Kinetic study of the pyrolysis of miscanthus and its acid hydrolysis residue by thermogravimetric analysis

The kinetic parameters of the pyrolysis of miscanthus and its acid hydrolysis residue (AHR) were determined using thermogravimetric analysis (TGA). The AHR was produced at the University of Limerick by treating miscanthus with 5 wt.% sulphuric acid at 175 °C as representative of a lignocellulosic acid hydrolysis product. For the TGA experiments, 3 to 6 g of sample, milled and sieved to a particle size below 250 μm, were placed in the TGA ceramic crucible. The experiments were carried out under non-isothermal conditions heating the samples from 50 to 900 °C at heating rates of 2.5, 5, 10, 17 and 25 °C/min. The activation energy (EA) of the decomposition process was determined from the TGA data by differential analysis (Friedman) and three isoconversional methods of integral analysis (Kissinger–Akahira–Sunose, Ozawa–Flynn–Wall, Vyazovkin). The activation energy ranged from 129 to 156 kJ/mol for miscanthus and from 200 to 376 kJ/mol for AHR increasing with increasing conversion. The reaction model was selected using the non-linear least squares method and the pre-exponential factor was calculated from the Arrhenius approximation. The results showed that the best fitting reaction model was the third order reaction for both feedstocks. The pre-exponential factor was in the range of 5.6 × 1010 to 3.9 × 10+ 13 min− 1 for miscanthus and 2.1 × 1016 to 7.7 × 1025 min− 1 for AHR.

Training a Linear Neural Network with a Stable LSP Solution for Jamming Cancellation

Two jamming cancellation algorithms are developed based on a stable solution of least squares problem (LSP) provided by regularization. They are based on filtered singular value decomposition (SVD) and modifications of the Greville formula. Both algorithms allow an efficient hardware implementation. Testing results on artificial data modeling difficult real-world situations are also provided.

Bootstrap for Critical Branching Process with Non-Stationary Immigration

2000 Mathematics Subject Classification: Primary 60J80, Secondary 62F12, 60G99.

Monte Carlo Method for Reconstruction of Densities

2000 Mathematics Subject Classification: 65C05

Biological and Physical Factors Affecting the Natural History and Evolution of Encapsulated Development

The evolution of reproductive strategies involves a complex calculus of costs and benefits to both parents and offspring. Many marine animals produce embryos packaged in tough egg capsules or gelatinous egg masses attached to benthic surfaces. While these egg structures can protect against environmental stresses, the packaging is energetically costly for parents to produce. In this series of studies, I examined a variety of ecological factors affecting the evolution of benthic development as a life history strategy. I used marine gastropods as my model system because they are incredibly diverse and abundant worldwide, and they exhibit a variety of reproductive and developmental strategies.

The first study examines predation on benthic egg masses. I investigated: 1) behavioral mechanisms of predation when embryos are targeted (rather than the whole egg mass); 2) the specific role of gelatinous matrix in predation. I hypothesized that gelatinous matrix does not facilitate predation. One study system was the sea slug Olea hansineensis, an obligate egg mass predator, feeding on the sea slug Haminoea vesicula. Olea fed intensely and efficiently on individual Haminoea embryos inside egg masses but showed no response to live embryos removed from gel, suggesting that gelatinous matrix enables predation. This may be due to mechanical support of the feeding predator by the matrix. However, Haminoea egg masses outnumber Olea by two orders of magnitude in the field, and each egg mass can contain many tens of thousands of embryos, so predation pressure on individuals is likely not strong. The second system involved the snail Nassarius vibex, a non-obligate egg mass predator, feeding on the polychaete worm Clymenella mucosa. Gel neither inhibits nor promotes embryo predation for Nassarius, but because it cannot target individual embryos inside an egg mass, its feeding is slow and inefficient, and feeding rates in the field are quite low. However, snails that compete with Nassarius for scavenged food have not been seen to eat egg masses in the field, leaving Nassarius free to exploit the resource. Overall, egg mass predation in these two systems likely benefits the predators much more than it negatively affects the prey. Thus, selection for environmentally protective aspects of egg mass production may be much stronger than selection for defense against predation.

