Experiences in Pattern Recognition for Machine Olfaction

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings

Deconstruction of a Metastatic Tumor Microenvironment Reveals a Common Matrix Response in Human Cancers.

We have profiled, for the first time, an evolving human metastatic microenvironment by measuring gene expression, matrisome proteomics, cytokine and chemokine levels, cellularity, extracellular matrix organization, and biomechanical properties, all on the same sample. Using biopsies of high-grade serous ovarian cancer metastases that ranged from minimal to extensive disease, we show how nonmalignant cell densities and cytokine networks evolve with disease progression. Multivariate integration of the different components allowed us to define, for the first time, gene and protein profiles that predict extent of disease and tissue stiffness, while also revealing the complexity and dynamic nature of matrisome remodeling during development of metastases. Although we studied a single metastatic site from one human malignancy, a pattern of expression of 22 matrisome genes distinguished patients with a shorter overall survival in ovarian and 12 other primary solid cancers, suggesting that there may be a common matrix response to human cancer.Significance: Conducting multilevel analysis with data integration on biopsies with a range of disease involvement identifies important features of the evolving tumor microenvironment. The data suggest that despite the large spectrum of genomic alterations, some human malignancies may have a common and potentially targetable matrix response that influences the course of disease. Cancer Discov; 8(3); 304-19. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 253

Application of multiple response optimization design to quantum dot-encoded microsphere bioconjugates hybridization assay

The optimization of DNA hybridization for genotyping assays is a complex experimental problem that depends on multiple factors such as assay formats, fluorescent probes, target sequence, experimental conditions, and data analysis. Quantum dot-doped particle bioconjugates have been previously described as fluorescent probes to identify single nucleotide polymorphisms even though this advanced fluorescent material has shown structural instability in aqueous environments. To achieve the optimization of DNA hybridization to quantum dot-doped particle bioconjugates in suspension while maximizing the stability of the probe materials, a nonsequential optimization approach was evaluated. The design of experiment with response surface methodology and multiple optimization response was used to maximize the recovery of fluorescent probe at the end of the assay simultaneously with the optimization of target-probe binding. Hybridization efficiency was evaluated by the attachment of fluorescent oligonucleotides to the fluorescent probe through continuous flow cytometry detection. Optimal conditions were predicted with the model and tested for the identification of single nucleotide polymorphisms. The design of experiment has been shown to significantly improve biochemistry and biotechnology optimization processes. Here we demonstrate the potential of this statistical approach to facilitate the optimization of experimental protocol that involves material science and molecular biology

Monitoring haemodialysis using electronic nose and chemometrics

An ever-increasing number of patients have to undergo regular renal dialysis to compensate for acute or chronic renal failure. The adequacy of the treatment has a profound effect on patients’ morbidity and mortality. Therefore it is necessary to assess the delivered dialysis dose. For the quantification of the dialysis dose two parameters are most commonly used, namely the Kt/V value (normalised dose of dialysis) and the urea reduction rate, yet the prescribed dialysis dose often differs from the actual delivered dialysis dose. Currently, no interactive process is available to ensure optimal treatment. The aim of this study was to investigate the potential for an “electronic nose” as a novel monitoring tool for haemodialysis. Blood samples were analysed using an electronic nose, comprising an array of 14 conducting polymer sensors, and compared to traditional biochemistry. Principal component analysis and hierarchical cluster analysis were applied to evaluate the data, and demonstrated the ability to distinguish between pre-dialysis blood from post-dialysis blood independent of the method used. It is concluded that the electronic nose is capable of discriminating pre-dialysis from post-dialysis blood and hence, together with an appropriate classification model, suitable for on-line monitoring

MRMaid: the SRM assay design tool for Arabidopsis and other species

Selected reaction monitoring (SRM), sometimes called multiple reaction monitoring (MRM), is becoming the tool of choice for targeted quantitative proteomics in the plant science community. Key to a successful SRM experiment is prior identification of the distinct peptides for the proteins of interest and the determination of the so-called transitions that can be programmed into an LC-MS/MS to monitor those peptides. The transition for a given peptide comprises the intact peptide m/z and a high intensity product ion that can be monitored at a characteristic retention time (RT). To aid the design of SRM experiments, several online tools and databases have been produced to help researchers select transitions for their proteins of interest, but many of these tools are limited to the most popular model organisms such as human, yeast, and mouse or require the experimental acquisition of local spectral libraries. In this paper we present MRMaid1, a web-based SRM assay design tool whose transitions are generated by mining the millions of identified peptide spectra held in the EBI’s PRIDE database. By using data from this large public repository, MRMaid is able to cover a wide range of species that can increase as the coverage of PRIDE grows. In this paper MRMaid transitions for 25Arabidopsis thalianaproteins are evaluated experimentally, and found capable of quantifying 23 of these proteins. This performance was found to be comparable with the more time consuming approach of designing transitions using locally acquired orbitrap data, indicating that MRMaid is a valuable tool for targeted Arabidopsis proteomics

