樱桃多品质数据分析与无损快速检测模型的建立  

Analysis of the multi-quality data and the development of the non-destructive rapid testing models of cherry

作　　者：王冬[1] 孙俊鹏 于世锋 李菁[3] 邱孟超 韩平[1] 王世芳 WANG Dong;SUN Jun-Peng;YU Shi-Feng;LI Jing;QIU Meng-Chao;HAN Ping;WANG Shi-Fang(Beijing Academy of Agriculture and Forestry Sciences,Beijing Research Center for Agricultural Standards and Testing,Beijing 100097,China;Xi’an Baqiao Testing and Monitoring Cetner for Agricultural Product Quality and Safety,Xi’an 710038,China;Xi’an Testing and Monitoring Center for Agricultural Products Quality and Safety,Xi’an 710077,China;Daxing District Soil and Fertilizer Workstation of Beijing,Beijing 102699,China)

机构地区：[1]北京市农林科学院,北京农业质量标准与检测技术研究中心,北京100097 [2]西安市灞桥区农产品质量安全检验监测中心,西安710038 [3]西安市农产品质量安全检验监测中心,西安710077 [4]北京市大兴区土肥工作站,北京102699

出　　处：《食品安全质量检测学报》2021年第18期7222-7228,共7页Journal of Food Safety and Quality

基　　金：北京市农林科学院科技创新能力建设专项(KJCX201910);北京市农林科学院农业科技示范推广项目“果蔬有机化生产植保投入品评价与应用技术示范”。

摘　　要：目的研究樱桃多品质数据分布情况,建立樱桃多品质无损快速检测方法。方法对樱桃样品分别测试可溶性固形物含量(soluble solid content, SSC)、可滴定酸含量(titratable acid content, TAC)、果实硬度(fruit firmness, Firm)。采用统计分析方法对数据进行统计学描述,分别绘制含量分布直方图并计算直方图分布频次百分比。以樱桃样品近红外(near infrared, NIR)光谱数据为自变量、品质数据参考值为因变量建立樱桃品质无损快速定量检测模型。结果统计分析结果表明,可溶性固形物含量11~17Brix区间范围内的样品数占样品总数的约86.0%,可滴定酸含量0.1%~0.8%区间范围内的样品数占样品总数的约90.4%,果实硬度1.60~3.00kg/cm^(2)区间范围内的样品数占样品总数的约86.0%。多元回归建模结果表明,剔除异常值有助于提高模型预测性能,剔除异常值后可溶性固形物含量、可滴定酸含量、果实硬度模型的相对预测性能值分别提高了15.3%、32.9%、12.3%。结论采用统计分析结合直方图分析可较直观地描述樱桃品质分布情况;剔除异常值对提高樱桃可滴定酸含量近红外无损检测模型预测能力的作用最大。Objective To study the distribution of the multi-quality data of cherry, and develop the non-destructive rapid testing methods for cherry. Methods The cherry samples were tested for soluble solid content(SSC), titratable acid content(TAC) and fruit firmness(Firm). The statistics methods were applied to describe the statistical characteristics of the data, meanwhile the histogram of the content distribution was drawn with the percentage of frequency in the histograms respectively. The non-destructive rapid quantitative calibration models were developed by the near infrared(NIR) spectra data of cherry as independent and the specified values of the qualities as dependent. Results It was demonstrated by statistic analysis that the percentage of the samples with SSC between 11 and 17 Brix was about 86.0% of the total samples, the percentage of the samples with TAC between 0.1% and 0.8% was about 90.4% and the percentage of the samples with Firm between 1.60-3.00 kg/cm^(2) was about 86.0%. It was indicated by multi-regression models that the outlier elimination was good for enhancing the prediction performance of the models, by which, the ratio performance deviation values had been increased by 15.3%, 32.9%, 12.3% for the models of SSC, TAC and Firm respectively. Conclusion Statistical analysis combined with histogram analysis can directly describe the distribution of cherry quality, eliminating outliers has the greatest effect on improving the prediction ability of NIR nondestructive testing model of titratable acid content in cherry.

关 键 词：近红外光谱法 无损检测 可溶性固形物含量 可滴定酸含量 果实硬度 樱桃 

分 类 号：O657.33[理学—分析化学] TS255.7[理学—化学]