In traditional tea quality testing, the quality indicators of tea both inside and outside are often difficult to consider at the same time. Recently, relevant researchers proposed for the first time the use of near-infrared spectroscopy and machine vision two sensor information fusion technologies to evaluate new ideas for comprehensive tea quality. This non-destructive testing technology has the advantages of fast detection, objective results, and no damage to the sample.
Dr. Chen Quansheng, of the School of Food and Biological Engineering, Jiangsu University, published in the Journal of Agricultural Engineering, No. 3, 2008, entitled "Multi-information fusion technology based on near-infrared spectroscopy and machine vision to evaluate the quality of tea", for multi-sensor information fusion. The technology provides a theoretical basis for the rapid and accurate evaluation of the comprehensive quality of tea.
According to reports, the indicators for judging the quality of tea are various, including external indicators such as color and appearance, as well as internal quality indicators such as taste and aroma. A single detection method is often not fully described and only one of them can be described. For example, near-infrared spectroscopy can be a good representation of the internal quality of tea, but in the description of tea's external quality characteristics, it often appears powerless; the other hand, machine vision technology can detect the external quality of tea, but can not be obtained to reflect the internal tea Effective information of quality. The different focus of the acquired information will inevitably affect the accuracy and stability of the test results.
Multi-sensor information fusion is the use of multiple sensing information resources to obtain a large amount of information describing different quality characteristics of the same object. Analyzing, synthesizing and balancing these information based on certain criteria will help improve the accuracy and accuracy of the assessment. Compared with the single detection method, it has the advantages of large information volume, good fault tolerance and similar to human cognitive process.
For the first time, the researchers proposed a new idea for the detection of tea quality using two kinds of sensing information fusion technology: near infrared spectroscopy and machine vision, and made a preliminary attempt. The experiment took 4 grades of frying green tea as the research object, and obtained the image feature information and spectral feature information, through the principal component analysis, and used the artificial neural network to establish the tea quality evaluation model. The research results show that the method of judging tea quality based on multi-sensor information fusion technology based on near-infrared spectroscopy and machine vision is feasible, and the evaluation accuracy and stability are improved compared to the previous single evaluation method.
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