Chemometric brains for artificial tongues

Silver nanoparticles from orange peel extract: Colorimetric detection of Pb2+ and Cd2+ ions with a chemometric approach

Green silver nanoparticles (AgNPs@OPE) were obtained by using orange (citrus sinensis) peel water extract (OPE) that acts as a reducing and capping agent. This procedure permits the valorisation of waste as orange peel, and lowers the environmental impact of the process, with respect to the conventional synthetic procedure. The OPE extract reduced Ag(I) to Ag(0) in alkaline conditions, and stabilised the produced nanoparticles as a capping agent. The AgNPs@OPE were deeply characterized by UV-Vis spectroscopy, FT-IR, SEM analysis and DLS analysis and successively used as colorimetric sensors for different metals in aqueous solution. The colourimetric assay showed that AgNPs@OPE were able to detect Pb2+ and Cd2+, as demonstrated by the splits of surface plasmon resonance (SPR) band accompanied by the formation of a second new band; these spectral modification resulted in a colour change, from pristine nanoparticles' yellow to brown, due to the aggregation process. For the quantification of each of the two target cations, a calibration was performed by using the univariate linear regression, within the linearity ranges, exploiting the absorbance ratio between the main SPR band and the new band relative to the aggregate formation. Then a multivariate approach was followed to perform both Cd2+ and Pb2+ quantification by means of Partial Least Square regression (PLS) and target cations distinction by Linear Discriminant Analysis (LDA) applied on Principal Components Analysis (PCA) outputs, in both cases using the entire UV-Vis spectra (350-800 nm) as input data. Finally, the ability to quantify and distinguish between Cd2+ and Pb2+ was tested in tap water samples spiked with the two cations in order to confirm the application of the AgNPs@OPE as selective sensor in real samples.

Exploiting Principal Component Analysis (PCA) to reveal temperature, buffer and metal ions' role in neuromelanin (NM) synthesis by dopamine (DA) oxidative polymerization

Neuromelanin (NM) plays a well-established role in neurological disorders pathogenesis; the mechanism of action is still discussed and the investigations in this field are limited by NM's complex and heterogeneous composition, insolubility, and low availability from human brains. An alternative can be offered by synthetic NM obtained from dopamine (DA) oxidative polymerization; however, a deep knowledge of the influence of both physicochemical parameters (T, pH, ionic strength) and other compounds in the reaction media (buffer, metal ions, other catecholamines) on DA oxidation process and, consequently, on synthetic NM features is mandatory to develop reliable NM preparation methodologies. To partially fulfill this aim, the present work focuses on defining the role of temperature, buffer and metal ions on both DA oxidation rate and DA oligomer size. DA oxidation in the specific conditions is monitored by UV-Vis spectroscopy and Principal Component Analysis (PCA) is run either on the raw spectra to model the background absorption increase, related to small DA oligomers formation, or on their first derivative to rationalize DA consumption. After having studied three case studies, 3-Way PCA is applied to directly evaluate the effect of temperature and buffer type on DA oxidation in the presence of different metal ions. Despite the proof-of-concept nature of the work and the number of compounds still to be included in the investigation, the preliminary results and the possibility to further expand the chemometric approach represent an interesting contribution to the field of in vitro simulation of NM synthesis.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Critical Evaluation of Laboratory Potentiometric Electronic Tongues for Pharmaceutical Analysis-An Overview

Electronic tongue systems equipped with cross-sensitive potentiometric sensors have been applied to pharmaceutical analysis, due to the possibility of various applications and developing new formulations. Many studies already proved the complementarity between the electronic tongue and classical analysis such as dissolution tests indicated by Pharmacopeias. However, as a new approach to study pharmaceuticals, electronic tongues lack strict testing protocols and specification limits; therefore, their results can be improperly interpreted and inconsistent with the reference studies. Therefore, all aspects of the development, measurement conditions, data analysis, and interpretation of electronic tongue results were discussed in this overview. The critical evaluation of the effectiveness and reliability of constructed devices may be helpful for a better understanding of electronic tongue systems development and for providing strict testing protocols.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Response Standardization for Drift Correction and Multivariate Calibration Transfer in "Electronic Tongue" Studies

