肉品无损检测光学传感器设计与试验
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国家重点研发计划项目(2017YFC1600800)


Design and Test of Optical Sensor for Meat Non-destructive Detection
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    摘要:

    畜肉品质光谱检测过程中,不同样品之间的厚度差异导致肉品表面到光纤探头的检测距离存在差异,对预测结果影响较大。针对这一问题,设计了一种用于肉品无损检测的光学传感器,并间隔玻璃从下至上对畜肉品质进行检测,消除了样品厚度对检测距离的影响,并分析了光谱曲线随检测距离变化的变化规律。为探究所设计方案的可行性,搭建了试验平台,包括光谱仪、距离调节机构、光源、石英玻璃、光学传感器和计算机,其中石英玻璃可透过220~2500nm波长范围的光而无吸收,光学传感器可以帮助采集更多的肉品漫反射光。选择了18个猪肉样品贮藏在4℃环境中,并在不同的冷藏时间取出进行光谱采集(400~1100nm),采用不同的检测距离(8~22mm,间隔2mm),最后测量样品的挥发性盐基氮(TVBN)含量。在获得样品光谱数据后,分别用1阶导数、多元散射校正(MSC)、标准正态变换(SNV)和1阶导数+SNV等方法进行预处理,并建立猪肉的TVBN含量的偏最小二乘回归(PLSR)预测模型。结果表明:当检测距离为16mm,采用1阶导数+ SNV预处理时,建立的TVBN含量预测模型效果最好,校正集相关系数和均方根误差分别为0.98和0.92mg/(100g),预测集相关系数和预测均方根误差分别为0.97和1.56mg/(100g)。因此,利用所设计光学传感器对猪肉新鲜度进行检测是可行的。

    Abstract:

    In the spectral detection of livestock products quality and safe, the thickness difference between different samples leads to the difference in the distance between the meat surface and the optical fiber probe, which has a great influence on the prediction results. Aiming at this problem, an optical sensor for nondestructive detection of meat was designed, and the quality of pork (Longissimus dorsi muscle) was detected from the bottom to the top through the glass, which eliminated the influence of sample thickness on the detection distance, and the variation law of spectral curve with the change of detection distance was also analyzed. In order to explore the feasibility of the design scheme, a test platform was set up, including spectral acquisition unit, distance control unit, light source, glass, optical sensor and computer. The quartz glass can pass through the wavelength range of 220~2500nm without absorption. And optical sensors can help collect more diffuse light from the meat. Eighteen pork samples were stored at 4℃ and taken out at different refrigeration times for spectral collection (400~1100nm) with different detecting distances in the range of 8~22mm at approximately 2mm intervals. Finally, the TVBN content of the samples were measured. After obtaining the spectral data of the samples, the methods such as first derivative, multiplicative scattering correction (MSC), standard normal variate (SNV) and first derivative plus SNV were used to pretreat the spectral data of the samples, and the partial least squares regression (PLSR) prediction model of the content of pork’s TVBN was established. The results showed that when the detection distance was 16mm, the prediction model of pork’s TVBN using the 1st DER plus SNV preprocessing method had the best prediction effect. The correlation coefficient and RMS error of correction set were 0.98 and 0.92mg/(100g), respectively, and the correlation coefficient and RMS error of prediction set were 0.97 and 1.56mg/(100g), respectively. Therefore, it was feasible to detect pork freshness with the designed optical sensor.

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郭庆辉,彭彦昆,李永玉,田文健,乔鑫.肉品无损检测光学传感器设计与试验[J].农业机械学报,2020,51(s2):484-490. GUO Qinghui, PENG Yankun, LI Yongyu, TIAN Wenjian, QIAO Xin. Design and Test of Optical Sensor for Meat Non-destructive Detection[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(s2):484-490.

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  • 收稿日期:2020-08-10
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  • 在线发布日期: 2020-12-10
  • 出版日期: 2020-12-10