便捷式智能化大米新鲜度检测系统设计与应用  

Design and application of portable intelligent rice freshness detection system

作　　者：邵小康 林颢[1] 王卓 李益兵 陈全胜[1] SHAO Xiaokang;LIN Hao;WANG Zhuo;LI Yibing;CHEN Quansheng(School of Food and Biological Engineering and Jiangsu University,Zhenjiang,Jiangsu 212013,China)

机构地区：[1]江苏大学食品与生物工程学院,江苏镇江212013

出　　处：《食品与机械》2023年第11期45-52,104,共9页Food and Machinery

基　　金：国家自然科学基金(编号:51975259)。

摘　　要：目的:实现大米新鲜度的快速无损检测。方法:研制一套基于纳米色敏传感器结合近红外光谱分析原理的便携式装置系统。对所采集到的不同掺陈度大米样品所对应色敏传感器的光谱数据,进行多梯度掺陈大米的鉴别与跨批次大米新鲜度的预测。结果:使用Si-CARS-PLS提取光谱特征变量,经LDA算法建模后判别模型的识别率最高,训练集和预测集的识别率分别为97.22%和95.83%。同时,PLSR模型预测跨批次数据具有更强的稳定性,不同批次大米样品数据训练集和预测集的相关系数(R_(c)、R_(p))均稳定在0.95左右,均方根误差(RMSEC、RMSEP)均低于0.2,相对分析误差(RPD)均大于3。结论:该系统具有准确率高、便捷和预测模型鲁棒性好等特点,在大米新鲜度的现场检测中有很好的应用前景。Objective:To realize rapid nondestructive testing of rice freshness.Methods:A portable device system based on nano color-sensitive sensors combined with near-infrared spectroscopy was developed.The spectral data of the corresponding color-sensitive sensors were collected for the identification of multi-gradient rice adulteration and the prediction of freshness across batches of rice.Results:Using Si-CARS-PLS to extract spectral characteristic variables,the recognition rate of discriminative model was the highest after modeling by LDA algorithm,and the recognition rate of training set and prediction set were 97.22%and 95.83%,respectively.At the same time,PLSR model predicted that cross-batch data had stronger stability.The correlation coefficients(R_(c),R_(p))of the training set and the prediction set of different batches of rice sample data were all stable at about 0.95,the root mean square errors(RMSEC,RMSEP)were all lower than 0.2,and the relative analysis errors(RPD)were all greater than 3.Conclusion:The system has the characteristics of high accuracy,convenience and good robustness of prediction model,and has a good application prospect in the field detection of rice freshness.

关 键 词：色敏传感器 可见/近红外光谱 检测系统 大米新鲜度 跨批次模型 

分 类 号：TS210.3[轻工技术与工程—粮食、油脂及植物蛋白工程]