[Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane].

UNLABELLED: Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was ex- panded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were signif- icantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively. CONCLUSION: Vis-NIR spectro- scopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.

[Fourier transform infrared spectroscopy combined with attenuated total reflection applied to reagent-free quantitative analysis of thalassemia screening indicators].

UNLABELLED: A simultaneous quantitative analysis method for the thalassemia screening indicators mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), and hemoglobin (Hb) was developed with Fourier transform infrared (FTIR) spectrometers and attenuated total reflection (ATR) combined with partial least squares (PLS). A total of 380 human peripheral blood samples were collected, which were composed of 180 positive samples and 200 negative samples according to the criteria of hematological indicator screening for thalassemia. One hundred fifty samples (64 negative, 86 positive) were randomly selected from all samples as the validation set, the remaining 230 samples (136 negative, 94 positive) were used as modeling samples; and then the modeling set was further subdivided into calibration set (68 negative, 47 positive, and 115 in total) and prediction set (68 negative, 47 positive, and 115 in total) for 200 times. Comparison of experimental results show that the prediction effect of PLS models in mid-infrared (MIR) fingerprint region (1,600-900 cm(-1)) was significantly better those of PLS models in the full scanning region (4,000-600 cm(-1)), and model complexity is significantly reduced. Based on PLS model in MIR fingerprint region, the optimal numbers of PLS factors for MCH, MCV and Hb were 10, 10 and 6, respectively, and the root mean square error (M_SEP(Ave)) and the correlation coefficient (M_Rp, Ave) of prediction in the modeling set were 2.19 pg, 0.902 for MCH, 5.13 fL, 0.898 for MCV and 8.0 g · L(-1), 0.922 for Hb, respectively. The root mean square error (V_SEP) and the correlation coefficient (V_Rp) of prediction in the validation set were 2.22 pg, 0.900 for MCH, 5.38 fL, 0.895 for MCV and 7.7 g · L(-1), 0.929 for Hb, respectively. The sensitivity and specificity for thalassemia screening achieved 100.0% and 95.3%, respectively. CONCLUSION: FTIR/ATR spectroscopy combined with PLS method could provide a new reagent-free and rapid technique for thalassemia screening for large populations.