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Nondestructive determination of the total mold colony count in green tea by hyperspectral imaging technology

机译:高光谱成像技术无损测定绿茶中的总模殖民币计数

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摘要

In the process of tea production and storage, mold is constantly multiplying due to improper production methods and environmental conditions. To realize the nondestructive detection of the total mold colony count in green tea, an accurate and rapid method based on visible-near-infrared (431-962 nm) hyperspectral image was proposed. Firstly, the spectral data extracted from hyperspectral images was preprocessed and partial least squares regression model based on different preprocessing methods was established to determine the best preprocessing method. Then, competitive adaptive reweighted sampling (CARS) and variable combination population analysis were used to select the characteristic wavelengths and support vector regression (SVR) was introduced to establish quantitative detection model. Because the parameter setting of SVR directly affects the effect of the model, a combination of genetic algorithm (GA) and particle swarm optimization (PSO) was adopted to optimize the parametersc(penalty factor) andg(kernel function parameter). The results showed that based on the wavelength selected by CARS, the SVR model optimized by GA-PSO (CARS-GA-PSO-SVR) achieved accuracy withRP2of 0.9577 and root mean square error of prediction set of 0.1140 lg(CFU/g). Therefore, hyperspectral imaging technology can realize the nondestructive determination of the total mold colony count in green tea. Practical applications Some molds, such as aspergillus and penicillium, are the main factors of tea mildew. These molds can produce mycotoxins such as aflatoxin and citreoviridin, which can not only cause damage to the tea quality, but also threaten the health of tea drinkers. In this paper, the total mold colony count of green tea was studied based on hyperspectral imaging technology. The experiment result indicated that the use of hyperspectral imaging technology can achieve accurate, nondestructive, and rapid detection of the total mold colony count in green tea. This research provides an effective solution to the quantitative detection of the total mold colony count in green tea.
机译:在茶叶生产和储存过程中,由于生产方法和环境条件不当,模具不断乘以。为了实现绿茶中总模群计数的非破坏性检测,提出了基于可见近红外(431-962nm)高光谱图像的准确和快速的方法。首先,从高光谱图像中提取的光谱数据是预处理的,并且建立了基于不同预处理方法的部分最小二乘回归模型以确定最佳的预处理方法。然后,使用竞争性自适应重新重量的采样(汽车)和可变组合群体分析来选择特征波长并引入支持向量回归(SVR)以建立定量检测模型。因为SVR的参数设置直接影响模型的效果,所采用遗传算法(GA)和粒子群优化(PSO)的组合来优化参数(惩罚因子)和G(内核函数参数)。结果表明,基于由汽车选择的波长,通过Ga-PSO(CARS-GA-PSO-SVR)优化的SVR模型实现了0.9577的精度0.9577和预测集的0.1140Lg(CFU / G)的均方根误差。因此,高光谱成像技术可以实现绿茶总模群的非破坏性测定。实际应用一些模具,如曲霉和青霉素,是茶叶霉菌的主要因素。这些模具可以生产霉菌毒素,如黄曲霉毒素和柠檬酰胺,这不仅可以造成茶饮料的损害,而且威胁到茶饮的健康。本文基于高光谱成像技术研究了绿茶的总模群计数。实验结果表明,高光谱成像技术的使用可以实现精确,无损,快速地检测绿茶中的总模群计数。本研究提供了有效的解决方案,用于在绿茶中定量检测霉菌菌落数量的定量检测。

著录项

  • 来源
    《Journal of food process engineering》 |2020年第12期|e13570.1-e13570.9|共9页
  • 作者单位

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

    Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 23:33:31

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