In the second study, I examined desiccation resistance in intertidal egg masses made by Haminoea vesicula, which preferentially attaches its flat, ribbon-shaped egg masses to submerged substrata. Egg masses occasionally detach and become stranded on exposed sand at low tide. Unlike adults, the encased embryos cannot avoid desiccation by selectively moving about the habitat, and the egg mass shape has high surface-area-to-volume ratio that should make it prone to drying out. Thus, I hypothesized that the embryos would not survive stranding. I tested this by deploying individual egg masses of two age classes on exposed sand bars for the duration of low tide. After rehydration, embryos midway through development showed higher rates of survival than newly-laid embryos, though for both stages survival rates over 25% were frequently observed. Laboratory desiccation trials showed that >75% survival is possible in an egg mass that has lost 65% of its water weight, and some survival (<25%) was observed even after 83% water weight lost. Although many surviving embryos in both experiments showed damage, these data demonstrate that egg mass stranding is not necessarily fatal to embryos. They may be able to survive a far greater range of conditions than they normally encounter, compensating for their lack of ability to move. Also, desiccation tolerance of embryos may reduce pressure on parents to find optimal laying substrata.

The third study takes a big-picture approach to investigating the evolution of different developmental strategies in cone snails, the largest genus of marine invertebrates. Cone snail species hatch out of their capsules as either swimming larvae or non-dispersing forms, and their developmental mode has direct consequences for biogeographic patterns. Variability in life history strategies among taxa may be influenced by biological, environmental, or phylogenetic factors, or a combination of these. While most prior research has examined these factors singularly, my aim was to investigate the effects of a host of intrinsic, extrinsic, and historical factors on two fundamental aspects of life history: egg size and egg number. I used phylogenetic generalized least-squares regression models to examine relationships between these two egg traits and a variety of hypothesized intrinsic and extrinsic variables. Adult shell morphology and spatial variability in productivity and salinity across a species geographic range had the strongest effects on egg diameter and number of eggs per capsule. Phylogeny had no significant influence. Developmental mode in Conus appears to be influenced mostly by species-level adaptations and niche specificity rather than phylogenetic conservatism. Patterns of egg size and egg number appear to reflect energetic tradeoffs with body size and specific morphologies as well as adaptations to variable environments. Overall, this series of studies highlights the importance of organism-scale biotic and abiotic interactions in evolutionary patterns.

Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments

In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

Modeling height-diameter relationship in pinus pinaster ait. in the forest intervention zone of Lomba, NE-Portugal

In this work, the relationship between diameter at breast height (d) and total height (h) of individual-tree was modeled with the aim to establish provisory height-diameter (h-d) equations for maritime pine (Pinus pinaster Ait.) stands in the Lomba ZIF, Northeast Portugal. Using data collected locally, several local and generalized h-d equations from the literature were tested and adaptations were also considered. Model fitting was conducted by using usual nonlinear least squares (nls) methods. The best local and generalized models selected, were also tested as mixed models applying a first-order conditional expectation (FOCE) approximation procedure and maximum likelihood methods to estimate fixed and random effects. For the calibration of the mixed models and in order to be consistent with the fitting procedure, the FOCE method was also used to test different sampling designs. The results showed that the local h-d equations with two parameters performed better than the analogous models with three parameters. However a unique set of parameter values for the local model can not be used to all maritime pine stands in Lomba ZIF and thus, a generalized model including covariates from the stand, in addition to d, was necessary to obtain an adequate predictive performance. No evident superiority of the generalized mixed model in comparison to the generalized model with nonlinear least squares parameters estimates was observed. On the other hand, in the case of the local model, the predictive performance greatly improved when random effects were included. The results showed that the mixed model based in the local h-d equation selected is a viable alternative for estimating h if variables from the stand are not available. Moreover, it was observed that it is possible to obtain an adequate calibrated response using only 2 to 5 additional h-d measurements in quantile (or random) trees from the distribution of d in the plot (stand). Balancing sampling effort, accuracy and straightforwardness in practical applications, the generalized model from nls fit is recommended. Examples of applications of the selected generalized equation to the forest management are presented, namely how to use it to complete missing information from forest inventory and also showing how such an equation can be incorporated in a stand-level decision support system that aims to optimize the forest management for the maximization of wood volume production in Lomba ZIF maritime pine stands.

