摘要：
D-limonene is one of the main olefin gas compounds released from citrus and its relative content changes most obviously with the infection time of Bactrocera dorsalis (Hendel). Thus, it is necessary to detect D-limonene content released from citrus, which may lead to detect the presence of early infestation by B. dorsalis in citrus. In this study, a gas-sensing system based on quartz crystal microbalance (QCM) sensors coated with ethyl cellulose (EC) was developed to detect D-limonene aromas emanating from Australian citrus. There was a linear relationship between the frequency shift and D-limonene concentrations ranging from 60 mg m(-3) to 6000 mg m(-3) with a determination coefficient (R-2) of 0.9899, and the limit of detection for D-limonene in pure gas was 300 mg m(-3). It has also been observed that the QCM sensor has better selectivity towards D-limonene aroma. Additionally, the sensor was found to be repeatable with an average R value of 96.31 %, and the lifetime of the sensor can be extended at least to one month with an acceptable drift (3.40 %) in their sensing characteristics. Furthermore, it was confirmed that there was high consistency between the QCM sensor's response and the GC-MS for D-limonene aroma. The six VOCs contributing the most to differentiating citrus infested with B. dorsalis were identified. The gas-sensing system based on QCM sensors has potential feasibility for the rapid detection of the presence of B. dorsalis infestations in postharvest citrus.
摘要：
A sweeping electronic nose system (SENS) was self-developed to detect the presence of early infestation by Bactrocera dorsalis (Hendel) in citrus fruits. Principal component analysis (PCA) and linear discriminate analysis (LDA) were applied to analyze citrus fruits that were subjected to different types of treatments (invasion and incubation stage) caused infestation. The results indicated that the SENS could successfully detect the presence of early infestation by B. dorsalis in citrus fruits. The different types of treatments in citrus fruits could be effectively classified by PCA and LDA, respectively. Meanwhile, the specific infestation time of citrus fruits within treatment stage could be satisfactorily identified by LDA model with correct recognition rate of 98.21%. Importantly, an optimized sensor array achieved better performance in classification and discrimination than that of the non-optimized. This study showed the potential feasibility of the electronic nose technology for in-filed detection of postharvest pest infestation citrus fruits under market conditions.
作者机构：
[龚中良; 郑立章; 李立君; 谢洁飞] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College, South China Agricultural University, Guangzhou, 510642, China;School of Science, Central South University of Forestry and Technology, Changsha, 410004, China;[文韬] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College, South China Agricultural University, Guangzhou, 510642, China<&wdkj&>School of Science, Central South University of Forestry and Technology, Changsha, 410004, China<&wdkj&>School of Science, Central South University of Forestry and Technology, Changsha, 410004, China;[马强] School of Science, Central South University of Forestry and Technology, Changsha, 410004, China<&wdkj&>School of Science, Central South University of Forestry and Technology, Changsha, 410004, China
通讯机构：
Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College, South China Agricultural University, Guangzhou, China
作者机构：
[文韬; 郑立章; 龚中良; 李立君; 桑孟祥; 董帅] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha;410004, China;[文韬; 郑立章; 龚中良; 李立君; 桑孟祥; 董帅] 410004, China
通讯机构：
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China
作者机构：
[文韬; 董帅; 龚中良; 李立君; 郑立章; 桑孟祥] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha;410004, China;[文韬; 董帅; 龚中良; 李立君; 郑立章; 桑孟祥] 410004, China
通讯机构：
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China
作者机构：
[郑立章; 龚中良; 董帅; 桑孟祥] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, China;Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, 510642, China;[文韬] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, China<&wdkj&>Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, 510642, China
通讯机构：
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China
作者机构：
