|
|
|
|
|
|
| Discrimination of Lettuce Storage Time Based on Near-Infrared Spectroscopy Combined With Fuzzy Uncorrelated QR Analysis |
| HU Cai-ping1*, FU Zhao-min2*, XU Hong-jia2, WU Bin3, SUN Jun4 |
1. School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China
2. Jiangsu University Jingjiang College, Zhenjiang 212028, China
3. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
4. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
|
|
|
|
|
Abstract Lettuce is one of the vegetables that people often eat, and the storage time of lettuce is an important factor affecting the freshness of lettuce. Therefore, it is necessary to develop a simple, fast, and non-destructive method to identify the storage time of lettuce. Near-infrared spectroscopy (NIR) can quickly and accurately detect the near-infrared spectrum of lettuce to realize the non-destructive identification of lettuce storage time. However, noise and redundant signals are in the NIR spectral data collected by the near-infrared spectrometer. To eliminate the noise information of the spectrum and extract the feature information, a novel method was proposed to identify the storage time of lettuce based on NIR spectroscopy and fuzzy uncorrelated QR analysis (FUQRA). Firstly, principal component analysis (PCA) was used to reduce the dimension of the original spectral data from 1557 dimensions to 22 dimensions. Secondly, after the feature vectors are obtained by fuzzy uncorrelated discriminant transformation (FUDT), the discriminant vector matrix is established by using the feature vectors, and the final discriminant vector matrix is obtained by QR decomposition. Finally, the k-nearest neighbor algorithm was utilized for classification. 60 fresh lettuce samples were selected as the research object. Firstly, the NIR spectral data of lettuce samples were collected by Antaris Ⅱ near-infrared spectrometer and detected once every 12 hours. Secondly, multivariate scatter correction (MSC) was used to reduce the noise signal in the NIR spectra. To verify the effectiveness of the proposed method, the experimental results were compared by four classification models: principal component analysis (PCA) combined with a K-nearest neighbor (KNN) algorithm, PCA and fuzzy linear discriminant analysis (FLDA) combined with KNN algorithm, PCA and fuzzy uncorrelated discriminant transformation (FUDT) combined with KNN algorithm and PCA and FUQRA combined with KNN algorithm. The classification accuracies produced by different values of the weight indexm were studied, and the most appropriate parameters were selected: m=2, K=3. Under this condition, the classification accuracies of the four algorithms were 43.33%, 96.67%, 96.67%, and 98.33%, respectively. It can be seen that compared with the other three algorithms, FUQRA can better realize the identification of lettuce storage time.
|
|
Received: 2023-06-10
Accepted: 2023-10-26
|
|
|
|
Corresponding Authors:
HU Cai-ping, FU Zhao-min
E-mail: hucp@jit.edu.cn; xijian302@gmail.com
|
|
[1] Guo Z, Zhang Y, Wang J, et al. Computers and Electronics in Agriculture, 2023, 212: 108127.
[2] León L, Ortiz A, Ezquerro S, et al. Meat Science, 2023, 206: 109348.
[3] Tian W, Li Y, Guzman C, et al. Journal of Food Composition and Analysis, 2023, 124: 105708.
[4] Yu F, Liu L, Yu N, et al. Symmetry, 2020, 12(1): 182.
[5] Wu X H, Zhu J, Wu B, et al. Foods, 2019, 8(1): 38.
[6] Hasan B M S, Abdulazeez A M. Journal of Soft Computing and Data Mining, 2021, 2(1): 20.
[7] Du Y J, Lu X B, Zeng W L, et al. Intelligent Data Analysis, 2018, 22(3): 675.
[8] Zhao M B, Chow T W S, Zhang Z. Soft Computing, 2012, 16(8): 1393.
[9] Tang Y G, Mu W W, Zhang X M, et al. Circuits, Systems, and Signal Processing, 2013, 32(2): 711.
[10] Zhang T F, Wu X H, Wu B, et al. Journal of Food Process Engineering, 2022, 45(8): e14040.
[11] Shen Y J, Wu X H, Wu B, et al. Agriculture, 2021, 11: 275.
[12] Mailagaha Kumbure M, Luukka P, Collan M. Pattern Recognition Letters, 2020, 140: 172.
[13] Wang A X, Chukova S S, Nguyen B P. Applied Soft Computing, 2023, 149: 110895.
