Abstract：Outlier samples strongly influence the precision of the calibration model in soluble solids content measurement of melons using NIR Spectra. According to the possible sources of outlier samples, three methods (predicted concentration residual test; Chauvenet test; leverage and studentized residual test) were used to discriminate these outliers respectively. Nine suspicious outliers were detected from calibration set which including 85 fruit samples. Considering the 9 suspicious outlier samples maybe contain some no-outlier samples, they were reclaimed to the model one by one to see whether they influence the model and prediction precision or not. In this way, 5 samples which were helpful to the model joined in calibration set again, and a new model was developed with the correlation coefficient (r) 0.889 and root mean square errors for calibration (RMSEC) 0.601°Brix. For 35 unknown samples, the root mean square errors prediction (RMSEP) was 0.854°Brix. The performance of this model was more better than that developed with non outlier was eliminated from calibration set(r=0.797, RMSEC=0.849°Brix, RMSEP=1.19°Brix), and more representative and stable with all 9 samples were eliminated from calibration set(r=0.892, RMSEC=0.605°Brix, RMSEP=0.862°Brix).
Key words：Melon;Outlier sample;Discriminating methods;NIR spectroscopy;Calibration model
田海清，王春光*，张海军，郁志宏，李建康 . 蜜瓜品质光谱检测中异常建模样品的综合评判[J]. 光谱学与光谱分析, 2012, 32(11): 2987-2991.
TIAN Hai-qing, WANG Chun-guang*, ZHANG Hai-jun, YU Zhi-hong, LI Jian-kang. Outlier Sample Discriminating Methods for Building Calibration Model in Melons Quality Detecting Using NIR Spectra . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(11): 2987-2991.
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