Méthodes de Bootstrap pour les modèles à facteurs

Cette thèse développe des méthodes bootstrap pour les modèles à facteurs qui sont couram- ment utilisés pour générer des prévisions depuis l'article pionnier de Stock et Watson (2002) sur les indices de diffusion. Ces modèles tolèrent l'inclusion d'un grand nombre de variables macroéconomiques et financières comme prédicteurs, une caractéristique utile pour inclure di- verses informations disponibles aux agents économiques. Ma thèse propose donc des outils éco- nométriques qui améliorent l'inférence dans les modèles à facteurs utilisant des facteurs latents extraits d'un large panel de prédicteurs observés. Il est subdivisé en trois chapitres complémen- taires dont les deux premiers en collaboration avec Sílvia Gonçalves et Benoit Perron. Dans le premier article, nous étudions comment les méthodes bootstrap peuvent être utilisées pour faire de l'inférence dans les modèles de prévision pour un horizon de h périodes dans le futur. Pour ce faire, il examine l'inférence bootstrap dans un contexte de régression augmentée de facteurs où les erreurs pourraient être autocorrélées. Il généralise les résultats de Gonçalves et Perron (2014) et propose puis justifie deux approches basées sur les résidus : le block wild bootstrap et le dependent wild bootstrap. Nos simulations montrent une amélioration des taux de couverture des intervalles de confiance des coefficients estimés en utilisant ces approches comparativement à la théorie asymptotique et au wild bootstrap en présence de corrélation sérielle dans les erreurs de régression. Le deuxième chapitre propose des méthodes bootstrap pour la construction des intervalles de prévision permettant de relâcher l'hypothèse de normalité des innovations. Nous y propo- sons des intervalles de prédiction bootstrap pour une observation h périodes dans le futur et sa moyenne conditionnelle. Nous supposons que ces prévisions sont faites en utilisant un ensemble de facteurs extraits d'un large panel de variables. Parce que nous traitons ces facteurs comme latents, nos prévisions dépendent à la fois des facteurs estimés et les coefficients de régres- sion estimés. Sous des conditions de régularité, Bai et Ng (2006) ont proposé la construction d'intervalles asymptotiques sous l'hypothèse de Gaussianité des innovations. Le bootstrap nous permet de relâcher cette hypothèse et de construire des intervalles de prédiction valides sous des hypothèses plus générales. En outre, même en supposant la Gaussianité, le bootstrap conduit à des intervalles plus précis dans les cas où la dimension transversale est relativement faible car il prend en considération le biais de l'estimateur des moindres carrés ordinaires comme le montre une étude récente de Gonçalves et Perron (2014). Dans le troisième chapitre, nous suggérons des procédures de sélection convergentes pour les regressions augmentées de facteurs en échantillons finis. Nous démontrons premièrement que la méthode de validation croisée usuelle est non-convergente mais que sa généralisation, la validation croisée «leave-d-out» sélectionne le plus petit ensemble de facteurs estimés pour l'espace généré par les vraies facteurs. Le deuxième critère dont nous montrons également la validité généralise l'approximation bootstrap de Shao (1996) pour les regressions augmentées de facteurs. Les simulations montrent une amélioration de la probabilité de sélectionner par- cimonieusement les facteurs estimés comparativement aux méthodes de sélection disponibles. L'application empirique revisite la relation entre les facteurs macroéconomiques et financiers, et l'excès de rendement sur le marché boursier américain. Parmi les facteurs estimés à partir d'un large panel de données macroéconomiques et financières des États Unis, les facteurs fortement correlés aux écarts de taux d'intérêt et les facteurs de Fama-French ont un bon pouvoir prédictif pour les excès de rendement.

