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Liu X.,Shanghai JiaoTong University | Zhang X.,CSIC - Institute of Environmental Assessment And Water Research | Rong Y.-Z.,Shanghai JiaoTong University | Wu J.-H.,Shanghai JiaoTong University | And 2 more authors.
Food Analytical Methods | Year: 2014

The feasibility of near-infrared (NIR) spectroscopy to determine fat, protein, and amino acids (AAs) in Coix seed was investigated. Partial least squares (PLS) regression was applied to establish quantitative model. Competitive adaptive reweighted sampling (CARS) and Norris derivative smoothing (NDS) were used to improve the model accuracy. For fat, protein, Asx, threonine, serine, Glx, alanine, leucine, proline, lysine, and histidine, NDS pretreatment improved the models’ performance and yielded better prediction results. Then, key variables were selected by CARS, and the PLS models could obtain the optimal results with good predictive ability and robustness. The models of protein, Asx, serine, Glx, alanine, valine, isoleucine, leucine, phenylalanine, and proline were fit for screening with the residual predictive deviation (RPD, the ratio between the standard deviation of the reference value and the root mean square error of prediction of the validation set) equal or greater than 2.50. The RPDs of fat and threonine were 1.61 and 2.00, and the other AAs’ were less than 1.50. It was concluded that the NIR spectral technique was suitable for determining fat, protein, and most of AAs in Coix seed. The technique might also give a rough estimate of the contents of glycine, cysteine, methionine, tyrosine, arginine, lysine, and histidine. © 2014, Springer Science+Business Media New York. Source


Liu X.,Shanghai JiaoTong University | Rong Y.-Z.,Shanghai JiaoTong University | Zhang X.,CSIC - Institute of Environmental Assessment And Water Research | Mao D.-Z.,Shanghai Institute for Drug Control | And 2 more authors.
Food Analytical Methods | Year: 2015

The feasibility of near-infrared (NIR) spectroscopy for determining total dietary fiber (TDF) and mineral elements (mainly K, Mg, P, and S) in Coix seed was investigated. Partial least squares regression (PLSR) was applied to establish quantitative models. Norris derivative smoothing (NDS) was used as pretreatment method. A comparison of three variable selection methods, namely competitive adaptive reweighted sampling (CARS), genetic algorithms (GA), and random frog (RF), showed that CARS obtained the best performances of PLSR models with the effective wavelengths mainly concentrated on around 12,000–11,000 cm−1 and 6500–3600 cm−1. For the quantitative determination models of TDF, K, Mg, P, and S, the optimal root mean square error of prediction (RMSEP) values were 0.0923, 182.7224, 75.4987, 162.6993, and 36.6278; the r values were 0.95, 0.88, 0.80, 0.96, and 0.96; the residual predictive deviation (RPD) values were 2.68, 2.05, 1.70, 3.24, and 3.04, respectively. It is concluded that the NIR spectral technique has a potential to determine TDF, K, Mg, P, and S in Coix seed. © 2014, Springer Science+Business Media New York. Source

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