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Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms.
PLoS One. 2014; 9(5):e98522.Plos

Abstract

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874-1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.

Authors+Show Affiliations

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24879306

Citation

Xie, Chuanqi, et al. "Identification of Different Varieties of Sesame Oil Using Near-infrared Hyperspectral Imaging and Chemometrics Algorithms." PloS One, vol. 9, no. 5, 2014, pp. e98522.
Xie C, Wang Q, He Y. Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms. PLoS One. 2014;9(5):e98522.
Xie, C., Wang, Q., & He, Y. (2014). Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms. PloS One, 9(5), e98522. https://doi.org/10.1371/journal.pone.0098522
Xie C, Wang Q, He Y. Identification of Different Varieties of Sesame Oil Using Near-infrared Hyperspectral Imaging and Chemometrics Algorithms. PLoS One. 2014;9(5):e98522. PubMed PMID: 24879306.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms. AU - Xie,Chuanqi, AU - Wang,Qiaonan, AU - He,Yong, Y1 - 2014/05/30/ PY - 2014/02/21/received PY - 2014/05/03/accepted PY - 2014/6/1/entrez PY - 2014/6/1/pubmed PY - 2015/1/13/medline SP - e98522 EP - e98522 JF - PloS one JO - PLoS One VL - 9 IS - 5 N2 - This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874-1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/24879306/Identification_of_different_varieties_of_sesame_oil_using_near_infrared_hyperspectral_imaging_and_chemometrics_algorithms_ L2 - https://dx.plos.org/10.1371/journal.pone.0098522 DB - PRIME DP - Unbound Medicine ER -