Decision criteria for soft independent modelling of class analogy applied to near infrared data

Maesschalck, Roy De; Candolfi, Andrea; Massart, D.L.

doi:10.1016/s0169-7439(98)00159-2

Cited by 152 publications

(104 citation statements)

References 21 publications

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“…Both supervised chemometric models demonstrated a high recognition rate, with a DFA dendrogram model performing a bit better than SIMCA. This usually happens because the classifier in the SIMCA model sometimes identifies samples (i.e., spectra) as members of multiple groups (11). In this study, the SIMCA model had a 97% average recognition rate, indicating the good reliability of our global Raman spectroscopy-based classification model.…”

Section: Discussionmentioning

confidence: 56%

“…SIMCA is a supervised chemometric model describing a plane (for two PCs), and the mean orthogonal distance of training data from this specific plane is calculated as residual standard deviation and subsequently employed to determine a critical distance (on the basis of F distribution with a 95% confidence interval) for the identification of an analyte (i.e., bacteria) to the species level (11). Prediction data were subsequently projected into each PC model, and the residual distances were calculated (whether below the statistical limit for a specific class or not) to determine the class to which the prediction data belong.…”

Section: Discriminatory Powermentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Huang

Miller

et al. 2012

J Clin Microbiol

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A novel strategy for the rapid detection and identification of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter -associated food-borne outbreaks. Bayesian probability coupled with Monte Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures, on the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm −1 and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >7.88). A standard protocol for sample preparation, spectral collection, model validation, and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.

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Section: Discussionmentioning

confidence: 56%

Section: Discriminatory Powermentioning

confidence: 99%

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Huang

Miller

et al. 2012

J Clin Microbiol

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“…New measurements are considered to belong to the class if their Euclidean distance towards the constructed PC space is not significantly larger than the Euclidean distance of the class objects towards their PC space (de Maesschalck et al, 1999). In our research, 30 DEEJ samples and 45 OEJ (Ejiao (Colla Corii Asini)) samples were used as the calibration set to establish the PC space and the corresponding Euclidean distance.…”

Section: Simca Modelmentioning

confidence: 99%

Manufacturer identification and storage time determination of “Dong’e Ejiao” using near infrared spectroscopy and chemometrics

Han

Zhang³

et al. 2016

J. Zhejiang Univ. Sci. B

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Abstract:We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine (TCM), Dong'e Ejiao (DEEJ). Based on near infrared (NIR) spectra of multiple samples, the genuine manufacturer of DEEJ, e.g. Dong'e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T 2 , distance to Model X (DModX), and similarity match value (SMV) as discriminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEEJ, storage time, the partial least squares-discriminant analysis (PLS-DA) method was applied with a satisfactory misjudgment ratio (15.6%) and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEEJ, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.

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“…The term Soft is derived from this point. Within this framework, the outlier values of the data are eliminated (8). A level of statistical significance is calculated according to a F test (5).…”

Section: The Advantages and Disadvantages Of The Modelmentioning

confidence: 99%

Affected States Soft Independent Modeling by Class Analogy from the Relation Between Independent Variables, Number of Independent Variables and Sample Size

Kanik¹,

Temel²,

Erdoğan³

et al. 2013

Balkan Med J

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Decision criteria for soft independent modelling of class analogy applied to near infrared data

Cited by 152 publications

References 21 publications

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Manufacturer identification and storage time determination of “Dong’e Ejiao” using near infrared spectroscopy and chemometrics

Affected States Soft Independent Modeling by Class Analogy from the Relation Between Independent Variables, Number of Independent Variables and Sample Size

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