Abstract
Objective: This study focuses on designing a more accurate identification model of lettuce storage time. Methods: The near-infrared spectral data from extraction feature of the preprocessed lettuce was obtained by principal component analysis (PCA), discriminant principal component analysis (DPCA) and fuzzy discriminant principal component analysis (FDPCA) respectively. An algorithm of higher accuracy in storage time discrimination was explored, and then a lettuce storage time discriminant model based on FDPCA was established. Results: The identification accuracy raised dramatically after FDPCA was used to extract feature. When employing PCA, DPCA and FDPCA algorithms, the highest accuracies achieved were 46.67%, 86.67% and 93.33% respectively. Conclusion: This discrimination model of employing near-infrared spectroscopy and FDPCA was characterized by high accuracy and superiority.
Publication Date
10-28-2021
First Page
119
Last Page
123
DOI
10.13652/j.issn.1003-5788.2021.10.021
Recommended Citation
Xiao-lei, HOU; Xiao-hong, WU; Bin, WU; Jia-qi, SHEN; and Xin, WANG
(2021)
"Discrimination of lettuce storage time based on fuzzy discriminant principal component analysis,"
Food and Machinery: Vol. 37:
Iss.
10, Article 21.
DOI: 10.13652/j.issn.1003-5788.2021.10.021
Available at:
https://www.ifoodmm.cn/journal/vol37/iss10/21
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