Details

Autor(en) / Beteiligte

• Niu, Xiao-Ying
• Shao, Li-Min
• Dong, Fang
• Zhao, Zhi-Lei
• Zhu, Yan

Titel

Discrimination of donkey meat by NIR and chemometrics

Ist Teil von

• Guang pu xue yu guang pu fen xi, 2014-10, Vol.34 (10), p.2737

Ort / Verlag

China

Erscheinungsjahr

2014

Quelle

MEDLINE

Beschreibungen/Notizen

• Donkey meat samples (n = 167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n = 47), pork (n = 51) and mutton (n = 32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4,000~12,500 cm(-1). The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98. 96% of prediction set. For minced samples of 7 mm diameter the optimal resu