Monitoring haemodialysis using electronic nose and chemometrics

An ever-increasing number of patients have to undergo regular renal dialysis to compensate for acute or chronic renal failure. The adequacy of the treatment has a profound effect on patients’ morbidity and mortality. Therefore it is necessary to assess the delivered dialysis dose. For the quantification of the dialysis dose two parameters are most commonly used, namely the Kt/V value (normalised dose of dialysis) and the urea reduction rate, yet the prescribed dialysis dose often differs from the actual delivered dialysis dose. Currently, no interactive process is available to ensure optimal treatment. The aim of this study was to investigate the potential for an “electronic nose” as a novel monitoring tool for haemodialysis. Blood samples were analysed using an electronic nose, comprising an array of 14 conducting polymer sensors, and compared to traditional biochemistry. Principal component analysis and hierarchical cluster analysis were applied to evaluate the data, and demonstrated the ability to distinguish between pre-dialysis blood from post-dialysis blood independent of the method used. It is concluded that the electronic nose is capable of discriminating pre-dialysis from post-dialysis blood and hence, together with an appropriate classification model, suitable for on-line monitoring

FT-infrared spectroscopic studies of lymphoma, lymphoid and myeloid leukaemia cell lines

This paper presents a novel method to characterise spectral differences that distinguish leukaemia and lymphoma cell lines. This is based on objective spectral measurements of major cellular biochemical constituents and multivariate spectral processing. Fourier transform infrared (FT-IR) maps of the lymphoma, lymphoid and myeloid leukaemia cell samples were obtained using a Perkin-Elmer Spotlight 300 FT-IR imaging spectrometer. Multivariate statistical techniques incorporating principal component analysis (PCA) and linear discriminant analysis (LDA) were used to construct a mathematical model. This model was validated for reproducibility. Multivariate statistical analysis of FTIR spectra collected for each cell sample permit a combination of unsupervised and supervised methods of distinguishing cell line types. This resulted in the clustering of cell line populations, indicating distinct bio-molecular differences. Major spectral differences were observed in the 4000 to 800 cm- 1 spectral region. Bands in the averaged spectra for the cell line were assigned to the major biochemical constituents including; proteins, fatty acids, carbohydrates and nucleic acids. The combination of FT-IR spectroscopy and multivariate statistical analysis provides an important insight into the fundamental spectral differences between the cell lines, which differ according to the cellular biochemical composition. These spectral differences can serve as potential biomarkers for the differentiation of leukaemia and lymphoma cells. Consequently these differences could be used as the basis for developing a spectral method for the detection and identification of haematological malignancies

Evaluation of a gas sensor array and pattern recognition for the identification of bladder cancer from urine headspace.

Previous studies have indicated that volatile compounds specific to bladder cancer may exist in urine headspace, raising the possibility that headspace analysis could be used for diagnosis of this particular cancer. In this paper, we evaluate the use of a commercially available gas sensor array coupled with a specifically designed pattern recognition algorithm for this purpose. The best diagnostic performance that we were able to obtain with independent test data provided by healthy volunteers and bladder cancer patients was 70% overall accuracy (70% sensitivity and 70% specificity). When the data of patients suffering from other non-cancerous urological diseases were added to those of the healthy controls, the classification accuracy fell to 65% with 60% sensitivity and 67% specificity. While this is not sufficient for a diagnostic test, it is significantly better than random chance, leading us to conclude that there is useful information in the urine headspace but that a more informative analytical technique, such as mass spectrometry, is required if this is to be exploited fully

A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20°C) using the dataset presented by Argyri etal. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN

Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data

Over the past years, the application of electronic nose devices has been investigated as a potential tool for assessing food freshness. This relies on the application of various pattern recognition methods to provide accurate classification and regression models. The models' accuracy depends on the number of samples used during the training process. This often leads to unstable and unreliable classifiers in the case of food quality assessment, where the number of samples is typically less than 200 for a given experiment. The aim of this work is to tackle this problem through the development of a series of ensemble-based classifiers and regression models using support vector machines and electronic nose datasets based on the previously published work of this group. It was found that the developed ensemble provides a higher prediction accuracy compared to the single model approach when estimating the freshness score assigned by the sensory panel; achieving an overall accuracy of 84.1% compared to 72.7% in the case of the single classifier model. Another set of calibration ensembles were developed based on SVMregression, in order to predict bacterial species counts, achieving an increase in the average overall performance of 85.0%, compared to 76.5% when a single classifier was applied. This increase in the predictive power therefore suggests that combining an electronic nose with ensemble-based systems can be used as an innovative method to assess the freshness of beef fillets