枇杷糖度无损检测及可视化研究

doi:10.13733/j.jcam.issn.2095-5553.2024.02.023

摘要/Abstract

摘要： 糖度是评价枇杷内部品质的重要指标。采集枇杷高光谱图像并提取不同感兴趣区域的平均光谱,分析光谱提取区域对枇杷糖度预测精度的影响规律。使用标准正态变换对原始光谱作预处理,分别利用竞争性自适应重加权算法和连续投影算法(Successive Projection Algorithm,SPA)筛选特征光谱,为枇杷糖度建立多元线性回归(Multi Linear Regression,MLR)预测模型。最后采用伪彩色技术实现枇杷糖度含量分布的可视化。结果表明:以整个样本为感兴趣区域,经连续投影算法筛选特征光谱建立的多元线性回归模型具有最好的预测性能(决定系数Rp2=0.822,均方根误差RMSEp=0.435,剩余预测偏差RPD=2.407),基于SPA-MLR模型计算枇杷每个像素点的糖度,生成可视化分布图。表明采用高光谱成像技术可实现枇杷糖度的快速无损检测及可视化研究。

关键词: 枇杷, 高光谱成像, 糖度, 可视化, 无损检测

Abstract: The sugar content is an important index to evaluate the internal quality of loquat. The hyperspectral images and the average spectra in different regions of interest of loquat were collected. And the effect of the spectral extraction regions on the prediction accuracy of sugar content of loquat was analyzed. The original spectra was preprocessed by standard normal variation.The competitive adaptive reweighting algorithm and the successive projection algorithm (SPA) were used to select characteristic spectra to establish multi linear regression (MLR) prediction model for predicting the sugar content of loquat. The visualization of the sugar content of loquat was realized by pseudo-color technology. The results indicated that SPA-MLR model based on the spectra in entire sample regions had the best prediction performance (Rp2=0.822, RMSEp=0.435, RPD=2.407). Visualization maps for sugar content were generated by calculating the spectral response of each pixel on loquat samples by SPA-MLR. It is indicated that hyperspectral imaging technology can realize rapid nondestructive detection and visualization of loquat sugar content.

Key words: loquat, hyperspectral imaging, sugar content, visualization, nondestructive detection

中图分类号:

O657.3: S663.9

冯树南, , 谭涛, , 尚静, , 温青纯, 孟庆龙, . 枇杷糖度无损检测及可视化研究[J]. 中国农机化学报, 2024, 45(2): 151-156.

Feng Shunan, , Tan Tao, , Shang Jing, , Wen Qingchun, Meng Qinglong, . Non-destructive detection and visualization of sugar content in loquat[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(2): 151-156.

