title: [Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine].
abstract: The purpose of the present paper is to determine calcium and magnesium in tobacco using NIR combined with least squares-support vector machine (LS-SVM). Five hundred ground and dried tobacco samples from Qujing city, Yunnan province, China, were surveyed by a MATRIX-I spectrometer (Bruker Optics, Bremen, Germany). At the beginning of data processing, outliers of samples were eliminated for stability of the model. The rest 487 samples were divided into several calibration sets and validation sets according to a hybrid modeling strategy. Monte-Carlo cross validation was used to choose the best spectral preprocess method from multiplicative scatter correction (MSC), standard normal variate transformation (SNV), S-G smoothing, 1st derivative, etc., and their combinations. To optimize parameters of LS-SVM model, the multilayer grid search and 10-fold cross validation were applied. The final LS-SVM models with the optimizing parameters were trained by the calibration set and accessed by 287 validation samples picked by Kennard-Stone method. For the quantitative model of calcium in tobacco, Savitzky-Golay FIR smoothing with frame size 21 showed the best performance. The regularization parameter λ of LS-SVM was e16.11, while the bandwidth of the RBF kernel σ2 was e8.42. The determination coefficient for prediction (Rc(2)) was 0.9755 and the determination coefficient for prediction (Rp(2)) was 0.9422, better than the performance of PLS model (Rc(2)=0.9593, Rp(2)=0.9344). For the quantitative analysis of magnesium, SNV made the regression model more precise than other preprocess. The optimized λ was e15.25 and σ2 was e6.32. Rc(2) and Rp(2) were 0.9961 and 0.9301, respectively, better than PLS model (Rc(2)=0.9716, Rp(2)=0.8924). After modeling, the whole progress of NIR scan and data analysis for one sample was within tens of seconds. The overall results show that NIR spectroscopy combined with LS-SVM can be efficiently utilized for rapid and accurate analysis of calcium and magnesium in tobacco.
authors.forename: Kuang-da
authors.initials: KD
authors.lastname: Tian
authors.forename: Kai-xian
authors.initials: KX
authors.lastname: Qiu
authors.forename: Zu-hong
authors.initials: ZH
authors.lastname: Li
authors.forename: Ya-qiong
authors.initials: YQ
authors.lastname: Lü
authors.forename: Qiu-ju
authors.initials: QJ
authors.lastname: Zhang
authors.forename: Yan-mei
authors.initials: YM
authors.lastname: Xiong
authors.forename: Shun-geng
authors.initials: SG
authors.lastname: Min
mesh_terms: D002118:Calcium / Q000032:analysis*; D002138:Calibration; D002681:China; D016018:Least-Squares Analysis; D008274:Magnesium / Q000032:analysis*; D008962:Models, Theoretical; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine; D014026:Nicotiana / Q000737:chemistry*
chemical_list: D008274:Magnesium; D002118:Calcium