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首页> 外文期刊>Journal of food engineering >Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging
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Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging

机译：使用高光谱成像技术对牛肉中的主要成分进行无化学物质评估和标测

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摘要

Developing a rapid and non-destructive method for food safety and quality monitoring has become a crucial request from the meat industry. Hyperspectral imaging technique provides extraordinary advantages over the traditional imaging and spectroscopy techniques in food quality evaluation due to the spatial and spectral information that it can offer. In this study, a laboratory-based pushbroom hyperspectral imaging system in reflectance mode was developed in the near infrared (NIR) range (900-1700 nm) for non-invasive determination of the major chemical compositions of beef. Beef samples collected from different breeds were scanned by the system followed by traditional assessment of their chemical composition by using the ordinary wet-chemical methods. The extracted spectral data and the measured quality parameters were modeled by partial least squares regression (PLSR) for predicting water, fat and protein contents yielding a reasonable accuracy with determination coefficients (R_p~2) of 0.89, 0.84 and 0.86 concomitant with standard error of prediction (SEP) of 0.46%, 0.65% and 0.29%, respectively. Some image processing algorithms were developed and the most relevant wavelengths were selected to visualize the predicted chemical constituents in each pixel of the hyperspectral image yielding the spatially distributed visualizations of the sample contents. The results were promising and implied that hyperspectral imaging technique associated with appropriate chemometric multivariate analyses has a great potential for simultaneous assessment of various chemical constituents without using hazardous chemical reagents.
机译：开发一种快速，无损的食品安全和质量监控方法已经成为肉类行业的重要要求。由于高光谱成像技术可以提供空间和光谱信息，因此在食品质量评估中比传统的成像和光谱技术具有非凡的优势。在这项研究中，开发了一种基于实验室的反射模式的推扫式高光谱成像系统，该系统在近红外（NIR）范围（900-1700 nm）中用于无创测定牛肉的主要化学成分。该系统对从不同品种中采集的牛肉样品进行扫描，然后使用常规的湿化学方法对其化学成分进行传统评估。通过偏最小二乘回归（PLSR）对提取的光谱数据和测得的质量参数进行建模，以预测水，脂肪和蛋白质含量，产生合理的准确度，测定系数（R_p〜2）为0.89、0.84和0.86，同时标准误差为预测（SEP）分别为0.46％，0.65％和0.29％。开发了一些图像处理算法，并选择了最相关的波长以可视化高光谱图像每个像素中的预测化学成分，从而在空间上可视化了样品内容。结果令人鼓舞，并暗示与适当的化学计量学多元分析相关的高光谱成像技术具有巨大的潜力，可以在不使用危险化学试剂的情况下同时评估各种化学成分。

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  • 来源
    《Journal of food engineering》 |2013年第2期|235-246|共12页
  • 作者

    Gamal ElMasry; Da-Wen Sun; Paul Allen;

  • 作者单位

    Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture ε Food Science Centre, Belfield, Dublin 4, Ireland;

    Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture ε Food Science Centre, Belfield, Dublin 4, Ireland;

    Ashtown Food Research Centre, Teagasc, Dublin 15, Ireland;

  • 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

    hyperspectral imaging; beef; meat; multivariate analysis; quality; PLS;

    机译：高光谱成像牛肉;肉;多元分析质量;PLS;

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