[李立君; 赵兵; 文韬; 张仟仟; 刘付] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China;[郭鑫] School of Science, Central South University of Forestry and Technology, Changsha, China;[洪添胜] Division of Citrus Machinery, China Agriculture Research System, Guangzhou, China;[洪添胜; 文韬] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China agricultural University, Guangzhou, China
通讯机构：
Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China agricultural University, Guangzhou, China
关键词：
模型;光谱检测;农业;霉变稻谷;脂肪酸;可见/近红外光谱;特征波段;样本集划分
摘要：
为了实现霉变稻谷脂肪酸含量无损、快速检测,该文研究应用可见/近红外光谱技术检测霉变稻谷的脂肪酸含量。考虑到直接选用霉变稻谷可见/近红外光谱数据构建脂肪酸含量预测模型存在建模费时、预测失准等问题,研究提出了霉变稻谷脂肪酸含量的可见/近红外优化校正模型。研究中通过光谱-理化值共生距离(sample set partitioning based on joint x-y distance, SPXY)算法结合偏最小二乘法初步分析了不同校正集样本预测霉变稻谷脂肪酸含量的差异;利用连续投影算法(SPA)提取了反映霉变稻谷脂肪酸含量变化的特征波段;采用偏最小二乘法(partial least square, PLS)和多元线性回归法(multivariable linear regression, MLR)分别建立了基于特征波段光谱反射值的霉变稻谷脂肪酸含量预测模型,并对比分析了采用SPXY样本集划分的模型预测效果。结果表明:采用SPXY法筛选出的65个校正集样本分布与初始校正集相近,脂肪酸含量变化范围为18.55~127.26 mg,其标准差为32.39;SPA算法最终从256个全光谱波段中优选出9个特征波段,实现了光谱数据的压缩;分别建立的SPXY-SPA-PLSR模型和SPXY-SPA-MLR模型预测霉变稻谷脂肪酸含量相关系数RP为0.922 1和0.915 9,预测均方根误差RMSEP为13.889 3和14.261 0;SPXY筛选校正集所构建模型预测精度与初始校正集所建模型相当,但校正集样本数量减少为初始校正集的41%,运算时长缩短为初始样本集的32%,提高了模型的校正速度。
作者机构：
[文韬; 郑立章; 龚中良; 李立君; 谢洁飞] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha;410004, China;[马强] School of Science, Central South University of Forestry and Technology, Changsha;[文韬; 郑立章; 龚中良; 李立君; 谢洁飞; 马强] 410004, China
通讯机构：
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China
摘要：
为大范围和准确监测果园实蝇的发生,设计了基于物联网的果园实蝇监测系统。该系统由智能捕虫器、监测终端、远程终端及移动终端组成,安装在果园的多个智能捕虫器和监测终端构成星形短程无线通信网,监测终端将收集的实蝇信息通过GSM/GPRS服务发送至远程终端及移动终端。智能捕虫器包括太阳能电池板、支架、捕虫器壳体及安装于壳体内部的光电检测电路、微处理器、短程无线通信模块、锂电池充电电路等功能电路,采用成本较低且稳定性较高的红外光电对管检测进入捕虫器的果园实蝇;监测终端包括微处理器、短程无线通信模块和GSM/GPRS模块。基于μC/OS–II实时操作系统设计了智能捕虫器和监测终端的应用软件。系统验证试验结果表明,智能捕虫器平均工作电流为97 m A,监测终端在GSM/GPRS模块休眠和工作时的电流分别为60 m A和328 m A,2种设备的工作电流消耗均低于各自电池的供电能力,实蝇监测准确率可达94.23%。
作者机构：
[李立君; 赵兵; 文韬; 张仟仟; 刘付] School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China;[郭鑫] School of Science, Central South University of Forestry and Technology, Changsha, China;[洪添胜] Division of Citrus Machinery, China Agriculture Research System, Guangzhou, China;[洪添胜; 文韬] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, Guangzhou, China
通讯机构：
Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, Guangzhou, China
作者机构：
[文韬; 李立君] College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China;[郭鑫] College of Science, Central South University of Forestry and Technology, Changsha, China;[张南峰] Guangzhou Entry-exit Inspection and Quarantine, Guangzhou, China;[洪添胜; 李震] Division of Citrus Machinery, China Agriculture Research System, Guangzhou, China;[李震; 洪添胜; 文韬] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, College of engineering, South China agricultural University, Guangzhou, China
通讯机构：
Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, College of engineering, South China agricultural University, Guangzhou, China
作者机构：
[文韬; 李立君] College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China;[叶智杰; 张彦晖] College of Engineering, Hong Kong University of Science and Technology, Hong Kong;[洪添胜; 李震] Division of Citrus Machinery, China Agriculture Research System, Guangzhou 510642, China;[叶智杰; 李震; 洪添胜; 文韬] Key Laboratory of Key Technology on Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, Guangzhou 510642, China
通讯机构：
Key Laboratory of Key Technology on Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, China
关键词：
监测;害虫防治;试验;植保;橘小实蝇;光电监测
摘要：
为了解决现有的害虫机器监测技术与传统的监测手段结合存在的实时监测难度高、信息处理困难、成本高等问题,该文设计研发一种适用于果园环境的橘小实蝇成虫诱捕监测装置用于监测橘小实蝇成虫虫口密度。该装置外观由遮光罩、进虫口、虫口监测区和储虫瓶构成,信号检测模块包括红外光电耦合传感器匹配电路、电压跟随器电路、差分放大电路和迟滞比较器电路4部分。性能测试结果表明：该诱捕监测装置底部储虫瓶有、无遮光处理时,相应的感应电压均值分别为3.923和3.883 V,差异显著（P〈0.05）,且上述2种方式均能使检测探头输出工作在线性区域;虫口监测通道管壁设计成黑、白、蓝3色,在自然光照条件下,管壁颜色对监测探头感应性能无显著差异性（P=0.606）;监测区域不同区域位置感应输出响应也无显著差异性（P=0.797）,区域位置对监测输出误差影响可以忽略。应用该诱捕监测装置和人工计数方式在橘小实蝇成虫发生高峰期连续5 d 24 h监测成虫虫口密度,结果表明该装置监测相对误差为3%-8%,相比传统的人工计数方式,具有实时、自动化监测的优点,能够满足现有的橘小实蝇成虫长时期数据动态监测的需求,适用于果园橘小实蝇成虫动态监测推广使用。
作者机构：
[文韬; 李立君] School of Mechanical and Electrical Engineering, Center South University of Forestry and Technology, Changsha 410004, China;[叶智杰; 张彦晖] School of Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong;[洪添胜; 李震] Division of Citrus Machinery, China Agriculture Research System, Guangzhou 510642, China;[叶智杰; 李震; 洪添胜; 文韬] Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, Guangzhou 510642, China
通讯机构：
Key Laboratory of Key Technology for South Agricultural Machinery and Equipment, Ministry of Education, Engineering College of South China Agricultural University, China
