One-class partial least squares (OCPLS) classifier

Xu, Lu; Yan, Si-Min; Cai, Chen-Bo; Yu, Xiaoping

doi:10.1016/j.chemolab.2013.04.008

Cited by 83 publications

(26 citation statements)

References 34 publications

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“…A recently proposed one-class partial least squares DA (OCPLS-DA) (Xu et al 2011(Xu et al , 2013 was also used to classify PDO and non-PDO objects. OCPLS-DA works as a special PLS regression to predict a response vector of 1 (with all elements being ones) using the spectral features of the target class (e.g., PDO objects).…”

Section: Multi-class and One-class Da Modelsmentioning

confidence: 99%

Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

Yang

et al. 2015

Food Anal. Methods

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Both multi-class and one-class discrimination analyses (DAs) have been widely used for tracing the geographical origins of Protected Designation of Origin (PDO) foods. However, due to the complexity of potential non-PDO frauds, both methods have encountered some problems. Because multiclass DA tries to classify two or more predefined classes, its classification results will be unreliable when it is used to predict a new object from an untrained class. One-class DA is developed using only the information concerning one-class objects, so they cannot necessarily ensure the model specificity for detection of various food frauds. In this work, a new chemometric strategy was proposed by fusion of multi-class and one-class DA to trace the geographical origin of a Chinese dried shiitake mushroom with PDO. The PDO shiitake objects (n=161) and non-PDO objects (n=264) from five other main producing areas were analyzed using near-infrared spectroscopy. The classification performance of multi-class DA, oneclass DA, and model fusion was compared. With second-order derivative (D2) spectra, model fusion obtained a high sensitivity (0.944) and specificity (0.968). Model comparison indicates that fusion of multi-class and one-class DA can enhance the specificity for detecting various non-PDO foods with little loss of model sensitivity.

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Section: Multi-class and One-class Da Modelsmentioning

confidence: 99%

Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

Yang

et al. 2015

Food Anal. Methods

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“…In OCPLS, the potential function probability density is calculated on PLS scores and residual Q statistics to develop an efficient one-class classifier. The detailed algorithm of OCPLS modeling is described elsewhere 24,[27][28][29] . Initially, a PLS model was developed by analytical data and response vectors with all elements being 1 using the SIMPLS algorithm 30 10 In this study, a one-class classification model was built to identify the authenticity of peanut oils by fatty acid profiles.…”

Section: Multivariate Analysismentioning

confidence: 99%

“…For example, we could obtain an adulterated peanut oil by adding 5% of soybean, sesame, sunflower and rapeseed with random proportions to 95% of peanut oil. 6 A one-class partial least squares (OCPLS) classifier was proposed based on partial least squares (PLS) using a distance-based sample density measurement as the response variable [26][27][28][29] . In OCPLS, the potential function probability density is calculated on PLS scores and residual Q statistics to develop an efficient one-class classifier.…”

Section: Multivariate Analysismentioning

confidence: 99%

One-class classification based authentication of peanut oils by fatty acid profiles

Zhang

Sun

et al. 2015

RSC Adv.

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Developing a method of identifying oil authenticity becomes critical for protecting customers' rights as adulteration of edible oils is a particular concern in food quality. Since adulterants in edible oils are usually unknown, the authenticity identification technique is a one-class classification model in chemometrics. In this study, a one-class classification model was built to identify the authenticity of peanut oils by fatty acid profiles. Based on the previous studies, 28 fatty acids were identified and quantified for peanut oils. The authenticity identification model was built by the one-class partial least squares (OCPLS) classifier for peanut oils. Subsequently, the established model was validated by independent test sets. The results indicated that the OCPLS classifier could completely detect adulterated oils and were therefore employed for authenticity assessment. Moreover, counterfeit oils adulterated with different levels of other edible oils were simulated by the Monte Carlo method and employed to test the lowest adulteration level of this one-class classifier. As a result, the model could identify peanut oils and sensitively detect adulteration of edible oils with other vegetable oils at the adulteration level of 4%.

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“…The recently proposed one‐class partial least squares (OCPLS) method (Xu et al . , , ) was used to develop class models with FT‐NIR spectroscopy. FT‐NIR spectroscopy was also combined with partial least squares discrimination analysis (PLSDA; Xie et al .…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Exogenous Adulterants and Species Discrimination for a Chinese Functional Tea (Banlangen) by Fourier‐Transform Near‐Infrared (FT‐NIR) Spectroscopy and Chemometrics

et al. 2015

Journal of Food Quality

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The practical adulterations of herbal tea are usually performed by adding some marker components together with adulterants, or some exogenous adulterants containing the marker components. It is difficult to detect such adulterations by the traditional analytical methods focused on a few marker components. Therefore, Fourier‐transform near‐infrared (FT‐NIR) spectroscopy was used to characterize the compositions and class models of authentic banlangen tea and to detect possible exogenous adulterants. Discrimination of two different species of banlangen tea was also performed using partial least squares discrimination analysis (PLSDA). The most accurate one‐class partial least squares (OCPLS) model was obtained with standard normal variate (SNV) transformation. SNV‐OCPLS could detect adulterations with 5% (w/w) or more dried apple skin in banlangen. For species discrimination, a total classification accuracy of 0.936 was achieved by SNV‐PLSDA. FT‐NIR spectrometry and chemometrics can provide a useful and alternative tool for rapid quality control of banlangen tea. Practical Applications FT‐NIR spectrometry and chemometrics can provide a useful and alternative tool for rapid quality control of banlangen tea.

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One-class partial least squares (OCPLS) classifier

Cited by 83 publications

References 34 publications

Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

One-class classification based authentication of peanut oils by fatty acid profiles

Rapid Detection of Exogenous Adulterants and Species Discrimination for a Chinese Functional Tea (Banlangen) by Fourier‐Transform Near‐Infrared (FT‐NIR) Spectroscopy and Chemometrics

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