Marco S. Reis

Aroma ageing trends in GC/MS profiles of liqueur wines.

Madeira wine has been studied with the main goal of acquiring a better understanding about the evolution of its properties over time. For that purpose, flexible and reliable data analysis tools were employed to characterize wines at different ageing stages, using flavour chromatography measurements. In this paper we present the results from such a study, where the main differences in the aroma profiles and their development in different types of aged Madeira wines are analyzed and evaluated according to their discriminating power. An exploratory multivariate data analysis was conducted using two different tools, namely biplots and contributions plots obtained through principal component analysis (PCA). In order to take advantage of the maximum amount of information provided by the chromatography data sets, a new approach that incorporates samples variability in the analysis of the statistical significance of contributions estimates, was developed and tested. In this way, it was possible to analyze which volatile compounds have statistically significant and/or similar contributions regarding the observed separation of wine samples from different groups. Furthermore, since several chemical compounds are expected to change together as a result of the ageing-related chemical reactions, they were clustered according to a similarity criterion relative to their importance in the trends observed in the scores space. Results obtained provide a sound basis for the differentiation and characterization of the ageing process followed by Madeira wines.

Multiscale Statistical Process Control of Paper Surface Profiles

Abstract Paper surface plays a key role in paper quality. Accurate paper surface profiles contain the fundamental raw information of the surface for a wide range of length-scales, to which different aspects of the paper quality are connected. With the goal of exploring the availability of such paper surface data obtained through a mechanical stylus profilometer, we present in this paper an approach for setting up a Multiscale SPC procedure that monitors simultaneously two key quality surface phenomena that develop at different scales: roughness and waviness. The raw profiles, after adequate processing using a multiscale framework based on wavelets, give rise to quantities that can be effectively used to monitor these two phenomena in a simple and integrated way, and therefore be implemented in practice for quality control purposes. The effectiveness of the proposed procedure is assessed by simulation as well as through a pilot study involving real paper surface profiles.

Denoising and Signal-to-Noise Ratio Enhancement: Wavelet Transform and Fourier Transform

This chapter introduces the methods of Fourier and wavelet analysis for enhancing the signal-to-noise ratio in typical chemometric and other measured data. Fourier analysis has been popular for many decades but is best suited for enhancing signals where most features are localized in frequency. In contrast, wavelet analysis is appropriate for signals that contain features localized in both time and frequency. It also retains the benefits of Fourier analysis such as orthono, mality and computational efficiency. Practical algorithms for off-line and on-line denoising are described and compared via simple examples. These algorithms can be used for off-line or on-line data and can remove Gaussian as well as non-Gaussian noise.

Optimal design of experiments applied to headspace solid phase microextraction for the quantification of vicinal diketones in beer through gas chromatography-mass spectrometric detection

Vicinal diketones, namely diacetyl (DC) and pentanedione (PN), are compounds naturally found in beer that play a key role in the definition of its aroma. In lager beer, they are responsible for off-flavors (buttery flavor) and therefore their presence and quantification is of paramount importance to beer producers. Aiming at developing an accurate quantitative monitoring scheme to follow these off-flavor compounds during beer production and in the final product, the head space solid-phase microextraction (HS-SPME) analytical procedure was tuned through experiments planned in an optimal way and the final settings were fully validated. Optimal design of experiments (O-DOE) is a computational, statistically-oriented approach for designing experiences that are most informative according to a well-defined criterion. This methodology was applied for HS-SPME optimization, leading to the following optimal extraction conditions for the quantification of VDK: use a CAR/PDMS fiber, 5 ml of samples in 20 ml vial, 5 min of pre-incubation time followed by 25 min of extraction at 30 °C, with agitation. The validation of the final analytical methodology was performed using a matrix-matched calibration, in order to minimize matrix effects. The following key features were obtained: linearity (R(2) > 0.999, both for diacetyl and 2,3-pentanedione), high sensitivity (LOD of 0.92 μg L(-1) and 2.80 μg L(-1), and LOQ of 3.30 μg L(-1) and 10.01 μg L(-1), for diacetyl and 2,3-pentanedione, respectively), recoveries of approximately 100% and suitable precision (repeatability and reproducibility lower than 3% and 7.5%, respectively). The applicability of the methodology was fully confirmed through an independent analysis of several beer samples, with analyte concentrations ranging from 4 to 200 g L(-1).

Evaluation of the presence of major anionic surfactants in marine sediments.

The contamination of aquatic environments has become the focus of increasing regulation and public concern due to their potential and unknown negative effects on the ecosystems. The present work develops a monitoring and statistical study, based on the analysis of variance test (ANOVA) and the multivariable analysis, both for insoluble soap and LAS in order to compare the behavior of different anionic surfactants in this environmental compartment. First, a novel and successfully validated methodology to analyze insoluble soap in these samples is developed. The matrix effect and the comparison of different extraction techniques were also performed. The optimized analytical methodologies were applied to 48 representative samples collected from the Almeria Coast (Spain) and then a statistical analysis to correlate anionic surfactant concentration and several variables associated with marine sediment samples was also developed. The results obtained showed relevant conclusions related to the environmental behavior of anionic surfactants in marine sediments.

