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Nondestructive detection of meat freshness using lightweight device based a ring light source structure
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2016 ASABE Annual International Meeting 162461143.(doi:10.13031/aim.20162461143)
Authors: Wensong Wei, Yankun Peng, Peng Li, Kuanglin Chao, Jianwei Qin
Keywords: Mathematical modeling methods, Pork freshness, Partial Least Squares Regression (PLSR),Ring light source detection, Total volatile basic nitrogen.
Abstract. Meat freshness is directly related to the health of consumers, and total volatile basic nitrogen (TVB-N) content is an important reference index for evaluating pork freshness. This paper attempted to measure TVB-N content for assessing pork freshness using a new self-developed portable and low cost detection device designed by ourselves basing on near infrared technique. The front-end part of this device was an integrated detection component containing ring light source which included 64 Light Emitting Diode (LED) light sources and the whole bottom detection zone of light zone was about 5cm in diameter circle. In addition, 64 LED light sources were divided into eight groups and each group incorporated eight different LED light sources of which the center wavelengths were 475, 515, 525, 575, 610, 760, 810, 910nm respectively, which formed a diameter of 5cm brightness uniformity detection range for obtaining spectral data of meat samples. At the top of the detection range, a silicon photodiode detector with spectral response range of 400-1100nm was installed to receive diffuse light from pork meat surface in detection zone. For verifying this device, 25 pork samples with different freshness attributes were collected for data acquisition. Multiple Linear Regression (MLR) mathematical with stepwise method, Partial least squares regression (PLSR) with interpolation variables method and PLSR with raw spectral variables method were employed to establish pork TVB-N content prediction model respectively. The 25 samples were divided into calibration and validation sets according to the proportion of 3:1 to achieve more reasonable prediction results. Comparing the results of three models, PLSR with raw 8 spectral variables had the best result, and coefficient of determination in calibration set (Rc),Coefficient of determination in prediction set (Rp) were 0.9179 and 0.9126 respectively. At last, 15 samples were used to verify the feasibility of this model, and the coefficient between predicted value of TVB-N content and true value was 0.8906, better than 0.8874 using PLSR with interpolation variables and 0.8675 using MLR with stepwise. This experiment demonstrates that it has the potential in nondestructive detection for assessing meat freshness using this device with ring LED arrays, which not only meet the requirements of nondestructive testing but also can be widely applied for assessing meat quality in future.
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