魏文松,彭彦昆.手持式生鲜肉品质参数无损检测装置研究[J].农业机械学报,2016,47(s1):324-331.
Wei Wensong,Peng Yankun.Research on Hand-held Device for Nondestructive Detection of Meat Quality Parameters[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(s1):324-331.
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手持式生鲜肉品质参数无损检测装置研究   [下载全文]
Research on Hand-held Device for Nondestructive Detection of Meat Quality Parameters   [Download Pdf][in English]
投稿时间:2016-07-20  
DOI:10.6041/j.issn.1000-1298.2016.S0.050
中文关键词:  生鲜肉  品质参数  无损检测  设计
基金项目:国家重点研发计划项目(2016YFD0401205)
作者单位
魏文松 中国农业大学
国家农产品加工技术装备研发分中心 
彭彦昆 中国农业大学
国家农产品加工技术装备研发分中心 
中文摘要:开发了一种手持式生鲜肉品质无损检测装置,并对装置进行了功能测试。硬件主要包括多光谱光源阵列(中心波长为470、515、545、 575、610、760、810、910nm)及探头模块、恒流源驱动模块、多光谱数据采集与处理模块、无线传输模块以及显示终端模块等。设计了基于Android的生鲜肉检测APP应用软件,实现了对装置的无线控制功能。整个装置体积为175mm×115mm×25mm,质量约为0.45kg。利用43个猪肉样品进行试验验证,按照37∶1比例将样品分为校正集与预测集,分别利用多元线性回归、偏最小二乘方法以及多元线性结合逐步回归建模方法对数据进行处理并比较结果,结果表明利用多元线性结合逐步回归算法建立的模型结果较好,其中颜色中L*、a*、b*的预测集相关系数分别为0.9471、0.8504、0.8563,挥发性盐基氮(TVB-N)含量预测集相关系数为0.8027。最后利用不同时间段的12块肉样进行验证模型,其中每组颜色指标中L*、a*、b*的真实值与预测值的预测相关系数均大于0.83,预测集标准偏差均小于等于162,挥发性盐基氮含量的真实值与预测值的预测相关系数均为0.80左右,预测偏差及标准偏差均小于等于4.04。从2组的验证结果来看,利用该设备对生鲜肉品质参数进行检测是可行的,模型具有一定的稳定性,且该设备具有轻便、体积小、质量轻、价格低廉等特点,能够为未来生鲜肉品质的便携式无损检测仪器的进一步研发提供参考。
Wei Wensong  Peng Yankun
China Agricultural University; National R&D Center for Agro-processing Equipment and China Agricultural University; National R&D Center for Agro-processing Equipment
Key Words:meat  quality parameters  nondestructive detection  design
Abstract:A hand held and portable device for detecting meat quality parameters was designed, which was composed of hardware components including detecting probe module with multispectral light sources array, constant flow driver module, multispectral data acquisition and processing module, wireless transmission and display module, as well as application software based on Android system on terminal display platform. In addition, multispectral array contained 64 LED light sources which related to the quality parameters and it 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, 910nm respectively, forming a diameter of 5cm brightness uniformity detection range for obtaining spectral data of meat samples. The dimension of this device was 175mm(L)×115mm(W)×25mm(H) and the weight was about 0.45kg. In addition, cost of device was less 2500 RMB. When using multispectral light array to detect samples, some lights were absorbed by samples and another part was reflected from samples which was called diffuse light. After obtaining diffuse light from samples, a silicon photodiode detector with spectral response range of 400~1100nm was installed to receive diffuse light from pork meat in detection zone, and then the signal from silicon photodiode detector were amplified and processed by amplifier chip and A/D converter chip, then different wavelengths spectral data were calculated for establishing meat quality prediction model. For verifying this device, 43 pork samples with different quality attributes were collected for data acquisition and three algorithms including multiple linear regression (MLR), partial least square regression (PLSR) and multiple linear regression (MLR) mathematical with stepwise method were employed to establish pork prediction models. The parameters included color parameters (L*, a*, b*) and total volatile basic nitrogen (TVB-N) content in meat respectively. The 43 samples were divided into calibration and validation sets according to the proportion of 3〖DK〗∶1 to achieve more reasonable prediction results. The results showed that the multiple linear regression (MLR) mathematical with stepwise method had the best prediction model, the correlation coefficients of prediction (Rp) for pork color (L*, a*, b*) and TVB-N content were 0.9471, 0.8504, 0.8563, 0.8027, respectively. At last, the same number of 12 pork samples from two groups of A, B at different time periods were used to verify the feasibility of this model, and the coefficient between predicted values of pork color (L*, a*, b*) and TVB-N content and their true values in each group were more than 0.80. In addition, coefficient of variation (CV) of verifying results in two groups was less than 1.1%, which indicated that this model of this device had certain stability to some extent. This experiment demonstrated that it has the potential in nondestructive detection for assessing meat freshness using this device, which not only meet the requirements of nondestructive testing but also can be widely applied for assessing meat quality in future.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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