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Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)
المؤلفون المشاركون
Zhang, Yujun
Lu, Changhua
Hong, Feng
Wang, Tao
Wang, Jizhou
Ju, Wei
Ou, Chunsheng
Jiang, Weiwei
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-03
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration.
However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity.
It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm.
The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy.
The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene.
Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms.
Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model.
Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jiang, Weiwei& Lu, Changhua& Zhang, Yujun& Ju, Wei& Wang, Jizhou& Hong, Feng…[et al.]. 2020. Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS). Journal of Spectroscopy،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190794
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jiang, Weiwei…[et al.]. Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS). Journal of Spectroscopy No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1190794
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jiang, Weiwei& Lu, Changhua& Zhang, Yujun& Ju, Wei& Wang, Jizhou& Hong, Feng…[et al.]. Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS). Journal of Spectroscopy. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1190794
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1190794
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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