Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics

Shaw, Adrian D.; Winson, Michael K.; Woodward, Andrew M.; McGovern, Aoife C.; Davey, Hazel M.; Kaderbhai, Naheed; Broadhurst, David; Taylor, Janet A.; Timmins, Éadaoin M.; Goodacre, Royston; Kell, Douglas B.; Alsberg, Bjørn K.; Rowland, Jem J.

doi:10.1007/3-540-48773-5_3

Cited by 32 publications

(23 citation statements)

References 119 publications

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“…It is evident that by reducing the reaction volume to nanoliters or even lower we could discriminate SNPs with attomoles or less of nucleotide reagent. The reaction time was held at 10 s, but we made no attempt to use spectral denoising techniques, such as those based on wavelets [90][91][92], which could have reduced the measurement time to less than 1 s while preserving the same signal:noise [93]. Especially since the method has been shown to work for SNPs in genes of medical interest, we are conWdent that the methods of single-molecule detection described in this paper hold out the hope of very rapid and inexpensive SNP analysis.…”

Section: Discussionmentioning

confidence: 99%

Single-nucleotide polymorphism detection using nanomolar nucleotides and single-molecule fluorescence

Twist

Winson

Rowland

et al. 2004

Analytical Biochemistry

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Section: Discussionmentioning

confidence: 99%

Single-nucleotide polymorphism detection using nanomolar nucleotides and single-molecule fluorescence

Twist

Winson

Rowland

et al. 2004

Analytical Biochemistry

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“…It is important to apply advanced data preprocessing and statistical analysis to interpret the raw spectra. Analysis of a "whole organism fingerprint" with vibrational spectroscopic methods yields a high dimension vector containing multiple dependent variables (or wavenumbers) (Shaw et al 1999). Chemometrics describes the multivariate statistical analysis that can be used to reduce this multidimensional information to a few, independent latent variables that preserve the most relevant and representative information (designated as the first several principal components).…”

Section: A Short Introduction To Chemometricsmentioning

confidence: 99%

“…Both infrared and Raman spectroscopies have been extensively applied in various research areas (Thygesen et al 2003), such as for detection of food toxicants and chemical adulteration (Cheung et al 2010;Lin et al 2008;), bioprocessing and fermentation monitoring (Goodacre and Jarvis 2005;Brewster et al 2009;Clarke et al 2005;Shaw et al 1999), enzyme activity (Hollywood et al 2010), microorganism identification and segregation (Burgula et al 2007;Mariey et al 2001;Preisner et al 2007;Maquelin et al 2002;Naumann 2001;Harz et al 2009;Lu and Rasco 2010a;Huang et al 2010;Davis et al 2010), microbial cell injury identification (Lin et al 2004;Al-Qadiri et al 2008a), virus identification (Fan et al 2010), and prion structure elucidation (Beekes et al 2007). Fourier transforminfrared (FT-IR) spectroscopy has also been used to monitor microbial spoilage in food systems, such as chicken breast (Ellis et al 2002), beef (Ellis et al 2004), and milk (Nicolaou and Goodacre 2008).…”

mentioning

confidence: 98%

Application of Mid-infrared and Raman Spectroscopy to the Study of Bacteria

Al-Qadiri

Lin

et al. 2011

Food Bioprocess Technol

204

120

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Infrared spectroscopy and Raman spectroscopy provide complementary technologies for rapid and precise detection of microorganisms and are emerging methods in food analysis. It is possible to use either of these techniques to differentiate and quantify microorganisms in relatively simple matrices such as liquid media and simple solutions with determinations taking less than an hour. Vibrational spectroscopy, unlike other techniques used in microbiology, is a relatively simple method for studying structural changes occurring within a microbial cell following environmental stress and applications of food processing treatments. Vibrational spectroscopy provides a wide range of biochemical properties about bacteria in a single spectrum, most importantly characteristics of the cell membrane. These techniques are especially useful for studying properties of bacterial biofilms on contact surfaces, the presence and viability of bacterial vegetative cells and spores, the type and degree of bacterial injury, and assessment of antibiotic susceptibility. Future trends in food analysis will involve combining vibrational spectroscopy with microscopy, mass spectroscopy, or DNA-based methods to comprehensively study bacterial stress. Further advances in selectivity, sensitivity, and improved chemometric methods, along with reduction in the cost of instrumentation, may lead to the development of fieldready and real-time analytical systems.

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“…FT-IR spectra are complex multivariate datasets, which can be extremely difficult to analyze by simplistic visual methods; consequently, multivariate mathematical modeling (chemometrics) can be applied to identify patterns within sets of these data (41). Two clustering methods are used here, viz., principal component (PC) analysis (PCA) and discriminant function (DF) analysis (DFA).…”

mentioning

confidence: 99%

High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics

Johnson

Broadhurst

Kell

et al. 2004

Appl Environ Microbiol

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Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm ؊1 ) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.

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Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics

Cited by 32 publications

References 119 publications

Single-nucleotide polymorphism detection using nanomolar nucleotides and single-molecule fluorescence

Single-nucleotide polymorphism detection using nanomolar nucleotides and single-molecule fluorescence

Application of Mid-infrared and Raman Spectroscopy to the Study of Bacteria

High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics

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