Monitoring of pigmented and wooden surfaces in accelerated ageing processes by FT-Raman spectroscopy and multivariate control charts

Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected.The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events.     

Monitoring of pigmented surfaces in accelerated ageing process by ATR-FT-IR spectroscopy and multivariate control charts

This work is an extension of a method for monitoring the conservation state of pigmented surfaces presented in a previous paper. A cotton canvas painted with an organic pigment (Alizarin) was exposed to UV light in order to evaluate the effects of the applied treatment on the surface of the sample. The conservation state of the pigmented surface was monitored with ATR–FT-IR spectroscopy and multivariate control charts.

The IR spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart, cumulative sums (CUSUM) and simultaneous scores monitoring and residuals tracking (SMART) control charts.

These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surface of the sample.

Finally, with the aim to more deeply investigate what happened to the sample surface during the UV exposure, a PCA of the residuals matrix of degradation analyses only, not present in the previous paper, was performed. This analysis produced interesting results concerning the identification of the processes taking place on the irradiated surface.     

The state of conservation of painted surfaces in the presence of accelerated ageing processes monitored by use of FT-Raman spectroscopy and multivariate control charts

A new method has been developed for monitoring the degradation of paintings. Two inorganic pigments (ultramarine blue and red ochre) were blended with linseed oil and spread on canvas. Each canvas was subjected to simulated accelerated ageing in the presence of typical degradation agents (UV radiation and acidic solution). Periodically the painted surfaces were analysed by FT-Raman, to investigate the status of the surface. The data obtained were analysed by principal component analysis (PCA). Finally the Shewhart and cumulative sum control charts based on the relevant principal components (PC) and the so called scores monitoring and residuals tracking (SMART) charts were built. The method based on the use of PC to describe the process was found to enable identification of the presence of relevant modification occurring on the surface of the samples studied.Electronic supplementary material Supplementary material is available for this article at     

Monitoring of paintings under exposure to UV light by ATR-FT-IR spectroscopy and multivariate control charts

This paper concerns the improvement of a method, already applied for the conservation state monitoring of wooden and painted surfaces, to a system closely simulating a real artwork, namely a canvas painted with mixtures of three organic pigments (Alizarin, Permanent Red, Phtalocyanine Green). Ten mixtures of these pigments, according to an augmented simplex-centroid design, were prepared, mixed with linseed oil and spread on 10 cotton canvas strips. Drying ended, all the samples were analysed by ATR-FT-IR spectroscopy to describe the superficial variability in normal conditions of conservation, i.e. when no degradation is present. Successively, the samples were exposed to artificial UV light simulating the action of an aggressive portion of sunlight. The IR spectra of the surfaces were regularly acquired to monitor the superficial changes due to the UV aggression. Treatment ended, a chemometric study based on the Principal Component Analysis of the spectroscopic data collected both in normal conditions of conservation and during the artificial accelerated ageing, was performed and the multivariate Shewhart and Cusum control charts were built with the scores of the significant PCs (principal components). PCA based control charts showed to be able to identify the presence of significant changes of the painted surfaces, to identify the starting of the degradations and to provide insights about the chemical alterations induced by the UV exposure.     

Fermentation diagnosis by multivariate statistical analysis

During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).     

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).     

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).     

Batch process monitoring using on-line MIR spectroscopy

Many high quality products are produced in a batch wise manner. One of the characteristics of a batch process is the recipe driven nature. By repeating the recipe in an identical manner a desired end-product is obtained. However, in spite of repeating the recipe in an identical manner, process differences occur. These differences can be caused by a change of feed stock supplier or impurities in the process. Because of this, differences might occur in the end-product quality or unsafe process situations arise. Therefore, the need to monitor an industrial batch process exists. An industrial process is usually monitored by process measurements such as pressures and temperatures. Nowadays, due to technical developments, spectroscopy is more and more used for process monitoring. Spectroscopic measurements have the advantage of giving a direct chemical insight in the process. Multivariate statistical process control (MSPC) is a statistical way of monitoring the behaviour of a process. Combining spectroscopic measurements with MSPC will notice process perturbations or process deviations from normal operating conditions in a very simple manner. In the following an application is given of batch process monitoring. It is shown how a calibration model is developed and used with the principles of MSPC. Statistical control charts are developed and used to detect batches with a process upset.     

