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### 基于高光谱反透射图像的新疆冰糖心红富士水心鉴别

1. （1.新疆农业大学机电工程学院，新疆 乌鲁木齐 830052；2.内蒙古农业大学机电工程学院，内蒙古 呼和浩特 010018；3.新疆农业大学数理学院，新疆 乌鲁木齐 830052）
• 出版日期:2020-03-25 发布日期:2020-03-23
• 基金资助:
国家自然科学基金面上项目（61367001）；新疆农业大学研究生科研创新项目（XJAUGRI2017-031）

### Identification of Watercore in Xinjiang-Grown Fuji Apples Based on Reflection-Transmission Hyperspectral Imaging

GUO Junxian, MA Yongjie, TIAN Haiqing, HUANG Hua, SHI Yong, ZHOU Jun

1. (1. College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, ürümqi 830052, China; 2. College of Mechanical and Electronic Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; 3. College of Mathematics and Physics, Xinjiang Agricultural University, ürümqi 830052, China)
• Online:2020-03-25 Published:2020-03-23

Abstract: In this research, hyperspectral technique combined with chemometrics was used to discriminate between Xinjiang-grown Fuji apples with and without watercore. Visible and near infrared hyperspectral images were acquired within the wavelength range of 380 to 1 004 nm. The region of interest was selected from the images to calculated average spectra. The original spectra were preprocessed by 9 different methods such as direct difference first-order derivative, and then principal component analysis (PCA), independent component analysis and correlation coefficient method were used to reduce the dimensionality of the spectral data. Finally, Bayes discriminant, K nearest neighbor method, Mahalanobis distance discriminant, least squares support vector machine, quadratic linear discriminant method were combined to perform pattern recognition. Results indicated that 15 principal components were extracted by PCA. The model developed using standard normal variate (SNV) or multiple scatter calibration (MSC) combined with PCA and least squares support vector machine (LSSVM) exhibited the best recognition performance with identification rates for the calibration and prediction sets of 100% and 91.2%, respectively.