A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries

Understanding the fluctuations in population abundance is a central question in fisheries. Sardine fisheries is of great importance to Portugal and is data-rich and of primary concern to fisheries managers. In Portugal, sub-stocks of Sardina pilchardus (sardine) are found in different regions: the Northwest (IXaCN), Southwest (IXaCS) and the South coast (IXaS-Algarve). Each of these sardine sub-stocks is affected differently by a unique set of climate and ocean conditions, mainly during larval development and recruitment, which will consequently affect sardine fisheries in the short term. Taking this hypothesis into consideration we examined the effects of hydrographic (river discharge), sea surface temperature, wind driven phenomena, upwelling, climatic (North Atlantic Oscillation) and fisheries variables (fishing effort) on S. pilchardus catch rates (landings per unit effort, LPUE, as a proxy for sardine biomass). A 20-year time series (1989-2009) was used, for the different subdivisions of the Portuguese coast (sardine sub-stocks). For the purpose of this analysis a multi-model approach was used, applying different time series models for data fitting (Dynamic Factor Analysis, Generalised Least Squares), forecasting (Autoregressive Integrated Moving Average), as well as Surplus Production stock assessment models. The different models were evaluated, compared and the most important variables explaining changes in LPUE were identified. The type of relationship between catch rates of sardine and environmental variables varied across regional scales due to region-specific recruitment responses. Seasonality plays an important role in sardine variability within the three study regions. In IXaCN autumn (season with minimum spawning activity, larvae and egg concentrations) SST, northerly wind and wind magnitude were negatively related with LPUE. In IXaCS none of the explanatory variables tested was clearly related with LPUE. In IXaS-Algarve (South Portugal) both spring (period when large abundances of larvae are found) northerly wind and wind magnitude were negatively related with LPUE, revealing that environmental effects match with the regional peak in spawning time. Overall, results suggest that management of small, short-lived pelagic species, such as sardine quotas/sustainable yields, should be adapted to a regional scale because of regional environmental variability.

The Impact of Internal Market Orientation on Staff Service Behaviours

The impact of service direction, service training and staff behaviours on perceptions of service delivery are examined. The impact of managerial behaviour in the form of internal market orientation (IMO) on the attitudes of frontline staff towards the firm and its consequent influence on their customer oriented behaviours is also examined. Frontline service staff working in the consumer transport industry were surveyed to provide subjective data about the constructs of interest in this study, and the data were analysed using structural equations modelling employing partial least squares estimation. The data indicate significant relationships between internal market orientation (IMO), the attitudes of the employees to the firm and their consequent behaviour towards customers. Customer orientation, service direction and service training are all identified as antecedents to high levels of service delivery. The study contributes to marketing theory by providing quantitative evidence to support assumptions that internal marketing has an impact on services success. For marketing practitioners, the research findings offer additional information about the management, training and motivation of service staff towards service excellence.

Trust and strong ties in the selection and evaluation of business advisors by small firms

