Class Analogy (class + analogy)

Distribution by Scientific Domains


Selected Abstracts


Discrimination of cyanobacterial strains isolated from saline soils in Nakhon Ratchasima, Thailand using attenuated total reflectance FTIR spectroscopy

JOURNAL OF BIOPHOTONICS, Issue 8-9 2010
Somchanh Bounphanmy
Abstract A method was developed whereby high quality FTIR spectra could be rapidly acquired from soil-borne filamentous cyanobacteria using ATR FTIR spectroscopy. Spectra of all strains displayed bands typical of those previously reported for microalgae and water-borne cyanobacteria, with each strain having a unique spectral profile. Most variation between strains occurred in the C,O stretching and the amide regions. Soft Independent Modelling by Class Analogy (SIMCA) was used to classify the strains with an accuracy of better than 93%, with best classification results using the spectral region from 1800,950 cm,1. Despite this spectral region undergoing substantial changes, particularly in amide and C,O stretching bands, as cultures progressed through the early-, mid- to late-exponential growth phases, classification accuracy was still good (,80%) with data from all growth phases combined. These results indicate that ATR/FTIR spectroscopy combined with chemometric classification methods constitute a rapid, reproducible, and potentially automated approach to classifying soil-borne filamentous cyanobacteria. ( 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]


OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification,

JOURNAL OF CHEMOMETRICS, Issue 8-10 2006
Max Bylesj
Abstract The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results. Copyright 2007 John Wiley & Sons, Ltd. [source]


Rapid Profiling of Swiss Cheese by Attenuated Total Reflectance (ATR) Infrared Spectroscopy and Descriptive Sensory Analysis

JOURNAL OF FOOD SCIENCE, Issue 6 2009
N.A. Kocaoglu-Vurma
ABSTRACT:, The acceptability of cheese depends largely on the flavor formed during ripening. The flavor profiles of cheeses are complex and region- or manufacturer-specific which have made it challenging to understand the chemistry of flavor development and its correlation with sensory properties. Infrared spectroscopy is an attractive technology for the rapid, sensitive, and high-throughput analysis of foods, providing information related to its composition and conformation of food components from the spectra. Our objectives were to establish infrared spectral profiles to discriminate Swiss cheeses produced by different manufacturers in the United States and to develop predictive models for determination of sensory attributes based on infrared spectra. Fifteen samples from 3 Swiss cheese manufacturers were received and analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). The spectra were analyzed using soft independent modeling of class analogy (SIMCA) to build a classification model. The cheeses were profiled by a trained sensory panel using descriptive sensory analysis. The relationship between the descriptive sensory scores and ATR-IR spectra was assessed using partial least square regression (PLSR) analysis. SIMCA discriminated the Swiss cheeses based on manufacturer and production region. PLSR analysis generated prediction models with correlation coefficients of validation (rVal) between 0.69 and 0.96 with standard error of cross-validation (SECV) ranging from 0.04 to 0.29. Implementation of rapid infrared analysis by the Swiss cheese industry would help to streamline quality assurance. [source]


Detection of Sublethal Thermal Injury in Salmonella enterica Serotype Typhimurium and Listeria monocytogenes Using Fourier Transform Infrared (FT-IR) Spectroscopy (4000 to 600 cm,1)

