...
首页> 外文期刊>Horticulture,Environment,and Biotechnology >Non-destructive analysis of Japanese table grape qualities using near-infrared spectroscopy
【24h】

Non-destructive analysis of Japanese table grape qualities using near-infrared spectroscopy

机译:近红外光谱法的日本表葡萄品质的无损分析

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Near-infrared (NIR) spectroscopy is a useful technique for the non-destructive analysis of fruit quality. The key quality parameters of table grapes (Vitis vinifera) that affect consumer preference are the soluble solids content (SSC), pH, firmness, and seedlessness. This research focused on using NIR spectroscopy for assessing the quality of 'Kyoho' table grapes, as a non-destructive analysis under laboratory and field conditions. NIR spectra for each sample were acquired in the wavelength range of 400-1000 nm, using a visible/NIR spectrometer with fibre optics in the interactance mode. Partial least-square regression was used to calibrate the NIR spectral data with all the measured properties of table grapes. The best prediction model for firmness was the Savitzky-Golay first derivative (SGD1) with a coefficient of determination (Rprediction2) of 0.7427 in the laboratory, and 0.7804 in the field. The Rprediction2values for pH in the laboratory and the field was 0.6276 using multiplicative scatter correction (MSC), and 0.7676 using SGD1, respectively. These values were similar to the Rprediction2values of SSC, which were 0.6926 using MSC, and 0.8052 using the Savitzky-Golay second derivative, respectively. In both analyses the R(2)of the calibration model was between 0.6944 and 0.8877. The partial least-square discriminant analysis was used to classify the percentage of seedlessness, which was 93.10% in the laboratory using SGD1 or MSC, and 79.31% in the field using MSC. Therefore, NIR spectroscopy is an efficient non-destructive technique for rapidly analysing Japanese table grape qualities in laboratory and field settings.
机译:近红外(NIR)光谱是一种用于果实质量的非破坏性分析的有用技术。影响消费者偏好的表葡萄(葡萄vinifera)的关键质量参数是可溶的固体含量(SSC),pH,坚固度和无籽。该研究专注于使用NIR光谱来评估'kyoho'葡萄的质量,作为实验室和现场条件下的非破坏性分析。每个样品的NIR光谱在400-1000nm的波长范围内获得,使用具有在相互作用模式的光纤的可见/ NIR光谱仪。部分最小二乘回归用于校准NIR光谱数据与表葡萄的所有测量性质。坚固性的最佳预测模型是Savitzky-Golay第一种衍生物(SGD1),其在实验室中的测定系数(RpricIction2)为0.7427,域内0.7804。在实验室中pH的Rprediction2Value分别使用乘法散射校正(MSC)和0.7676分别使用SGD1的0.6276。这些值类似于SSC的Rprediction2Value,其使用MSC为0.6926,分别使用Savitzky-Golay第二衍生物0.8052。在分析中,校准模型的R(2)为0.6944和0.8877之间。部分最小二乘判别分析用于对无籽的百分比进行分类,在实验室中使用SGD1或MSC在实验室中为93.10%,使用MSC在该场中的79.31%。因此,NIR光谱是一种有效的非破坏性技术,用于在实验室和现场设置中快速分析日本表葡萄品质。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号