A novel multi‐mode data processing method and its application in industrial process monitoring

Multi‐mode process monitoring is a key issue often raised in industrial process control. Most multivariate statistical process monitoring strategies, such as principal component analysis (PCA) and partial least squares, make an essential assumption that the collected data follow a unimodal or Gaussian distribution. However, owing to the complexity and the multi‐mode feature of industrial processes, the collected data usually follow different distributions. This paper proposes a novel multi‐mode data processing method called weighted k neighbourhood standardisation (WKNS) to address the multi‐mode data problem. This method can transform multi‐mode data into an approximately unimodal or Gaussian distribution. The results of theoretical analysis and discussion suggest that the WKNS strategy is more suitable for multi‐mode data normalisation than the z‐score method is. Furthermore, a new fault detection approach called WKNS‐PCA is developed and applied to detect process outliers. This method does not require process knowledge and multi‐mode modelling; only a single model is required for multi‐mode process monitoring. The proposed method is tested on a numerical example and the Tennessee Eastman process. Finally, the results demonstrate that the proposed data preprocessing and process monitoring methods are particularly suitable and effective in multi‐mode data normalisation and industrial process fault detection. Copyright © 2014 John Wiley & Sons, Ltd.     

Presence of nonylphenol, octyphenol and bisphenol a in two aquifers close to agricultural, industrial and urban areas

Active endocrine disruptors (nonylphenol, octylphenol and bisphenol A) were analysed in 2 aquifers and the corresponding surface waters. They are compounds widely used in industrial processes. The objective of this study was to determine the leaching potential of these compounds in groundwaters and to eventually correlate these levels with surface water samples. The areas sampled were agricultural, close to large cities and with an important industrial activity in the surrounding area. Samples (200 mL) were extracted using off-line SPE with polymeric OASIS 60 mg cartridges. Analyses were performed by gas chromatography-mass spectrometry (GC-MS) using selected ion monitoring (SIM) and full scan for quantification and unequivocal identification, respectively. This paper reports the detection limit for the compounds studied (from 0.001 to 0.030 μg L⁻¹), and method performance as regards to linearity (0.01–1.3 μg L⁻¹), reproducibility (less than 9%) and recovery (84 to 95%). The results from a monitoring program revealed the presence of the target compounds in all samples analysed, at levels of 0.07 and 1.9 μg L⁻¹. The presence of these compounds in groundwater was attributed basically to degradation of inert ingredients present in the formulation of many pesticides or to the increasing application of sludge in agricultural practice, although the infiltration of industrial run-off and wastewater disposal cannot be disregarded.     

Fault diagnosis using kNN reconstruction on MRI variables

In industrial processes, investigating the root causes of abnormal events is a crucial task when process faults are detected; isolating the faulty variables provides additional information for investigating the root causes of the faults. The traditional contribution plot is a popular and perspicuous tool to isolate faulty variables. However, this method can only determine one faulty variable (the biggest contributor) when there are several variables out of control at the same time. In the presented work, a novel fault diagnosis method is derived using k‐nearest neighbor (kNN) reconstruction on maximize reduce index (MRI) sensors; it is aimed at identifying all fault variables precisely. This method can identify the faulty variables effectively through reconstructing MRI variables one by one. A numerical example focuses on validating the performance of kNN missing data analysis method firstly, then multi‐sensors fault identification results are also given. Tennessee Eastman process is provided to demonstrate that the proposed approach can identify the responsible variables for the multiple sensors fault. Copyright © 2015 John Wiley & Sons, Ltd.     

Multiscale and megavariate monitoring of the process networked structure: M2NET

We present a process monitoring scheme aimed at detecting changes in the networked structure of process data that is able to handle, simultaneously, three pervasive aspects of industrial systems: (i) their multivariate nature, with strong cross‐correlations linking the variables; (ii) the dynamic behavior of processes, as a consequence of the presence of inertial elements coupled with the high sampling rates of industrial acquisition systems; and (iii) the multiscale nature of systems, resulting from the superposition of multiple phenomena spanning different regions of the time‐frequency domain. Contrary to current approaches, the multivariate structure will be described through a local measure of association, the partial correlation, in order to improve the diagnosis features without compromising detection speed. It will also be used to infer the relevant causal structure active at each scale, providing a fine map for the complex behavior of the system. The scale‐dependent causal networks will be incorporated in multiscale monitoring through data‐driven sensitivity enhancing transformations (SETs). The results obtained demonstrate that the use of SET is a major factor in detecting process upsets. In fact, it was observed that even single‐scale monitoring methodologies can achieve comparable detection capabilities as their multiscale counterparts as long as a proper SET is employed. However, the multiscale approach still proved to be useful because it led to good results using a much simpler SET model of the system. Therefore, the application of wavelet transforms is advantageous for systems that are difficult to model, providing a good compromise between modeling complexity and monitoring performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

In this work, mid‐infrared spectroscopy and multivariate control charts based on net analyte signal were applied for quality control of B5 blends of biodiesel/diesel (5% biodiesel/95% diesel). Control charts were constructed using instrumental signal decomposition, generating three charts: the net analyte signal chart for monitoring the analyte of interest (methyl soybean biodiesel); the interference chart, which corresponds to the contribution of all other compounds in the diesel sample (diesel); and the residual chart, which corresponds to non‐systematic variations. Statistical limits were established for each developed chart, using samples inside quality specifications (normal operation conditions). To validate multivariate control charts, new samples were analyzed. The new samples represented samples in‐control and samples out‐of‐control in relation to the content of biodiesel, adulterated biodiesel with severe vegetable oils and adulterated diesel with residual automotive lubricant oil, kerosene, and gasoline. The results obtained show an excellent distinction between the samples inside and out of the quality specifications, with 91% and 100% correctly classified, respectively, which demonstrates that the methodology developed is a viable alternative for quality monitoring of this type of fuel. Copyright © 2015 John Wiley & Sons, Ltd.     