The procedures for response standardization in "electronic tongue" (ET) studies are described. The construction of reliable multivariate calibration for "electronic tongue" requires the analysis of a large number of representative samples both with ET and reference techniques. This is a laborious and expensive process. Long-term sensor array operation leads to the changes in sensor response characteristics and thus invalidates the multivariate predictive models. Moreover, due to the individual parameters of each sensor in different sensor arrays, it is not possible to use the calibration model for one system together with the data acquired by another system, even if they have the same sensors. Both of these issues lead to the necessity of frequent sensor array calibration which would be ideal to avoid. Instead of recalibration, these two problems can be handled using mathematical methods intended for sensor response standardization. This chapter describes two popular methods of standardization which can be used for both drift correction and calibration transfer. Thus, significant efforts on measuring representative sample sets for sensor array recalibration can be avoided.

Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films

A nanostructured electrochemical bi-sensor system for the analysis of milks has been developed using the layer-by-layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]₂, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity, and chitosan (CHI), that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]₂-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV⁻vis, and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal component analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using a PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components, such as fats, proteins, and acidity, can also be obtained. The method developed is simple, and the short response time permits its use in assaying milk samples online.

Molecular processes in an electrochemical clozapine sensor

Selectivity presents a crucial challenge in direct electrochemical sensing. One example is schizophrenia treatment monitoring of the redox-active antipsychotic clozapine. To accurately assess efficacy, differentiation from its metabolite norclozapine-similar in structure and redox potential-is critical. Here, the authors leverage biomaterials integration to study, and effect changes in, diffusion and electron transfer kinetics of these compounds. Specifically, the authors employ a catechol-modified chitosan film, which the authors have previously presented as the first electrochemical detection mechanism capable of quantifying clozapine directly in clinical serum. A key finding in our present work is differing dynamics between clozapine and norclozapine once the authors interface the electrodes with chitosan-based biomaterial films. These additional dimensions of redox information can thus enable selective sensing of largely analogous small molecules.

Electronic Noses and Tongues in Wine Industry

The quality of wines is usually evaluated by a sensory panel formed of trained experts or traditional chemical analysis. Over the last few decades, electronic noses (e-noses) and electronic tongues have been developed to determine the quality of foods and beverages. They consist of arrays of sensors with cross-sensitivity, combined with pattern recognition software, which provide a fingerprint of the samples that can be used to discriminate or classify the samples. This holistic approach is inspired by the method used in mammals to recognize food through their senses. They have been widely applied to the analysis of wines, including quality control, aging control, or the detection of fraudulence, among others. In this paper, the current status of research and development in the field of e-noses and tongues applied to the analysis of wines is reviewed. Their potential applications in the wine industry are described. The review ends with a final comment about expected future developments.

Has your ancient stamp been regummed with synthetic glue? A FT-NIR and FT-Raman study

The potential of FT-NIR and FT-Raman spectroscopies to characterise the gum applied on the backside of ancient stamps was investigated for the first time. This represents a very critical issue for the collectors' market, since gum conditions heavily influence stamp quotations, and fraudulent application of synthetic gum onto damaged stamp backsides to increase their desirability is a well-documented practice. Spectral data were processed by exploratory pattern recognition tools. In particular, application of principal component analysis (PCA) revealed that both of the spectroscopic techniques provide information useful to characterise stamp gum. Examination of PCA loadings and their chemical interpretation confirmed the robustness of the outcomes. Fusion of FT-NIR and FT-Raman spectral data was performed, following both a low-level and a mid-level procedure. The results were critically compared with those obtained separately for the two spectroscopic techniques.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately.