Principal Topic A small firm is unlikely to possess internally the full range of knowledge and skills that it requires or could benefit from for the development of its business. The ability to acquire suitable external expertise - defined as knowledge or competence that is rare in the firm and acquired from the outside - when needed thus becomes a competitive factor in itself. Access to external expertise enables the firm to focus on its core competencies and removes the necessity to internalize every skill and competence. However, research on how small firms access external expertise is still scarce. The present study contributes to this under-developed discussion by analysing the role of trust and strong ties in the small firm's selection and evaluation of sources of external expertise (henceforth referred to as the 'business advisor' or 'advisor'). Granovetter (1973, 1361) defines the strength of a network tie as 'a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding) and the reciprocal services which characterize the tie'. Strong ties in the context of the present investigation refer to sources of external expertise who are well known to the owner-manager, and who may be either informal (e.g., family, friends) or professional advisors (e.g., consultants, enterprise support officers, accountants or solicitors). Previous research has suggested that strong and weak ties have different fortes and the choice of business advisors could thus be critical to business performance) While previous research results suggest that small businesses favour previously well known business advisors, prior studies have also pointed out that an excessive reliance on a network of well known actors might hamper business development, as the range of expertise available through strong ties is limited. But are owner-managers of small businesses aware of this limitation and does it matter to them? Or does working with a well-known advisor compensate for it? Hence, our research model first examines the impact of the strength of tie on the business advisor's perceived performance. Next, we ask what encourages a small business owner-manager to seek advice from a strong tie. A recent exploratory study by Welter and Kautonen (2005) drew attention to the central role of trust in this context. However, while their study found support for the general proposition that trust plays an important role in the choice of advisors, how trust and its different dimensions actually affect this choice remained ambiguous. The present paper develops this discussion by considering the impact of the different dimensions of perceived trustworthiness, defined as benevolence, integrity and ability, on the strength of tie. Further, we suggest that the dimensions of perceived trustworthiness relevant in the choice of a strong tie vary between professional and informal advisors. Methodology/Key Propositions Our propositions are examined empirically based on survey data comprising 153 Finnish small businesses. The data are analysed utilizing the partial least squares (PLS) approach to structural equation modelling with SmartPLS 2.0. Being non-parametric, the PLS algorithm is particularly well-suited to analysing small datasets with non-normally distributed variables. Results and Implications The path model shows that the stronger the tie, the more positively the advisor's performance is perceived. Hypothesis 1, that strong ties will be associated with higher perceptions of performance is clearly supported. Benevolence is clearly the most significant predictor of the choice of a strong tie for external expertise. While ability also reaches a moderate level of statistical significance, integrity does not have a statistically significant impact on the choice of a strong tie. Hence, we found support for two out of three independent variables included in Hypothesis 2. Path coefficients differed between the professional and informal advisor subsamples. The results of the exploratory group comparison show that Hypothesis 3a regarding ability being associated with strong ties more pronouncedly when choosing a professional advisor was not supported. Hypothesis 3b arguing that benevolence is more strongly associated with strong ties in the context of choosing an informal advisor received some support because the path coefficient in the informal advisor subsample was much larger than in the professional advisor subsample. Hypothesis 3c postulating that integrity would be more strongly associated with strong ties in the choice of a professional advisor was supported. Integrity is the most important dimension of trustworthiness in this context. However, integrity is of no concern, or even negative, when using strong ties to choose an informal advisor. The findings of this study have practical relevance to the enterprise support community. First of all, given that the strength of tie has a significant positive impact on the advisor's perceived performance, this implies that small business owners appreciate working with advisors in long-term relationships. Therefore, advisors are well advised to invest into relationship building and maintenance in their work with small firms. Secondly, the results show that, especially in the context of professional advisors, the advisor's perceived integrity and benevolence weigh more than ability. This again emphasizes the need to invest time and effort into building a personal relationship with the owner-manager, rather than merely maintaining a professional image and credentials. Finally, this study demonstrates that the dimensions of perceived trustworthiness are orthogonal with different effects on the strength of tie and ultimately perceived performance. This means that entrepreneurs and advisors should consider the specific dimensions of ability, benevolence and integrity, rather than rely on general perceptions of trustworthiness in their advice relationships.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of not, vert, similar±10% is acceptable.

Small molecule–biopolymer interactions : Ultraviolet–visible and fluorescence spectroscopy and chemometrics

Interactions between small molecules with biopolymers e.g. the bovine serum albumin (BSA protein), are important, and significant information is recorded in the UV–vis and fluorescence spectra of their reaction mixtures. The extraction of this information is difficult conventionally and principally because there is significant overlapping of the spectra of the three analytes in the mixture. The interaction of berberine chloride (BC) and the BSA protein provides an interesting example of such complex systems. UV–vis and fluorescence spectra of BC and BSA mixtures were investigated in pH 7.4 Tris–HCl buffer at 37 °C. Two sample series were measured by each technique: (1) [BSA] was kept constant and the [BC] was varied and (2) [BC] was kept constant and the [BSA] was varied. This produced four spectral data matrices, which were combined into one expanded spectral matrix. This was processed by the multivariate curve resolution–alternating least squares method (MCR–ALS). The results produced: (1) the extracted pure BC, BSA and the BC–BSA complex spectra from the measured heavily overlapping composite responses, (2) the concentration profiles of BC, BSA and the BC–BSA complex, which are difficult to obtain by conventional means, and (3) estimates of the number of binding sites of BC.