参考文献

［1］　森姚. 开阳枇杷: 铜铃之中有黄金［J］. 消费指南, 2015(9): 26-27.
［2］　Zhao Yiying, Zhang Chu, Zhu Susu, et al. Shape induced reflectance correction for non-destructive determination and visualization of soluble solids content in winter jujubes using hyperspectral imaging in two different spectral ranges ［J］. Postharvest Biology and Technology, 2020, 161: 111080.
［3］　刘燕德, 周延睿. 便携式近红外水果内部品质检测仪原理及应用进展［J］. 中国农机化学报, 2013, 34(4): 204-209.
Liu Yande, Zhou Yanrui. Principle and application of nondestructive evaluation for fruit internal quality using portable near-infrared spectrometry ［J］. Journal of Chinese Agricultural Mechanization, 2013, 34(4): 204-209.
［4］　Magwaza L S, Opara U L, Nieuwoudt H, et al. NIR spectroscopy applications for internal and external quality analysis of citrus fruit: A review ［J］. Food and Bioprocess Technology, 2012, 5(2): 425-444.
［5］　张伏, 张朝臣, 陈自均, 等. 光谱检测技术在种子质量检测中的应用［J］. 中国农机化学报, 2021, 42(2): 109-114.
Zhang Fu, Zhang Chaochen, Chen Zijun, et al. Application of spectral testing technology in seed quality testing ［J］. Journal of Chinese Agricultural Mechanization, 2021, 42(2): 109-114.
［6］　刘燕德, 姜小刚, 周衍华, 等. 基于高光谱成像技术对脐橙叶片的叶绿素、水分和氮素定量分析［J］. 中国农机化学报, 2016, 37(3): 218-224.
Liu Yande, Jiang Xiaogang, Zhou Yanhua, et al. Quantitative analysis of chlorophyll, water and nitrogen for navel orange leaf based on hyper-spectral imaging technology ［J］. Journal of Chinese Agricultural Mechanization, 2016, 37(3): 218-224.
［7］　马本学, 肖文东, 祁想想, 等. 基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究［J］. 光谱学与光谱分析, 2012, 32(11): 3093-3097.
Ma Benxue, Xiao Wendong, Qi Xiangxiang, et al. Nondestructive measurement of sugar content of Hami melon based on diffuse reflectance hyperspectral imaging technique ［J］. Spectroscopy and Spectral Analysis, 2012, 32(11): 3093-3097.
［8］　连雅茹. 基于高光谱成像技术的番茄内部品质检测研究［D］. 杨凌: 西北农林科技大学, 2020.
Lian Yaru. Detecting tomato internal quality based on hyperspectral imaging technology ［D］. Yangling: Northwest A & F University, 2020.
［9］　Zhang Dongyan, Xu Yunfei, Huang Wenqian, et al. Nondestructive measurement of soluble solids content in apple using near infrared hyperspectra imaging coupled with wavelength selection algorithm ［J］. Infrared Physics & Technology, 2019, 98: 297-304.
［10］　许丽佳, 陈铭, 王玉超, 等. 高光谱成像的猕猴桃糖度无损检测方法［J］. 光谱学与光谱分析, 2021, 41(7): 2188-2195.
Xu Lijia, Chen Ming, Wang Yuchao, et al. Study on non-destructive detection method of kiwifruit sugar content based on hyperspectral imaging technology ［J］. Spectroscopy and Spectral Analysis, 2021, 41(7): 2188-2195.
［11］　Zhang Hailiang, Zhan Baishao, Pan Fan, et al. Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models ［J］. Postharvest Biology and Technology, 2020, 163: 111148.
［12］　Gao Sheng, Xu Jianhua. Hyperspectral image information fusion-based detection of soluble solids content in red globe grapes ［J］. Computers and Electronics in Agriculture, 2022, 196: 106822.
［13］　霍迎秋, 张晨, 李宇豪, 等. 高光谱图像结合机器学习方法无损检测猕猴桃［J］. 中国农机化学报, 2019, 40(4): 71-77.
Huo Yingqiu, Zhang Chen, Li Yuhao, et al. Nondestructive detection for kiwifruit based on the hyperspectral technology and machine learning ［J］. Journal of Chinese Agricultural Mechanization, 2019, 40(4): 71-77.
［14］　尚静, 黄人帅, 张艳, 等. 高光谱成像结合模式识别无损检测猕猴桃成熟度［J］. 中国农机化学报, 2022, 43(8): 90-95.
Shang Jing, Huang Renshuai, Zhang Yan, et al. Nondestructive detection for maturity of kiwifruit based on hyperspectral imaging and pattern recognition ［J］. Journal of Chinese Agricultural Mechanization, 2022, 43(8): 90-95.
［15］　董朋欣, 董安国, 李楚婷, 等. 基于全卷积网络和自编码的高光谱图像分类［J］. 计算机工程与应用, 2022, 58(5): 256-263.
Dong Pengxin, Dong Anguo, Li Chuting, et al. Hyperspectral image classification based on fully convolutional network and auto-encoder ［J］. Computer Engineering and Applications, 2022, 58(5): 256-263.
［16］　周扬, 戴曙光, 吕进, 等. 光谱预处理对近红外光谱快速检测黄酒酒精度的影响［J］. 光电工程, 2011, 38(4): 54-58.
Zhou Yang, Dai Shuguang, Lü Jin, et al. Effect of spectral pretreatment on near infrared spectroscopy for rapid detection of wine alcohol ［J］. Opto-Electronic Engineering, 2011, 38(4): 54-58.
［17］　Sun Jun, Zhou Xin, Hu Yongguang, et al. Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging ［J］. Computers and Electronics in Agriculture, 2019, 160: 153-159.
［18］　王晓明, 章海亮, 罗微, 等. 近红外光谱检测梨果硬度研究［J］. 中国农机化学报, 2015, 36(6): 120-123, 142.
Wang Xiaoming, Zhang Hailiang, Luo Wei, et al. Study on pear firmness detection by using near infrared reflectance spectroscopy based on CARS ［J］. Journal of Chinese Agricultural Mechanization, 2015, 36(6): 120-123, 142.
［19］　蒋柏春, 李德仑, 韦克苏, 等. 基于高光谱的烤烟叶绿素含量估算模型研究［J］. 中国农机化学报, 2022, 43(3): 104-110.
Jiang Baichun, Li Delun, Wei Kesu, et al. Estimation model of chlorophyll content of flue-cured tobacco based on hyperspectral ［J］. Journal of Chinese Agricultural Mechanization, 2022, 43(3): 104-110.
［20］　高文强, 肖志云. 基于Atrous-CDAE-1DCNN的紫丁香高光谱数据的叶绿素含量反演［J］. 中国农机化学报, 2022, 43(7): 158-166.
Gao Wenqiang, Xiao Zhiyun. Inversion of chlorophyll content of lilac hyperspectral data based on Atrous-CDAE-1DCNN ［J］. Journal of Chinese Agricultural Mechanization, 2022, 43(7): 158-166.
［21］　Jie Dengfei, Xie Lijuan, Fu Xiaping, et al. Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique ［J］. Journal of Food Engineering, 2013, 118(4): 387-392.
［22］　Wang Bin, He Junlin, Zhang Shujuan, et al. Nondestructive prediction and visualization of total flavonoids content in Cerasus Humilis fruit during storage periods based on hyperspectral imaging technique ［J］. Journal of Food Process Engineering, 2021, 44(10): e13807.
［23］　邵园园, 王永贤, 玄冠涛, 等. 基于高光谱成像的肥城桃品质可视化分析与成熟度检测［J］. 农业机械学报, 2020, 51(8): 344-350.
Shao Yuanyuan, Wang Yongxian, Xuan Guantao, et al. Visual detection of SSC and firmness and maturity prediction for Feicheng peach by using hyperspectral imaging ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(8): 344-350.

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