Set-up of a multivariate approach based on serum biomarkers as an alternative strategy for the screening evaluation of the potential abuse of growth promoters in veal calves

A chemometric class modelling strategy (unequal dispersed classes - UNEQ) was applied for the first time as a possible screening method to monitor the abuse of growth promoters in veal calves. Five serum biomarkers, known to reflect the exposure to classes of compounds illegally used as growth promoters, were determined from 50 untreated animals in order to design a model of controls, representing veal calves reared under good, safe and highly standardised breeding conditions. The class modelling was applied to 421 commercially bred veal calves to separate them into 'compliant' and 'non-compliant' with respect to the modelled controls. Part of the non-compliant animals underwent further histological and chemical examinations to confirm the presence of either alterations in target tissues or traces of illegal substances commonly administered for growth-promoting purposes. Overall, the congruence between the histological or chemical methods and the UNEQ non-compliant outcomes was approximately 58%, likely underestimated due to the blindness nature of this examination. Further research is needed to confirm the validity of the UNEQ model in terms of sensitivity in recognising untreated animals as compliant to the controls, and specificity in revealing deviations from ideal breeding conditions, for example due to the abuse of growth promoters.

Differentiation of prostate cancer from normal tissue in radical prostatectomy specimens by desorption electrospray ionization and touch spray ionization mass spectrometry

Radical prostatectomy is a common treatment option for prostate cancer before it has spread beyond the prostate. Examination for surgical margins is performed post-operatively with positive margins reported to occur in 6.5-32% of cases. Rapid identification of cancerous tissue during surgery could improve surgical resection. Desorption electrospray ionization (DESI) is an ambient ionization method which produces mass spectra dominated by lipid signals directly from prostate tissue. With the use of multivariate statistics, these mass spectra can be used to differentiate cancerous and normal tissue. The method was applied to 100 samples from 12 human patients to create a training set of MS data. The quality of the discrimination achieved was evaluated using principal component analysis - linear discriminant analysis (PCA-LDA) and confirmed by histopathology. Cross validation (PCA-LDA) showed >95% accuracy. An even faster and more convenient method, touch spray (TS) mass spectrometry, not previously tested to differentiate diseased tissue, was also evaluated by building a similar MS data base characteristic of tumor and normal tissue. An independent set of 70 non-targeted biopsies from six patients was then used to record lipid profile data resulting in 110 data points for an evaluation dataset for TS-MS. This method gave prediction success rates measured against histopathology of 93%. These results suggest that DESI and TS could be useful in differentiating tumor and normal prostate tissue at surgical margins and that these methods should be evaluated intra-operatively.

Ambient ionisation mass spectrometry for lipid profiling and structural analysis of mammalian oocytes, preimplantation embryos and stem cells

Lipids play fundamental roles in mammalian embryo preimplantation development and cell fate. Triacylglycerol accumulates in oocytes and blastomeres as lipid droplets, phospholipids influence membrane functional properties, and essential fatty acid metabolism is important for maintaining the stemness of cells cultured in vitro. The growing impact that lipids have in the field of developmental biology makes analytical approaches to analyse structural information of great interest. This paper describes the concept and presents the results of lipid profiling by mass spectrometry (MS) of oocytes and preimplantation embryos, with special focus on ambient ionisation. Based on our previous experience with oocytes and embryos, we aim to convey that ambient MS is also valuable for stem cell differentiation analysis. Ambient ionisation MS allows the detection of a wide range of lipid classes (e.g. free fatty acids, cholesterol esters, phospholipids) in single oocytes, embryos and cell pellets, which are informative of in vitro culture impact, developmental and differentiation stages. Background on MS principles, the importance of underused MS scan modes for structural analysis of lipids, and statistical approaches used for data analysis are covered. We envisage that MS alone or in combination with other techniques will have a profound impact on the understanding of lipid metabolism, particularly in early embryo development and cell differentiation research.

Lipid characterization of individual porcine oocytes by dual mode DESI-MS and data fusion

The development of sensitive measurements to analyze individual cells is of relevance to elucidate specialized roles or metabolic functions of each cell under physiological and pathological conditions. Lipids play multiple and critical roles in cellular functions and the application of analytical methods in the lipidomics area is of increasing interest. In this work, in vitro maturation of porcine oocytes was studied. Two independent sources of chemical information (represented by mass spectra in the positive and negative ion modes) from single oocytes (immature oocytes, 24-h and 44-h in vitro matured oocytes) were acquired by using desorption electrospray ionization-mass spectrometry (DESI-MS). Low and mid-level data fusion strategies are presented with the aim of better exploring the large amount of chemical information contained in the two mass spectrometric lipid profiles. Data were explored by principal component analysis (PCA) within the two multi-block approaches to include information on free fatty acids, phospholipids, cholesterol-related molecules, di- and triacylglycerols. After data fusion, clearer differences among immature and in vitro matured porcine oocytes were observed, which provide novel information regarding lipid metabolism throughout oocyte maturation. In particular, changes in TAG composition, as well as increase in fatty acid metabolism and membrane complexity were evidenced during the in vitro maturation process. This information can assist the improvement of in vitro embryo production for porcine species.

