Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

Borin, Alessandra; Ferrão, Marco Flôres; Mello, César; Maretto, Danilo Althmann; Poppi, Ronei J.

doi:10.1016/j.aca.2006.07.008

Cited by 254 publications

(144 citation statements)

References 32 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…The SVM method proposed by Vapnik can simultaneously minimize estimation errors and model dimensions, has superior generalization and accurate prediction capabilities, and can prevent overfitting problems (Borin et al 2006;Durand et al 2007;Langeron et al 2007;Pierna et al 2004). During SVM model development, the determination of the optimal combination of C and g is greatly important in constructing high-performance regression models.…”

Section: Results Of Svm Modelmentioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

View full text Add to dashboard Cite

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

show abstract

Section: Results Of Svm Modelmentioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…Support vector machines are the models that involve a solution of a quadratic programming problem leading to global models that are often unique [55]. The application of this type of model is further discussed and investigated by the authors in Refs.…”

Section: Nonlinear Calibration Models For Near-infrared Spectroscopymentioning

confidence: 99%

“…Least squares SVM is capable of dealing with both linear and nonlinear multivariate calibration problems relatively fast [60]. In LS-VSM, a linear estimation is done in a kernel-induced feature space; the use of LS-SVM and NIR has been investigated by Borin et al [55].…”

Section: Nonlinear Calibration Models For Near-infrared Spectroscopymentioning

confidence: 99%

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

Nomngongo¹,

Munonde²,

Mpupa³

et al. 2017

Developments in Near-Infrared Spectroscopy

View full text Add to dashboard Cite

Environmental contamination by trace elements is becoming increasingly important problem worldwide. Trace metals such as cadmium, copper, lead, chromium, and mercury are major environmental pollutants that are predominantly found in areas with high anthropogenic activities. Therefore, there is a need for rapid and reliable tools to assess and monitor the concentration of heavy metal in environmental matrices. A nondestructive, cost-effective, and environmentally friendly procedure based on near-infrared reflectance spectroscopy (NIRS) and chemometric tools has been used as alternative technique for the simultaneous estimation of various heavy metal concentrations in environmental sample. The metal content is estimated by assigning the absorption features of metals associated with molecular vibrations of organic and inorganic functional groups in organic matter, silicates, carbonates, and water at 780-2500 nm in the near-infrared region.This chapter, reviewed the application of NIRS combined with chemometric tools such as multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS) regression. The disadvantages and advantages of each chemometric tool are discussed briefly.

show abstract

“…Therefore, LS-SVM encompasses similar advantages as SVM, but its additional advantage is that it requires only solving a set of linear equations, which is much easier and computationally very simple (Thissen et al, 2004). A comprehensive introduction of LS-SVM was presented in literatures (Suykens et al, 2002;Thissen et al, 2004;Borin et al, 2006;Li et al, 2007).…”

Section: Chemometrics Analysismentioning

confidence: 99%

Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice

Xie

2009

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.

show abstract

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

Cited by 254 publications

References 32 publications

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice

Contact Info

Product

Resources

About