刘旭,吴迪,梁曼,杨蜀秦,张振文,宁纪锋.基于高光谱的酿酒葡萄果皮花色苷含量多元回归分析[J].农业机械学报,2013,44(12):180-186,139.
Liu Xu,Wu Di,Liang Man,Yang Shuqin,Zhang Zhenwen,Ning Jifeng.Multiple Regression Analysis of Anthocyanin Content of Winegrape Skins Using Hyper-spectral Image Technology[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(12):180-186,139.
摘要点击次数: 3998
全文下载次数: 1861
基于高光谱的酿酒葡萄果皮花色苷含量多元回归分析   [下载全文]
Multiple Regression Analysis of Anthocyanin Content of Winegrape Skins Using Hyper-spectral Image Technology   [Download Pdf][in English]
  
DOI:10.6041/j.issn.1000-1298.2013.12.030
中文关键词:  酿酒葡萄  色苷  高光谱图像  偏最小二乘回归  支持向量回归  BP神经网络
基金项目:国家自然科学基金资助项目(61003151)、“十二五”国家科技支撑计划资助项目(2012BAD31B07)、中央高校基本科研业务费专项资金资助项目(QN2011099、QN2013062、QN2013055)和国家葡萄产业技术体系酿酒葡萄栽培岗位子项目(CARS-30-02A)
作者单位
刘旭 西北农林科技大学
陕西省葡萄与葡萄酒工程中心 
吴迪 西北农林科技大学 
梁曼 西北农林科技大学 
杨蜀秦 西北农林科技大学 
张振文 西北农林科技大学 
宁纪锋 西北农林科技大学 
中文摘要:以酿酒葡萄赤霞珠果实为研究对象,利用高光谱成像技术检测葡萄果皮中的花色苷含量。采集60组样本的900~1700nm近红外波段高光谱图像,并用pH示差法测量样本果皮中花色苷含量。选取高光谱图像中葡萄果实区域作为感兴趣区域(ROI),计算其平均光谱,并采用SG平滑、归一化、多元散射校正等预处理方法提高光谱的信噪比。然后采用偏最小二乘回归(PLSR)、支持向量回归(SVR)和BP神经网络算法建立花色苷含量预测模型。研究表明:基于PLSR模型推荐的13个隐含变量建立的BP神经网络模型的预测决定系数和预测均方根误差分别为0.9102和0.3795。
Liu Xu  Wu Di  Liang Man  Yang Shuqin  Zhang Zhenwen  Ning Jifeng
Northwest A&F University;Shaanxi Engineering Research Center for Viti-Viniculture;Northwest A&F University;Northwest A&F University;Northwest A&F University;Northwest A&F University;Northwest A&F University
Key Words:Winegrape  Anthocyanin  Hyperspectral image  Partial least squares regression  Support vector regression  BP neural network
Abstract:This work aimed to determine the anthocyanin content in skin based on hyperspectral imaging technology. The grapes of Cabernet Sauvignon (Vitis vinifera L.) produced in Shaanxi province were used as experimental materials. Hyperspectral images of 60 groups of grape samples were collected by near infrared hyperspectral camera (900~1700nm). After then, the anthocyanin content of skin was detected by pH-differential method. The grape berry regions of hyperspectral images were extracted as region of interest (ROI) in which its average spectrum was calculated. Moreover, different preprocessing methods were used to improve the signal noise ratio (SNR) including Savitzky-Golay smoothing, normalization and multiplicative scatter correction, et al. Prediction model was established for determining anthocyanin content by the partial least squares regression (PLSR), least squares support vector regression (SVR) and BP neural network (BPNN). It was shown that prediction coefficient of determination (P-R2) of BPNN model built by the thirteen latent variables recommended by PLSR model was 0.9102 and the root mean square error of prediction (RMSEP) was 0.3795. 

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器