• 成分分析 •

### 基于MCCV-CARS-RF建立红提糖度和酸度的可见-近红外光谱无损检测方法

1. （华中农业大学工学院，湖北?武汉 430070）
• 出版日期:2018-04-25 发布日期:2018-04-17
• 基金资助:
湖北省自然科学基金项目（012FKB02910）；湖北省研究与开发计划项目（2011BHB016）

### Nondestructive Detection of Sugar Content and Acidity in Red Globe Table Grapes Using Visible Near Infrared Spectroscopy Based on Monte-Carlo Cross Validation-Competitive Adaptive Reweighted Sampling-Random Forest (MCCV-CARS-RF)

XU Feng, FU Dandan, WANG Qiaohua*, XIAO Zhuang, WANG Bin

1. (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)
• Online:2018-04-25 Published:2018-04-17

Abstract: A USB2000+ micro spectrometer was used to acquire the transmittance spectra of Red Globe table grapes in the range of 400–1 000 nm. Moreover, the sugar content and acidity value were measured chemically. The original spectra were pretreated by SavitZky-Golay smoothing (SG) and then the singularity was eliminated by Monte-Carlo cross validation method (MCCV) followed by dimension reduction by competitive adaptive reweighted sampling for development of a random forest (RF) prediction model. The correlation coefficient and root mean square error of the sugar prediction model were 0.955 8 and 0.315 8 for the calibration set, and 0.956 8 and 0.318 5 for the validation set, respectively. The correlation coefficient and root mean square error of the acidity prediction model were 0.945 6 and 0.300 1 for the calibration set, and 0.940 5 and 0.311 2 for the validation set, respectively. The results showed that this method could be suitable for rapid, nondestructive and accurate detection of sugar content and acidity in Red Globe table grapes.