Statistical Process Control of Multivariate Systems with Autocorrelation

Current industrial processes are characterized by encompassing a large number of interdependent variables, which very often exhibit autocorrelated behavior, due to the dynamic nature of the phenomena involved, associated with the high sampling rates of modern data acquisition systems. Multivariate statistical process control charts have been developed to handle the cross-correlation issue, such as the Hotellings T2, MEWMA and MCUSUM control charts, but they are not able to handle properly the presence of autocorrelation in data. In order to address both problems simultaneously, alternative procedures were developed, namely by adapting the control limits, using residuals from time series modeling and applying data transformation techniques, some of which will be addressed in this paper, along with others we now propose. The proposed monitoring methods use a combination of Dynamic PCA (DPCA), ARMA models and missing data estimation methods, allowing for the simultaneous reduction of data dimensionality while capturing its dynamic behavior, therefore also handling the autocorrelation effects. The results obtained show that the proposed methodologies based upon missing data estimation tend to present better performance, constituting good alternatives to methodologies currently in use.

Translation-Invariant Multiscale Energy-Based PCA for Monitoring Batch Processes in Semiconductor Manufacturing

The overwhelming majority of processes taking place in semiconductor manufacturing operate in a batch mode by imposing time-varying conditions to the products in a cyclic and repetitive fashion. These conditions make process monitoring a very challenging task, especially in massive production plants. Among the state-of-the-art approaches proposed to deal with this problem, the so-called multiway methods incorporate the batch dynamic features in a normal operation model at the expense of estimating a large number of parameters. This makes these approaches prone to overfitting and instability. Moreover, batch trajectories are required to be well aligned in order to provide the expected performance. To overcome these issues and other limitations of the conventional methodologies for process monitoring in semiconductor manufacturing, we propose an approach, translation-invariant multiscale energy-based principal component analysis, that requires a much lower number of estimated parameters. It is free of process trajectory alignment requirements and thus easier to implement and maintain, while still rendering useful information for fault detection and root cause analysis. The proposed approach is based on implementing a translation-invariant wavelet decomposition along the time series profile of each variable in one batch. The normal operational signatures in the time-frequency domain are extracted, modeled, and then used for process monitoring, allowing prompt detection of process abnormalities. The proposed procedure was tested with real industrial data and it proved to effectively detect the existing faults as well as to provide reliable indications of their underlying root causes.

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

Evaluation of Linear Alkylbenzene Sulfonate (LAS) behaviour in agricultural soil through laboratory continuous studies

The behaviour of Linear Alkylbenzene Sulfonate (LAS) in agricultural soil is investigated in the laboratory using continuous-flow soil column studies in order to simultaneously analyze the three main underlying phenomena (adsorption/desorption, degradation and transport). The continuous-flow soil column experiments generated the breakthrough curves for each LAS homologue, C10, C11, C12 and C13, and by adding them up, for total LAS, from which the relevant retention, degradation and transport parameters could be estimated, after proposing adequate models. Several transport equations were considered, including the degradation of the sorbate in solution and its retention by soil, under equilibrium and non-equilibrium conditions between the sorbent and the sorbate. In general, the results obtained for the estimates of those parameters that were common to the various models studied (such as the isotherm slope, first order degradation rate coefficient and the hydrodynamic dispersion coefficient) were rather consistent, meaning that mass transfer limitations are not playing a major role in the experiments. These three parameters increase with the length of the LAS homologue chain. The study will provide the underlying conceptual framework and fundamental parameters to understand, simulate and predict the environmental behaviour of LAS compounds in agricultural soils.

Advanced predictive methods for wine age prediction: Part II – A comparison study of multiblock regression approaches

In this article, we extend the scope of the first paper of the sequel, which was dedicated to the analysis of advanced single-block regression methods (Rendall et al., 2016) [1], to the class of multiblock regression approaches. The datasets contemplated for developing the multiblock approaches are the same as in the former publication: volatile, polyphenols, organic acids composition and the UV-Vis spectra. The context is still the prediction of the ageing time of one of finest Portuguese fortified wines, the Madeira Wine, but now the data collected from the different analytical sources is explored simultaneously, in a more structured and informative way, through multiblock methodologies. The goal of this paper is to provide a critical assessment of a rich variety of multiblock regression methods, namely: Concatenated PLS, Multiblock PLS (MBPLS), Hierarchical PLS (HPLS), Network-Induced Supervised Learning (NI-SL) and Sequential Orthogonalised Partial Least Squares (SO-PLS). A comparison of block scaling methods was also undertaken for the Concatenated PLS algorithm, and new block scaling methods were proposed that led to better prediction performances. This study explores and reveals the potential advantages of applying multiblock methods for fusing datasets from different sources, from both the predictive and interpretability perspectives.