on the contrary, in an experiment with sugar samples the concentration prediction errors become about five times lower by including the whole emission spectrum in the analysis instead of using a univariate calibration based on an emission wavelength that only reflects the analyte of interest. (d) An algorithm for variable selection is implemented and employed in the selection of optimal excitation wavelengths. Among 13 emission spectra recorded for a sugar sample at different excitation wavelengths, four of these are chosen that describe 98.51% of the total variance in the original data. (e) Finally the combination of fluorescence spectroscopy and multivariate calibration with conventional chemical data according to the near-infrared black box model is presented. The refined sugar quality parameter, the ash content and the fluorescence emission spectra are correlated by a partial least-squares regression model. Five experiments employing different monochromator slit widths and sugar concentrations are performed, and the best correlation obtained by full cross-validation of the 15 sugar samples is R = 0.98.