JOURNAL OF FOOD SCIENCE, Issue 2 2008
H.M. Al-Qadiri
ABSTRACT:, Fourier transform infrared (FT-IR) spectroscopy (4000 to 600 cm,1) was utilized to detect sublethally heat-injured microorganisms: Salmonella enterica serotype Typhimurium ATCC 14028, a Gram-negative bacterium, and Listeria monocytogenes ATCC 19113, a Gram-positive bacterium. A range of heat treatments (N= 2) at 60 C were evaluated: 0D (control), 2D, 4D, 6D, and 8D using a D60 C (S. enterica serotype Typhimurium ATCC 14028 = 0.30 min, L. monocytogenes ATCC 19113 = 0.43 min). The mechanism of cell injury appeared to be different for Gram-negative and Gram-positive microbes as observed from differences in the 2nd derivative transformations and loadings plot of bacterial spectra following heat treatment. The loadings for PC1 and PC2 confirmed that the amide I and amide II bands were the major contribution to spectral variation, with relatively small contributions from C-H deformations, the antisymmetric P==O stretching modes of the phosphodiester nucleic acid backbone, and the C-O-C stretching modes of polysaccharides. Using soft independent modeling of class analogy (SIMCA), the extent of injury could be predicted correctly at least 83% of the time. Partial least squares (PLS) calibration analysis was constructed using 5 latent variables for predicting the bacterial counts for survivors of the different heat treatments and yielded a high correlation coefficient (R= 0.97 [S. enterica serotype Typhimurium] and 0.98 [L. monocytogenes]) and a standard error of prediction (SEP= 0.51 [S. enterica serotype Typhimurium] and 0.39 log10 CFU/mL [L. monocytogenes]), indicating that the degree of heat injury could be predicted. [source]


INTERCOLONIAL VARIABILITY IN MACROMOLECULAR COMPOSITION IN P-STARVED AND P-REPLETE SCENEDESMUS POPULATIONS REVEALED BY INFRARED MICROSPECTROSCOPY,

JOURNAL OF PHYCOLOGY, Issue 5 2008
Philip Heraud
Macromolecular variability in microalgal populations subject to different nutrient environments was investigated, using the chlorophyte alga Scenedesmus quadricauda (Turpin) Brb. as a model organism. The large size of the four-cell coenobia in the strain used in this study (,35 ,m diameter) conveniently allowed high quality spectra to be obtained from individual coenobia using a laboratory-based Fourier transform infrared (FTIR) microscope with a conventional globar source of IR. By drawing sizable subpopulations of coenobia from two Scenedesmus cultures grown under either nutrient-replete or P-starved conditions, the population variability in macromolecular composition, and the effects of nutrient change upon this, could be estimated. On average, P-starved coenobia had higher carbohydrate and lower protein absorbance compared with P-replete coenobia. These parameters varied between coenobia with histograms of the ratio of absorbance of the largest protein and carbohydrate bands being Gaussian distributed. Distributions for the P-replete and P-starved subpopulations were nonoverlapping, with the difference in mean ratios for the two populations being statistically significant. Greater variance was observed in the P-starved subpopulation. In addition, multivariate models were developed using the spectral data, which could accurately predict the nutrient status of an independent individual coenobium, based on its FTIR spectrum. Partial least squares discriminant analysis (PLS-DA) was a better prediction method compared with soft independent modeling by class analogy (SIMCA). [source]


Rapid characterization and quality control of complex cell culture media solutions using raman spectroscopy and chemometrics

BIOTECHNOLOGY & BIOENGINEERING, Issue 2 2010
Boyan Li
Abstract The use of Raman spectroscopy coupled with chemometrics for the rapid identification, characterization, and quality assessment of complex cell culture media components used for industrial mammalian cell culture was investigated. Raman spectroscopy offers significant advantages for the analysis of complex, aqueous-based materials used in biotechnology because there is no need for sample preparation and water is a weak Raman scatterer. We demonstrate the efficacy of the method for the routine analysis of dilute aqueous solution of five different chemically defined (CD) commercial media components used in a Chinese Hamster Ovary (CHO) cell manufacturing process for recombinant proteins. The chemometric processing of the Raman spectral data is the key factor in developing robust methods. Here, we discuss the optimum methods for eliminating baseline drift, background fluctuations, and other instrumentation artifacts to generate reproducible spectral data. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were then employed in the development of a robust routine for both identification and quality evaluation of the five different media components. These methods have the potential to be extremely useful in an industrial context for "in-house" sample handling, tracking, and quality control. Biotechnol. Bioeng. 2010;107: 290,301. 2010 Wiley Periodicals, Inc. [source]