Support Vector Machines: A Recent Method for Classification in Chemometrics

Xu, Yun; Zomer, Simeone; Brereton, Richard G.

doi:10.1080/10408340600969486

Cited by 164 publications

(102 citation statements)

References 31 publications

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“…However, the PLS-DA model showed less prediction error than the SVM model. This might be due to the SVM model which is good for less variant nonlinear spectral model [50,51]. The spectral data contained many linear variants which caused the higher prediction error in the SVM model, which was identical with the previous report [52].…”

Section: Discussionsupporting

confidence: 77%

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Yang

Liu

et al. 2018

Journal of Spectroscopy

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Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.

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Section: Discussionsupporting

confidence: 77%

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Yang

Liu

et al. 2018

Journal of Spectroscopy

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“…A combination of LS-SVM, SNV preprocessing, and selected variables produced most accurate and robust regression models [131]. However, for linear problems that are well-understood in analytical chemistry, SVMs are unnecessarily complicated and conventional approaches, such as LDA, PCA, and PLSR, are more effective [125].…”

Section: Recent Advances In Chemometricsmentioning

confidence: 99%

“…The results infer that all pre-processing methods except piecewise MSC exert equivalent impact on prediction performance, which is evaluated by root mean square error of prediction (RMSEP) values, of PLS models for particle sizes. Support vector machine (SVM) is a relatively new classification technique that has only recently been introduced to the chemometrics society to solve both classification and calibration problems [125]. The SVM is a boundary method based on statistical learning theory with the aim of determining optimal separating boundaries between classes [126].…”

Section: Recent Advances In Chemometricsmentioning

confidence: 99%

Near-infrared spectroscopy and imaging in food quality and safety

Wang

Paliwal

2007

Sens. & Instrumen. Food Qual.

141

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Over the last two decades, near-infrared spectroscopy (NIRS) has established itself as a non-destructive analytical technique in a variety of disciplines. However, recent technological advancements in hardware design and data mining techniques have unleashed the potential of NIRS to become a tool of choice for routine analyses of agricultural products. The current paper synthesizes the status of NIRS in the agri-food industry in terms of hardware and software development as well as the direction in which the NIRS research is headed. An extensive review of literature reveals that the emphasis on hardware development is focused on developing compact, robust, and portable spectrometers and hyperspectral imaging (HSI) systems. The software development on the other hand is geared towards developing better preprocessing, analyses, and modeling techniques using chemometrics, support vector machines, and artificial neural networks. The four main agri-food sectors identified to be the beneficiaries of this research revolution are grain quality monitoring; postharvest handling of fruits and vegetables; identification of contaminants in animal produce and feed; and food safety and authenticity. Apart from discussing the aforementioned topics, the paper also provides food scientists some working knowledge on parameters crucial to the performance of spectral and imaging systems. It is expected that further development of NIRS will help agricultural and food scientists to enhance the quality and safety of our food.

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“…Although in this paper simple and widely used tools such as PLSDA and SIMCA have been used, other classification techniques, such as Support Vector Machines [23], Linear and Quadratic Discriminant Analysis (LDA & QDA) [24], and KNN [25], could be taken into consideration, among others. In this paper, the three approaches studied showed good classification performance in terms of the area under the ROC) curve (the so-called AUROC).…”

Section: Discussionmentioning

confidence: 99%

Using warping information for batch process monitoring and fault classification

González-Martínez

Westerhuis

Ferrer

2013

Chemometrics and Intelligent Laboratory Systems

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ElsevierGonzalez-Martinez, J.; Ferrer Riquelme, AJ.; Westerhuis, J. (2013) AbstractThis paper discusses how to use the warping information obtained after batch synchronization for process monitoring and fault classification. The warping information can be used for i) building unsupervised control charts or ii) fault classification when a rich faulty batches database is available. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the proposal.

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Support Vector Machines: A Recent Method for Classification in Chemometrics

Cited by 164 publications

References 31 publications

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Near-infrared spectroscopy and imaging in food quality and safety

Using warping information for batch process monitoring and fault classification

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