| 掌上式生鲜猪肉新鲜度无损智能检测分级装置 |
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| 引用本文： | 彭彦昆,邹文龙,李荣娇,左杰文,姚现强,姚现琦,杨德勇.掌上式生鲜猪肉新鲜度无损智能检测分级装置[J].农业工程学报,2023,39(18):262-269. |
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| 作者姓名： | 彭彦昆 邹文龙 李荣娇 左杰文 姚现强 姚现琦 杨德勇 |
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| 作者单位： | 中国农业大学工学院, 北京 100083;国家农产品加工技术装备研发分中心, 北京 100083;临沂新程金锣肉制品集团有限公司, 临沂 276711 |
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| 基金项目： | 国家自然科学基金项目（32172287） |
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| 摘 要： | 为实现储运过程中生鲜猪肉新鲜度实时检测，该研究基于可见/近红外光谱技术开发了掌上式生鲜猪肉新鲜度无损智能检测装置。检测装置以可见\近红外光谱采集单元为核心，搭建了硬件系统，开发了生鲜猪肉新鲜度多指标同时检测和新鲜度分级软件系统。通过研发的检测装置采集了不同部位猪肉的650～1 100 nm波长范围的漫反射光谱，经过标准正态变量变换（standard normal variable transformation，SNV）预处理后，对比连续投影算法（successive projections algorithm, SPA）和竞争性自适应加权抽样算法（competitive adaptive reweighted sampling, CARS）算法优选了猪肉新鲜度特征光谱，分别建立了不同部位猪肉新鲜度指标通用预测模型，并根据挥发性盐基氮（total volatile basic nitrogen，TVB-N）含量和pH值预测值，将猪肉分为新鲜、次新鲜和变质3个等级。试验结果表明，通过 SNV预处理和CARS算法筛选特征波长后建立的PLS预测模型（下文简称“SNV-CARS-PLS”）具有更好的性能， TVB-N含量、pH值、亮度L*、红度a*和黄度b*通用预测模型的预测集相关系数分别为0.942、0.945、0.940、0.933和0.833，预测均方根误差分别为1.131 mg/100 g、0.136、1.706、1.217和0.717。将通用检测模型导入检测装置进行了试验验证，对不同部位猪肉样本试验结果表明，TVB-N含量、pH值、亮度L*、红度a*和黄度b*的预测结果与理化值的均方根误差分别为1.109 mg/100 g、0.134、1.140、1.094和0.636；新鲜度的分级正确率为92.86%；单个样品检测时间约为1 s。该检测装置可满足不同部位猪肉新鲜度多指标现场快速检测和分级的需求，为及时掌握储运过程中生鲜猪肉新鲜度情况、辅助决策储运和销售方案、保障生鲜猪肉品质安全具有重要作用。
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| 关 键 词： | 掌上式 智能分级装置 新鲜度 可见/近红外光谱 实时检测 |
| 收稿时间： | 2023/5/19 0:00:00 |
| 修稿时间： | 2023/9/9 0:00:00 |
A handheld non-destructive intelligent detection and grading device for the freshness of pork |
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| PENG Yankun,ZOU Wenlong,LI Rongjiao,ZUO Jiewen,YAO Xianqiang,YAO Xianqi,YANG Deyong.A handheld non-destructive intelligent detection and grading device for the freshness of pork[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(18):262-269. |
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| Authors: | PENG Yankun ZOU Wenlong LI Rongjiao ZUO Jiewen YAO Xianqiang YAO Xianqi YANG Deyong |
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| Affiliation: | College of Engineering, China Agricultural University, Beijing 100083, China;National R & D Center for Agro-processing Equipment, Beijing 100083, China;Xincheng Jinluo Meat Products Group Co., Linyi 276711, China |
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| Abstract: | China is the world''s largest producer and consumer of fresh pork, but in the production and sales of pork, fresh pork is very prone to spoilage. In order to realize the real-time detection of fresh pork freshness during storage and transportation, this study developed a handheld fresh pork freshness non-destructive intelligent detection and grading device based on visible/near-infrared spectroscopy. The detection device took the visible/near-infrared spectrum acquisition unit as the core, built a hardware system, and developed a software system for simultaneous detection of fresh pork freshness and freshness grading. The detection device mainly consists of a power supply, central controller, display unit, heat dissipation unit, detection light source and spectral sensor. The spectral sensor and detection light source are integrated with a visible/near-infrared spectral acquisition unit by the detection probe. The spectral acquisition unit transmits the diffuse reflectance spectral signal of pork to the central controller for processing and analysis. Through the display unit and control buttons, human-computer interaction operation, real-time control and result display can be achieved. According to the selection of each component and the actual application scenario, the whole machine structure was designed, the size of the whole machine is only 165 mm×75 mm×115 mm, the weight is 745 g, and it can be held by one person and completed the detection with one button. Through the developed device, the diffuse reflection spectrum of pork in the wavelength range of 650-1 100 nm was collected. After the spectra were preprocessed by standard normal variable transformation (SNV), the continuous projection algorithm (SPA) and the competitive adaptive weighted sampling algorithm (CARS) algorithm were compared, and the general prediction model of pork freshness index in different parts was established. According to the predicted value of volatile salt-based nitrogen (TVB-N) content and pH, pork was divided into three grades: fresh, sub-fresh and spoilage. The experimental results show that the prediction model established by SNV-CARS-PLS has better performance. The prediction set correlation coefficients of TVB-N content, pH, L*, a* and b* were 0.942, 0.945, 0.940, 0.933 and 0.833, respectively. And the prediction root mean square errors were 1.131 mg/100 g, 0.136, 1.706, 1.217 and 0.717, respectively. The results of the general detection model were introduced into the device and verified by experiments, and the test results showed that the prediction results of root mean square errors of TVB-N content, pH, L*, a* and b* were 1.109 mg/100 g, 0.134, 1.140, 1.094 and 0.636, respectively. The correct grading rate of freshness was 92.86%. The detection time for a single sample was about 1 s. Research has shown that the detection device can meet the needs of multi-index on-site rapid detection and grading of pork freshness of different parts, with advantages such as low cost, portability, simple operation, and efficiency. It is suitable for various links in the supply chain of pork and providing guidance for production and consumption decision-making. The detection also plays an important role in timely grasping the freshness of fresh pork in the process of storage and transportation, assisting in decision-making storage, transportation and sales plans, and ensuring the quality and safety of fresh pork. |
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| Keywords: | handheld device intelligent classifier freshness visible/near-infrared spectroscopy real-time monitoring |
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