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| NIR Spectral Classification of Lettuce Using Principal Component
Analysis Sort and Fuzzy Linear Discriminant Analysis |
| WU Bin1, SHEN Jia-qi2, WANG Xin2, WU Xiao-hong3, HOU Xiao-lei2 |
1. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
2. Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China
3. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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Abstract The storage time of lettuce is an important factor affecting the quality. The traditional way of detecting lettuce storage time mostly depends on artificial experience, so it lacks accuracy and reliability. This study aims to provide a fuzzy recognition model for spectral analysis of lettuce to identify the storage time of lettuce compared with other discriminant methods. For this objective, sixty samples of fresh lettuce bought in the local supermarket were prepared and stored in a refrigerator for later detection. These samples were detected by near-infrared spectroscopy (NIR). Firstly, the Antaris II NIR spectrometer (the wave number range: 10 000~4 000 cm-1) was utilized to collect the near-infrared spectral data of lettuce samples every 12 hours, and every sample detection was repeated three times, taking the average value as experiment data. Secondly,NIR spectra were preprocessed with multiple scatter correction (MSC) for decreasing reductant information. PCA and PCA Sort were used to further clear the useless data of NIR spectra and simplify the following classification of data. PCA Sort was based on PCA with sorting principal components and could improve the classification accuracy and help the FLDA extract features effectively. In this step, only the first fifteen components of PCA and PCA Sort were used to compress NIR spectra. Finally, fuzzy linear discriminant analysis (FLDA) algorithm and k-nearest neighbor (KNN) were performed to classify the previous low-dimensional data. The classification accuracy of the model based on PCA coupled with KNN was 43%, and that based on PCA as well as FLDA and KNN was 83%. The classification results in experiments showed that the discriminant of the model based on PCA, FLDA and KNN was significantly improved. Replacing PCA in the model with PCA Sort, the recognition accuracy of this new model based on the algorithm PCA Sortcoupled with FLDA and KNN was better and achieved 98.33%, which was higher than other classification algorithms. The classification results in experiments showed that PCA Sort plus FLDA and KNN could build an efficient discrimination model for the identification of the storage time of lettuce.
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Received: 2021-07-19
Accepted: 2022-03-17
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[1] Bie T Y, Xu J S, Yang L L, et al. Food Research and Development, 2012, 33(12): 205.
[2] Dong W, Cheng Z J, Wang X L, et al. International Journal of Food Sciences and Nutrition, 2011, 62(5): 537.
[3] Gao T T, Tian Y, Zhu Z W, et al. Trends in Food Science &Technology, 2020, 99: 311.
[4] Sun L, Yuan L M, Cai J R, et al. Food Analytical Methods, 2015, 8(4): 922.
[5] Sun J, Ge X, Wu X H, et al. Journal of Food Processing and Preservation, 2018, 41(6): e12816.
[6] Jiang S Y, Sun J, Zhou X, et al. Journal of Food Processing and Preservation, 2017, 40(4): e12510.
[7] Yu H D, Zuo S M, Xia G H, et al. Food Analytical Methods, 2020, 6: 1.
[8] Guidea A, Gaceanu R D, Pop H F. Studia Universitatis Babes-Bolyal Chemia, 2020, 65: 45.
[9] Shen Y J, Wu X H, Wu B, et al. Agriculture, 2021, 11: 275.
[10] Nayak S K, Panda M, Palai G. Optik, 2020, 212: 164675.
[11] Chen Q S, Cai J R, Wang X M, et al. LWT—Food Science and Technology, 2011, 2053: 2058.
[12] Wu X H, Zhou H X, Wu B, et al. Journal of Food Processing and Preservation, 2020, 44(8): e14561.
[13] Wu X H, Wu B, Sun J, et al. Journal of Food Process Engineering 2017, 40(2): e12355.
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