融合图谱特征信息的明虾挥发性盐基氮含量无损检测  

Non-destructive Detection of Volatile Basic Nitrogen Content in Prawns(Fenneropenaeus chinensis) Based on Spectral and Image Information Infusion

作　　者：王娅 张存存 付玉叶 张凡 王颉[1] 王文秀 WANG Ya;ZHANG Cuncun;FU Yuye;ZHANG Fan;WANG Jie;WANG Wenxiu(Engineering Research Center of Hebei Province for Agricultural Products Processing,College of Food Science and Technology,Hebei Agricultural University,Baoding 071000,China)

机构地区：[1]河北农业大学食品科技学院,河北省农产品加工工程技术中心,河北保定071000

出　　处：《食品科学》2021年第2期283-290,共8页Food Science

基　　金：“十三五”国家重点研发计划重点专项(2018YFD0901004)。

摘　　要：为实现明虾中挥发性盐基氮(total volatile basic nitrogen,TVB-N)含量的快速预测,采用近红外光谱和机器视觉技术获取明虾图谱特征信息,融合图谱特征信息构建预测明虾中TVB-N含量的支持向量机模型。获取明虾4℃贮藏0~12 d共51个样品的光谱信息和图像信息,同时参照GB 5009.228—2016《食品中TVB-N的测定》方法测定其TVB-N含量。结果表明,利用350~1 000 nm和940~1 650 nm双波段融合的光谱特征信息,并对其进行一阶导数的预处理,同时采用竞争性自适应加权算法挑选特征波长后建立的模型效果较好,其预测集相关系数(correlation coefficient in the prediction set,Rp)为0.968 7,验证集标准分析误差(standard error of prediction,SEP)为10.56mg/100g,相对分析误差(relative percent deviation,RPD)为3.38;利用图像特征信息所构建的模型效果较差,Rp为0.933 5,SEP为19.79 mg/100 g,RPD为1.74。最后,融合特征图谱信息构建TVB-N含量的预测模型,相比其他2种方法,该模型精度和稳定性都得到了提高,其Rp为0.988 4,SEP为7.51 mg/100 g,RPD为6.29。该结果证实近红外光谱技术结合机器视觉方法预测明虾中TVB-N含量的潜力,为分析评价明虾在冷藏过程中新鲜度的变化规律提供了快速检测技术。A support vector machine(SVM) model for rapidly predicting the total volatile basic nitrogen(TVB-N) content in prawns was established using near-infrared spectroscopy and machine vision technology based on spectral and image information infusion. Spectral and image information of 51 samples stored at 4 ℃ for 0 to 12 days was obtained, and their TVB-N content was measured according to the national standard method of China. The results showed that the characteristic dual-band spectral information in the range of 350–1 000 nm and 940–1 650 nm was fused and pre-processed by the first derivative method. Besides, the competitive adaptive reweighted sampling(CARS) algorithm was used to select the characteristic wavelengths. The developed model worked well with correlation coefficient in the prediction set(R_p) of 0.968 7, standard error of prediction(SEP) of the validation set of 10.56 mg/100 g, and relative percent deviation(RPD) of 3.38. On the other hand, the model constructed using the characteristic image information had poor performance, with R_p of 0.933 5, SEP of 19.79 mg/100 g and RPD of 1.74. Compared with the above two models, the model developed based on spectral and image information fusion had improved accuracy and stability with R_p of 0.988 4, SEP of 7.51 mg/100 g and RPD of 6.29. These results confirmed the potential near-infrared spectroscopy combined with machine vision to predict the TVB-N content in prawns, which could allow rapid detection of freshness changes of prawns during cold storage.

关 键 词：近红外光谱 机器视觉 挥发性盐基氮含量 支持向量机模型 

分 类 号：TS254.4[轻工技术与工程—水产品加工及贮藏工程]