Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath-Geva clustering algorithm

Apple, as an important agricultural product, has extremely high nutritional value. In order to distinguish apple varieties quickly, accurately, and nondestructively, an improved possibilistic Gath-Geva (IPGG) clustering algorithm was proposed to classify near infrared reflectance (NIR) spectra of apple samples. This paper used Antaris II NIR spectrometer (Thermo Electron Co., USA) to collect NIR spectra of four kinds of apples (Fuji, Huaniu, Gala, and Huangjiao). Then, multiple scatter correction (MSC) and principal component analysis (PCA) were applied to eliminate redundant information and reduce spectral dimensions, respectively. Finally, fuzzy c-means (FCM), Gustafson-Kessel (GK), Gath-Geva (GG), improved possibilistic c-means (IPCM), and IPGG clustering algorithms were run on the preprocessed spectral data. The results shown that the clustering accuracy of IPGG was the highest, and it reached 96.5%. Experimental results demonstrated that NIR spectroscopy along with MSC, PCA, and IPGG clustering was an effective method for identifying apple varieties. Practical applications The apple variety is of great importance to the quality of apple. For this, the proposed IPGG clustering along with near infrared reflectance spectroscopy was used to build an effective classification model to identify apple varieties quickly, accurately, and nondestructively. The experimental results showed that IPGG clustering algorithm has obvious advantages compared with FCM, GK, GG, and IPCM. This study provides a new method for apple grading and screening at the fruit and vegetable processing plants.