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蜜瓜品质光谱检测中异常建模样品的综合评判

Outlier Sample Discriminating Methods for Building Calibration Model in Melons Quality Detecting Using NIR Spectra

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摘要

针对蜜瓜可溶性固形物含量透射光谱检测中, 异常建模样品对模型精度的影响及多种可能来源, 提出异常样品的综合评判方法。 为防止漏判, 分别针对不同来源, 采用基于预测浓度残差、 Chauvenet检验法及杠杆值与学生残差T检验准则对85个建模样品(偏最小二乘法建模)进行初步判别, 共判别出9个疑似异常样品。 为防止误判, 对疑似样品逐一回收, 考察其对建模与预测精度的影响。 先后回收5个样品后, 所建校正模型相关系数r为0.889, 均方根校正偏差RMSEC为0.601°Brix, 对35个未知样品的均方根预测偏差RMSEP为0.854°Brix, 比未剔除异常样品前所建模型(r=0.797, RMSEC=0.849°Brix, RMSEP=1.19°Brix)精度明显提高, 比剔除全部疑似异常样品所建模型(r=0.892, RMSEC=0.605°Brix, RMSEP=0.862°Brix)更稳定, 预测精度更高。

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).

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中图分类号:S132

DOI:10.3964/j.issn.1000-0593(2012)11-2987-05

基金项目:国家自然科学基金项目(31160248), 中国博士后科学基金项目(20110491551)和2011年博士点基金课题(新教师类)(20111515120008)资助

收稿日期:2011-12-02

修改稿日期:2012-02-25

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田海清:内蒙古农业大学机电工程学院, 内蒙古 呼和浩特010018
王春光:内蒙古农业大学机电工程学院, 内蒙古 呼和浩特010018
张海军:内蒙古农业大学机电工程学院, 内蒙古 呼和浩特010018
郁志宏:内蒙古农业大学机电工程学院, 内蒙古 呼和浩特010018
李建康:内蒙古农业大学机电工程学院, 内蒙古 呼和浩特010018

联系人作者:田海清(hqtian@126.com)

备注:田海清, 1973年生, 内蒙古农业大学机电工程学院博士后

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引用该论文

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[J]. Spectroscopy and Spectral Analysis, 2012, 32(11): 2987-2991

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

被引情况

【1】张德虎,田海清,武士钥,刘超,陈亚莉,王辉. 河套蜜瓜糖度可见近红外光谱特征波长提取方法研究. 光谱学与光谱分析, 2015, 35(9): 2505-2509

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