کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1235680 | 968851 | 2007 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Calibration in non-linear NIR spectroscopy using principal component artificial neural networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Near-infrared (NIR) spectroscopy was used in simultaneous, non-destructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PC-ANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC) spectra led to simplified and more robust models than conventional spectra. In PC-ANNs models, the spectra data were analyzed by principal component analysis (PCA) firstly. Then the scores of the principal compounds (PCs) were chosen as input nodes for input layer instead of the spectra data. The artificial neural networks (ANNs) models using the spectra data as input nodes were also established, which were compared with the PC-ANNs models. The result shows the SNV model of PC-ANNs multivariate calibration has the lowest training error and predicting error. The concept of the degree of approximation was introduced and performed as the selective criterion of the optimum network parameters.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy - Volume 68, Issue 5, 31 December 2007, Pages 1201-1206
Journal: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy - Volume 68, Issue 5, 31 December 2007, Pages 1201-1206
نویسندگان
Ying Dou, Tingting Zou, Tong Liu, Nan Qu, Yulin Ren,