Lingwu Changzao (Ziziphus. jujube Mill) is a Ningxia local variety, it has a good table quality despite its short postharvest life. The respiration rate, the weight change of single fruit during growth development a...Lingwu Changzao (Ziziphus. jujube Mill) is a Ningxia local variety, it has a good table quality despite its short postharvest life. The respiration rate, the weight change of single fruit during growth development and the losing water of postharvest of fruit were studied. The results showed that the curve of fruit growth development was a double sigmoid characteristic. When the surface colour of fruit changed form jade-green to alizarin crimson, the respiration rate tended to fall during a entire growth development of fruit, which showed a fluctuation phenomenon. The respiration rate descended when the surface color changed from coloring to baby red, but after a crimson stage the respiration rate recurred to its original downtrend. With increasing of single fruit weight, phenomenon of respiration climacteric has not happened and the respiration rate curve showed a concavity characteristic at some stages. As a result, there was not a phenomenon of respiration climacteric with a double sigmoid growth development curve characteristic of fruit.展开更多
利用可见近红外(Vis-NIR)高光谱成像技术对完好和损伤等级灵武长枣进行快速识别检测。采用定量损伤装置得到损伤Ⅰ,Ⅱ,Ⅲ,Ⅳ和Ⅴ级的灵武长枣,借助高光谱成像系统采集完好长枣和损伤长枣样本高光谱图像。提取感兴趣区域(region of inter...利用可见近红外(Vis-NIR)高光谱成像技术对完好和损伤等级灵武长枣进行快速识别检测。采用定量损伤装置得到损伤Ⅰ,Ⅱ,Ⅲ,Ⅳ和Ⅴ级的灵武长枣,借助高光谱成像系统采集完好长枣和损伤长枣样本高光谱图像。提取感兴趣区域(region of interest,ROI)并计算样本平均光谱值。利用光谱-理化值共生距离算法(SPXY)将420个长枣样本按3∶1的比例划分校正集315个和预测集105个。灵武长枣原始光谱建立偏最小二乘判别分析(PLS-DA)分类模型,得到校正集和预测集准确率分别为72.70%和86.67%;灵武长枣原始光谱数据采用移动平均(MA)、卷积平滑(SG)、多元散射校正(MSC)、正交信号修正(OSC)、基线校准(baseline)和去趋势(de-trending)等方法进行光谱预处理并建立PLS-DA分类判别模型。通过分析比较,得到MSC-PLS-DA为最优分类判别模型,校正集准确率为76.19%,预测集准确率为86.67%,其中校正集比原始光谱建模准确率提高了3.49%,预测集准确率较原始光谱建模结果未提高;为了提高建模效果,对灵武长枣原始光谱和预处理后的光谱分别采用连续投影算法(SPA)、无信息变量消除(UVE)、竞争性自适应加权抽样(CARS)和区间变量迭代空间收缩法(iVISSA)等算法提取特征波长,建立PLS-DA分类判别模型,结果表明,MSC-CARS-PLS-DA为最优模型组合,校正集准确率为77.14%,预测集准确率为89.52%,建模准确率较原始光谱建模准确率分别提高了4.44%和2.85%。结果表明,Vis-NIR高光谱成像技术结合MSC-CARS-PLS-DA模型可实现灵武长枣损伤等级的快速识别。展开更多
文摘Lingwu Changzao (Ziziphus. jujube Mill) is a Ningxia local variety, it has a good table quality despite its short postharvest life. The respiration rate, the weight change of single fruit during growth development and the losing water of postharvest of fruit were studied. The results showed that the curve of fruit growth development was a double sigmoid characteristic. When the surface colour of fruit changed form jade-green to alizarin crimson, the respiration rate tended to fall during a entire growth development of fruit, which showed a fluctuation phenomenon. The respiration rate descended when the surface color changed from coloring to baby red, but after a crimson stage the respiration rate recurred to its original downtrend. With increasing of single fruit weight, phenomenon of respiration climacteric has not happened and the respiration rate curve showed a concavity characteristic at some stages. As a result, there was not a phenomenon of respiration climacteric with a double sigmoid growth development curve characteristic of fruit.
文摘利用可见近红外(Vis-NIR)高光谱成像技术对完好和损伤等级灵武长枣进行快速识别检测。采用定量损伤装置得到损伤Ⅰ,Ⅱ,Ⅲ,Ⅳ和Ⅴ级的灵武长枣,借助高光谱成像系统采集完好长枣和损伤长枣样本高光谱图像。提取感兴趣区域(region of interest,ROI)并计算样本平均光谱值。利用光谱-理化值共生距离算法(SPXY)将420个长枣样本按3∶1的比例划分校正集315个和预测集105个。灵武长枣原始光谱建立偏最小二乘判别分析(PLS-DA)分类模型,得到校正集和预测集准确率分别为72.70%和86.67%;灵武长枣原始光谱数据采用移动平均(MA)、卷积平滑(SG)、多元散射校正(MSC)、正交信号修正(OSC)、基线校准(baseline)和去趋势(de-trending)等方法进行光谱预处理并建立PLS-DA分类判别模型。通过分析比较,得到MSC-PLS-DA为最优分类判别模型,校正集准确率为76.19%,预测集准确率为86.67%,其中校正集比原始光谱建模准确率提高了3.49%,预测集准确率较原始光谱建模结果未提高;为了提高建模效果,对灵武长枣原始光谱和预处理后的光谱分别采用连续投影算法(SPA)、无信息变量消除(UVE)、竞争性自适应加权抽样(CARS)和区间变量迭代空间收缩法(iVISSA)等算法提取特征波长,建立PLS-DA分类判别模型,结果表明,MSC-CARS-PLS-DA为最优模型组合,校正集准确率为77.14%,预测集准确率为89.52%,建模准确率较原始光谱建模准确率分别提高了4.44%和2.85%。结果表明,Vis-NIR高光谱成像技术结合MSC-CARS-PLS-DA模型可实现灵武长枣损伤等级的快速识别。