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Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Jan 15; 189:183-189.SA

Abstract

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

Authors+Show Affiliations

Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China.Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China. Electronic address: chaotan1112@163.com.Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28810180

Citation

Chen, Hui, et al. "Classification and Quantitation of Milk Powder By Near-infrared Spectroscopy and Mutual Information-based Variable Selection and Partial Least Squares." Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, vol. 189, 2018, pp. 183-189.
Chen H, Tan C, Lin Z, et al. Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares. Spectrochim Acta A Mol Biomol Spectrosc. 2018;189:183-189.
Chen, H., Tan, C., Lin, Z., & Wu, T. (2018). Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 189, 183-189. https://doi.org/10.1016/j.saa.2017.08.034
Chen H, et al. Classification and Quantitation of Milk Powder By Near-infrared Spectroscopy and Mutual Information-based Variable Selection and Partial Least Squares. Spectrochim Acta A Mol Biomol Spectrosc. 2018 Jan 15;189:183-189. PubMed PMID: 28810180.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares. AU - Chen,Hui, AU - Tan,Chao, AU - Lin,Zan, AU - Wu,Tong, Y1 - 2017/08/10/ PY - 2017/01/11/received PY - 2017/08/07/revised PY - 2017/08/09/accepted PY - 2017/8/16/pubmed PY - 2018/8/11/medline PY - 2017/8/16/entrez KW - Feature selection KW - Milk KW - Near-infrared KW - PLS- SP - 183 EP - 189 JF - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy JO - Spectrochim Acta A Mol Biomol Spectrosc VL - 189 N2 - Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content. SN - 1873-3557 UR - https://www.unboundmedicine.com/medline/citation/28810180/Classification_and_quantitation_of_milk_powder_by_near_infrared_spectroscopy_and_mutual_information_based_variable_selection_and_partial_least_squares_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1386-1425(17)30661-3 DB - PRIME DP - Unbound Medicine ER -