Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration

Xu, Lu; Ye, Zihong; Yan, Si-Min; Shi, Peng-Tao; Cui, Henggang; Fu, Xian-Shu; Yu, Xiaoping

doi:10.1016/j.aca.2012.10.011

Cited by 23 publications

(9 citation statements)

References 28 publications

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“…Ensemble strategies, also called data fusion, consensus, multimodel, or population analysis in different research, have been combined with multivariate calibration, pattern recognition, calibration transfer, variable selection, outlier detection, preprocessing, and parameter selection in chemometrics. Among these methods, ensemble calibration is the hottest research topic.…”

Section: Introductionmentioning

confidence: 99%

Ensemble calibration for the spectral quantitative analysis of complex samples

Bian

Diwu

Liu

et al. 2017

Journal of Chemometrics

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Ensemble strategies have gained increasing attention in multivariate calibration for quantitative analysis of complex samples. The aim of ensemble calibration is to obtain a more accurate, stable, and robust prediction by combining the predictions of multiple submodels. The generation and calibration of the training subsets, as well as the integration of the submodels, are three keys to the success of ensemble calibration. Many training subset generating and submodel integrating strategies have been developed to form numerous ensemble calibration methods for improving the performance of the basic calibration method. This contribution focuses on the recent ensemble strategies in relation to calibration, especially the ensemble modeling for quantitative analysis of complex samples. The limitations and perspectives of ensemble strategies are also discussed.

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Section: Introductionmentioning

confidence: 99%

Ensemble calibration for the spectral quantitative analysis of complex samples

Bian

Diwu

Liu

et al. 2017

Journal of Chemometrics

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“…For instance, several nonseparation/nondestructive techniques (e.g., isotope ratio mass spectrometry, infrared spectroscopy, and nuclear magnetic resonance spectroscopy) have been applied for the detection of adulterated sesame oils [1,4,6]. Previous studies have also employed techniques such as gas chromatography (GC) coupled with a flame ionization detector [7] or mass spectrometer [8,9], high performance liquid chromatography with a refractive index detector [10], an evaporative light scattering detector [5,11] or a fluorescence detector [12], an electronic nose [13], and realtime PCR [14].…”

Section: Introductionmentioning

confidence: 99%

Determination of 2-Propenal Using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography–Time-of-Flight Mass Spectrometry as a Marker for Authentication of Unrefined Sesame Oil

Mansur

Nam

Jang

et al. 2017

Journal of Chemistry

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Ascertaining the authenticity of the unrefined sesame oil presents an ongoing challenge. Here, the determination of 2-propenal was performed by headspace solid-phase microextraction (HS-SPME) under mild temperature coupled to gas chromatography with time-of-flight mass spectrometry, enabling the detection of adulteration of unrefined sesame oil with refined corn or soybean oil. Employing this coupled technique, 2-propenal was detected in all tested refined corn and soybean oils but not in any of the tested unrefined sesame oil samples. Using response surface methodology, the optimum extraction temperature, equilibrium time, and extraction time for the HS-SPME analysis of 2-propenal using carboxen/polydimethylsiloxane fiber were determined to be 55 ∘ C, 15 min, and 15 min, respectively, for refined corn oil and 55 ∘ C, 25 min, and 15 min, respectively, for refined soybean oil. Under these optimized conditions, the adulteration of unrefined sesame oil with refined corn or soybean oils (1-5%) was successfully detected. The detection and quantification limits of 2-propenal were found to be in the range of 0.008-0.010 and 0.023-0.031 g mL −1 , respectively. The overall results demonstrate the potential of this novel method for the authentication of unrefined sesame oil.

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“…However, to tackle the problem of food adulterations and frauds, both DA and CMTs have encountered some difficulties [10][11][12]. DA models are trained from two or more known classes, so their applications should be limited to predictions of objects from the predefined classes; otherwise, if a new object comes from an untrained class, the prediction would be unreliable or wrong [13]. In contrast, CMTs are trained using only the data of one class (e.g., PDO food to be controlled) and can answer the question of whether a future object is from the target class or not.…”

Section: Introductionmentioning

confidence: 99%

Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics

Cai

She

et al. 2015

Journal of Spectroscopy

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The traceability of a Chinese white lotus seed (WLS) with Protected Designation of Origin (PDO) was investigated using nearinfrared (NIR) spectroscopy and chemometrics. Three chemometrics methods, discrimination analysis (DA), class modeling, and a newly proposed strategy, the fusion of DA and class modeling, were investigated to compare their capacity to trace the geographical origins of WLS. Least squares support vector machine (LS-SVM) was developed to distinguish the PDO WLS from non-PDO WLS of four main producing areas. A class modeling technique, one-class partial least squares (OCPLS), was developed only using the data of PDO WLS. By the fusion of LS-SVM and OCPLS, the best prediction sensitivity and specificity were 0.900 and 0.973, respectively. The results indicate that fusion of DA and class modeling can enhance the specificity for detection of non-PDO products. The conclusion is that DA and class modeling should be combined for tracing food geographical origins.

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Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration

Cited by 23 publications

References 28 publications

Ensemble calibration for the spectral quantitative analysis of complex samples

Ensemble calibration for the spectral quantitative analysis of complex samples

Determination of 2-Propenal Using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography–Time-of-Flight Mass Spectrometry as a Marker for Authentication of Unrefined Sesame Oil

Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics

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