Monitoring analytical measurements in presence of two component measurement error

Control charts are increasingly adopted by laboratories for effective monitoring of analytical processes. Analytical methods are mostly subject to two types of measurement errors, i—additive and ii—multiplicative, or proportional, error. These errors have been combined in a single model, namely the two component error model (TCME) proposed by [1]. In this study we present a comparison among the performance of three widely used location control charts, i.e. Shewhart, CUSUM and EWMA charts in presence of TCME model. This study will help quality practitioners to choose an efficient chart for the monitoring of analytical measurements.     

Multivariate statistical analysis of a multi-step industrial processes

Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors. This approach will show how the process develops from a data point of view. The procedure is illustrated on a relatively simple industrial batch process, but it is also applicable in a general context, where knowledge about the variables is available.     

Method for monitoring sensor stability in a multiple semiconductor gas-sensor system

An empirical method is reported for monitoring the long-term stability of a multiple semiconductor gas-sensor system. The method is based on control charts, modified for the multivariate data set. This modified is used to assess the data produced simultaneously from eight gas sensors on exposure to acetone over 102 days. It is shown that the multisensor system provides adequate long-term stability for recognition of gaseous materials.     

Enhanced monitoring of biopharmaceutical product purity using liquid chromatography-mass spectrometry

LC-MS is a widely used technique for impurity detection and identification. It is very informative and generates huge amounts of data. However, the relevant chemical information may not be directly accessible from the raw data map, particularly in reference to applications where unknown impurities are to be detected. This study demonstrates that multivariate statistical process control (MSPC) based on principal component analysis (PCA) in conjunction with multiple testing is very powerful for comprehensive monitoring and detection of an unknown and co-eluting impurity measured with liquid chromatography-mass spectrometry (LC-MS). It is demonstrated how a spiked impurity present at low concentrations (0.05% (w/w)) is detected and further how the contribution plot provides clear diagnostics of the unknown impurity. This tool makes a fully automatic monitoring of LC-MS data possible, where only relevant areas in the LC-MS data are highlighted for further interpretation.     

Chemometric investigations on environmental data from Carlo Alberto irrigation canal (Alessandria,Piedmont, Italy)

The Carlo Alberto Canal connects Bormida and Tanaro rivers in Piedmont (ITALY). It was created for irrigation purposes but since its waters are suspected to be polluted, a sampling campaign was performed by the ARPA of Alessandria. The physico-chemical parameters analysed along 3 years (1998-2000) were investigated by multivariate chemometric methods. PCA showed that the waters situation depends heavily on the sampling period. Also a Kohonen self-organising map confirmed the clustering observed, providing insights into the causes of the clusterisation. New samplings are now being performed and a larger set of environmental variables is determined on each sample.     

Study of the application of multiway multivariate techniques to model data from an industrial fermentation process

Several multivariate statistical techniques have been extensively proposed for monitoring industrial processes. In this paper, multiway extensions of two such techniques: multiway principal component analysis (MPCA) and multiway partial least squares regression (MPLS) were applied to a large data set from an industrial pilot-scale fermentation process to improve process knowledge. The MPCA model is able to diagnose faults occurring in the process whether they affect or not process productivity while the MPLS model enables the prediction of final product concentration and the detection of faults that will influence the fermentation productivity.     

Stopping rule for variable selection using stepwise discriminant analysis

Summary In general, when characterizing samples, such as ceramic samples or other types of samples, for first time by means of chemical elements, the analyst measures a large number of variables, many of which may not be very informative. In fact, some may even be unrelated to the issue at hand and blur the picture instead of making it clearer. In subsequent studies the analyst may wish to measure fewer variables for several reasons, such as being very time consuming; in cases where measurement time is important, such as on-line monitoring; in order to reduce cost or effort; etc. Therefore, the hope is to determine those variables that are most relevant without losing essential information and to remove the less productive information. The problem is how to perform this in an objective way and to capture crucial information using a multivariate analysis. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variable subsets, preserving multivariate data structure using stepwise discriminant analysis, selecting those variables that are in some senses adequate for discrimination purposes. One illustrative example using data sets obtained via INAA of ceramic samples from two archaeological sites is provided.