Non-destructive Identification of the geographical origin of red jujube by near-infrared spectroscopy and fuzzy clustering methods

ABSTRACT

The red jujube quality is closely associated with its place of origin. In order to quickly and easily identify the geographical origin of red jujube, the classification of red jujube samples’ near-infrared reflectance (NIR) spectra was performed using several fuzzy clustering methods in combination with principal component analysis (PCA) and linear discriminant analysis (LDA). Firstly, a NIR-M-R2 portable near-infrared spectrometer was used to collect four varieties of red jujube samples from four representative producing areas in four provinces: Gansu, Henan, Shanxi and Xinjiang in China. Each variety corresponded to a producing area, and it had 60 samples with a total of 240 samples. Near-infrared spectra of red jujube were acquired using a NIR-M-R2 portable near-infrared spectrometer, and the initial near-infrared spectra were preprocessed by Savitzky-Golay (SG) filtering. Secondly, PCA and LDA were used to further process the NIR data for dimension reduction and feature extraction, respectively. Finally, red jujube samples were classified by fuzzy C-means (FCM) clustering, Gustafson-Kessel (GK) clustering and possibility fuzzy C-means (PFCM) clustering. When GK served as the clustering algorithm, the clustering accuracy was the highest, as the value of 98.8%. Based on the experimental results, it was evident that the GK clustering algorithm played a significant role in identifying the place of origin of red jujube with near-infrared spectroscopy.

KEYWORDS:

• red jujube
• near-infrared spectroscopy
• linear discriminant analysis
• fuzzy C-means
• fuzzy clustering algorithm
• Gustafson-kessel
• possibility fuzzy C-means clustering algorithm

Acknowledgement

The authors sincerely thank Mr. Zuxuan Qi for providing NIR spectra of red jujube.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data are available from the corresponding authors.

Additional information

Funding

This research was funded by Jinling Institute of Technology High-level Talent Research Start-up Project (JIT-RCYJ-202102), Key R&D Plan Project of Jiangsu Province (BE2022077), Jiangsu Province College Student Innovation Training Program Project (202313573080Y, 202313573081Y), the Major Natural Science Research Projects of Colleges and Universities in Anhui Province (2022AH040333), the Undergraduate Innovation and Entrepreneurship Training Program of Jiangsu Province (202213986008Y).