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电子鼻应用于猪肉丸子香味预测
引用本文:蒋强,郑丽敏,田立军,程国栋,蒙万隆. 电子鼻应用于猪肉丸子香味预测[J]. 食品科学, 2018, 39(10): 228-233. DOI: 10.7506/spkx1002-6630-201810035
作者姓名:蒋强  郑丽敏  田立军  程国栋  蒙万隆
作者单位:(1.中国农业大学信息与电气工程学院,北京 100083;2.食品质量与安全北京实验室,北京 100083)
基金项目:“十三五”国家重点研发计划重点专项(2016YFD0401504);“十二五”国家科技支撑计划项目(2014BAD04B05)
摘    要:
为应用电子鼻技术快速、客观地评价猪肉丸子风味,实验设计了4?种肥肉、瘦肉配比(100%瘦、90%瘦、80%瘦和70%瘦)的猪肉丸子,使用电子鼻和顶空固相微萃取和气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)联用对猪肉丸子的各种挥发性物质进行检测。同时对其进行香味指标感官评定。采用线性判别和神经网络方法对不同猪肉丸子的电子鼻数据进行识别分类;并利用偏最小二乘回归方法对电子鼻传感器和挥发性物质的相关性进行分析。结果表明,4?类丸子在香味评价上差异极显著(P<0.01),肥肉比例高的丸子获得了较高的评分。线性判别分析和神经网络方法的分类效果显示,电子鼻对4?类猪肉丸子具有良好的分类能力。GC-MS共检测出了67?种风味化合物,其中主要是醛类、醇类、酮类等物质,挥发性风味物质的差异是造成各类丸子感官评分差异的根本原因。偏最小二乘回归模型显示电子鼻传感器数据与主要挥发性化合物相对含量具有良好的相关性。使用逐步回归建立电子鼻与评价指标数据之间的分值预测回归模型(R2>0.9,P<0.01),表明丸子香味可以使用电子鼻进行预测。

关 键 词:猪肉丸子风味  感官评价  电子鼻  神经网络  气相色谱-质谱  偏最小二乘回归  

Application of Electronic Nose in Aroma Prediction of Pork Balls
JIANG Qiang,ZHENG Limin,TIAN Lijun,CHENG Guodong,MENG Wanlong. Application of Electronic Nose in Aroma Prediction of Pork Balls[J]. Food Science, 2018, 39(10): 228-233. DOI: 10.7506/spkx1002-6630-201810035
Authors:JIANG Qiang  ZHENG Limin  TIAN Lijun  CHENG Guodong  MENG Wanlong
Affiliation:(1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Beijing Laboratory of Food Quality and Safety, Beijing 100083, China)
Abstract:
The feasibility of rapidly and objectively evaluating the flavor of pork balls using an electronic nose was investigated. The odor of four pork meatballs with different proportions of lean (100%, 90%, 80% and 70%) was analyzed by the electronic nose, and their volatile compounds were determined by head space-solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Besides, the aroma intensity was scored by a trained sensory panel. Both linear discriminant analysis (LDA) and neural network algorithm (NNA) were used to identify and classify the electronic nose data. Partial least squares regression (PLSR) was applied to correlate the aroma compounds identified by electronic nose sensors and GC-MS data. The results showed that there were significant differences in the aroma scores of four meatball samples (P < 0.01). The meatballs with higher proportion of fat had higher sensory scores. Analysis by LDA and NNA demonstrated that the aroma profiles of all four samples could be discriminated by the electronic nose. A total of 67 volatile aroma compounds were detected by GC-MS, mainly including aldehydes, alcohols and ketones. Differences in volatile flavor compounds were mainly responsible for the differences in sensory scores. The PLSR model established exhibited good correlation between the electronic nose data and the relative contents of major volatile compounds. The prediction model established by stepwise regression (R2 > 0.9, P < 0.01) indicated that the electronic nose can be used to predict the aroma of meatballs with reliable results.
Keywords:pork ball flavor  sensory evaluation  electronic nose  neural network  gas chromatography-mass spectrometry  partial least squares regression  
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