Rapid discrimination of bacteria by paper spray mass spectrometry

Paper spray mass spectrometry ambient ionization is utilized for rapid discrimination of bacteria without sample preparation. Bacterial colonies were smeared onto filter paper precut to a sharp point, then wetted with solvent and held at a high potential. Charged droplets released by field emission were sucked into the mass spectrometer inlet and mass spectra were recorded. Sixteen different species representing eight different genera from Gram-positive and Gram-negative bacteria were investigated. Phospholipids were the predominant species observed in the mass spectra in both the negative and positive ion modes. Multivariate data analysis based on principal component analysis, followed by linear discriminant analysis, allowed bacterial discrimination. The lipid information in the negative ion mass spectra proved useful for species level differentiation of the investigated Gram-positive bacteria. Gram-negative bacteria were differentiated at the species level by using a numerical data fusion strategy of positive and negative ion mass spectra.

Evaluation of oxygen exposure levels and polyphenolic content of red wines using an electronic panel formed by an electronic nose and an electronic tongue

An electronic panel formed by an electronic nose and an electronic tongue has been used to analyse red wines showing high and low phenolic contents, obtained by flash release and traditional soaking, respectively, and processed with or without micro-oxygenation. Four oxygen transfer rate conditions (0.8, 1.9, 8.0, and 11.9 μl oxygen/bottle/day) were ensured by using synthetic closures with controlled oxygen permeability and storage under controlled atmosphere. Twenty-five chemical parameters associated with the polyphenolic composition, the colour indices and the levels of oxygen were measured in triplicate and correlated with the signals registered (seven replicas) by means of the electronic nose and the electronic tongue using partial least squares regression analysis. The electronic nose and the electronic tongue showed particularly good correlations with those parameters associated with the oxygen levels and, in particular, with the influence of the porosity of the closure to oxygen exposure. In turn, the electronic tongue was particularly sensitive to redox species including oxygen and phenolic compounds. It has been demonstrated that a combined system formed from the electronic nose and the electronic tongue provides information about the chemical composition of both the gas and the liquid phase of red wines. This complementary information improves the capacity to predict values of oxygen-related parameters, phenolic content and colour parameters.

A combination of dynamic measurement protocol and advanced data treatment to resolve the mixtures of chemically similar analytes with potentiometric multisensor system

Data processing techniques and measuring protocol are very important parts of the multisensor systems methodology. Complex analytical tasks like resolving the mixtures of two components with very similar chemical properties require special attention. We report on the application of non-linear (artificial neural networks, ANNs) and linear (projections on latent structures, PLS) regression techniques to the data obtained from the flow cell with potentiometric multisensor detection of neighouring lanthanides in the Periodic System of the elements (samarium, europium and gadolinium). Quantification of individual components in mixtures is possible with reasonable precision if dynamic components of the response are incorporated thanks to the use of an automated sequential injection analysis system. The average absolute error in prediction of lanthanides with PLS was around 1 × 10(-4)mol/L, while the use of ANNs allows the lowering of prediction errors down to 2 × 10(-5)mol/L in certain cases. The suggested protocol seems to be useful for other analytical applications where simultaneous determination of chemically similar analytes in mixtures is required.

Classification of lectins by pattern recognition using glyconanoparticles

Carbohydrate-functionalized gold nanoparticles were employed to differentiate plant-legume lectins using a statistical analysis method of linear discriminant analysis (LDA). Various carbohydrates were conjugated on gold nanoparticles, and the resulting glyconanoparticles were treated with lectins. Changes in the localized surface plasmon resonance of the glyconanoparticles upon lectin binding were recorded, and the data were subjected to LDA. Results showed that the glyconanoparticles successfully differentiated all lectins.