[14] Shen L, Yin Q. Knowledge-Based Systems, 2020, 193: 105420.
|
| [1] |
WU Bin1, XIE Chen-ao2, CHEN Yong2, WU Xiao-hong2, JIA Hong-wen1. Discrimination of Chuzhou Chrysanthemum Tea Grades Using Noise
Discriminant C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2202-2207. |
| [2] |
WANG Shu-tao1, WAN Jin-cong1*, LIU Shi-yu2, ZHANG Jin-qing1, WANG Yu-tian1. Qualitative Modeling Method of Mango Species in Near Infrared Based on Attention Mechanism Residual Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2262-2267. |
| [3] |
LIAN Bing-rui1, 2, LI Ya-hao1, 3, ZHANG Jing2, LI Chang-qing4, YANG Xiao-dong5*, WANG Ji-qing2, ZOU Guo-yuan1, Thompson Rodney6, YANG Jun-gang1*. Prediction of Total Nitrogen Content of Lettuce Based on UAV
Multi-Spectral Vegetation Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2318-2325. |
| [4] |
XIAO Nan1, LI Han-lin1, WENG Ding-kang1, HU Dong1, SUN Tong1*, XIONG Yong-sen2. Rapid Identification of Apple Moldy Core Disease by Near Infrared
Spectroscopy With Information Fusion of Different Illumination
Patterns[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2388-2394. |
| [5] |
LI Zhen-yu1, ZHAO Peng1, 2*, WANG Cheng-kun3. Tree Class Recognition in Open Set Based on an Improved Fuzzy
Reasoning Classifier[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1868-1876. |
| [6] |
XIAO Huai-chun1, LIU Yang1, WEI Bing-xue1, GAO Jia-rong1, LIU Yan-de2, XIAO Hui1. Identification of Visible and Short Wave Near Infrared Spectra of
Super-Enriched Plants in Uranium Ore Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1813-1819. |
| [7] |
HUANG Hua1, LIU Ya2, MA Yi-hang1, XIANG Si-han1, HE Jia-ning1, WANG Shi-ting1, GUO Jun-xian3*. Prediction of Soluble Solid Contents in Apples Using Vis-NIRS and
Functional Linear Regression Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1905-1912. |
| [8] |
CUI Hao-fan1, LIU Hong-zhi1, GUO Qin1*, GU Feng-ying1, ZHANG Yu2, WANG Qiang1*. Establishment of High-Throughput Model of Peanut Protein Components and Subunits by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1982-1987. |
| [9] |
YANG Sen1, WANG Zhen-min1*, SONG Wen-long1, XING Jian1, DAI Jing-min2. Optimization of Polished Rice Varieties Discrimination Based on
Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1988-1992. |
| [10] |
NIU Xiao-ying1, 2, 3, MU Xiao-qing1, 2, 3, SUN Jie1, 2, 3, ZHAO Zhi-lei1, 2, 3*, ZHANG Chun-jiang4. Qualitative and Quantitative Analyses of Cooked Donkey Meat
Adulteration Based on NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1993-2001. |
| [11] |
NI Jin1, SUO Li-min1*, LIU Hai-long1, ZHAO Rui2. Identification of Corn Varieties Based on Northern Goshawk Optimization Kernel Based Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1584-1590. |
| [12] |
YU Shui1, HUAN Ke-wei1*, LIU Xiao-xi2, WANG Lei1. Quantitative Analysis Modeling of Near Infrared Spectroscopy With
Parallel Convolution Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1627-1635. |
| [13] |
WEI Zi-chao1, 2, LU Miao1, 2, LEI Wen-ye1, 2, WANG Hao-yu1, 2, WEI Zi-yuan1, 2, GAO Pan1, 2, WANG Dong1, 2, CHEN Xu1, 2*, HU Jin1, 2*. A Nondestructive Method Combined Chlorophyll Fluorescence With Visible-NIR Spectroscopy for Detecting the Severity of Heat Stress on Tomato Seedlings[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1613-1619. |
| [14] |
GE Qing, LIU Jin*, HAN Tong-shuai, LIU Wen-bo, LIU Rong, XU Ke-xin. Influence of Medium's Optical Properties on Glucose Detection
Sensitivity in Tissue Phantoms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1262-1268. |
| [15] |
LIU Yu-ming1, 2, 3, WANG Qiao-hua1, 2, 3*, CHEN Yuan-zhe1, LIU Cheng-kang1, FAN Wei1, ZHU Zhi-hui1, LIU Shi-wei1. Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical
Indicator of Pork Meat[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1346-1353. |
|
|
|
|
