电子鼻应用于猪肉丸子香味预测的研究

摘要/Abstract

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（head space-solid phase microextraction，HS-SPME）和气相色谱质谱联用（gas chromatography-mass spectrometry，GC-MS）对猪肉丸子的各种挥发性物质进行检测和测定。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百分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，经检验，结果可靠。

关键词: 猪肉丸子风味, 感官评定, 电子鼻, 神经网络, 气相色谱-质谱, 偏最小二乘回归

Abstract: In order to study how to use electronic nose technology to evaluate pork balls flavor quickly and objectively, the experiment designed 4 kinds of pork balls with different fat ratio(100%,90%,80% and 70% lean meat), making a fragrance sensory evaluation for the pork balls and determined the aroma compounds of balls by using electronic nose, head space-solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). Linear discriminant and neural network algorithm were used to identify and classify the electronic nose data of different pork balls. Partial least squares regression (PLSR) was applied to indicate the correlation between the aroma compounds identified by electronic nose sensor and GC-MS data. The results showed that there were significant differences in fragrance sensory index (P<0.01). The balls which with higher proportion of fat obtained a better evaluation in the sensory index. From the classification effect of linear discriminant and the neural network algorithm can be concluded that the electronic nose has a good classification ability on the four types of pork balls. 67 kinds of aroma compounds were detected by GC-MS, mainly including aldehydes, alcohols and ketones. The fundamental factor which lead to different sensory indexes scores of the 4 kinds of balls is the difference in aroma compounds. Good relationships between electronic nose sensor and GC/MS data were found in partial least squares regression (PLSR) model. In order to use electronic nose to predict the results of the evaluation, the regression model (R2>0.9,P<0.01) between the electronic nose and the sensory indexes data were established by stepwise regression. After examining, the model was reliable , indicating that the electronic nose be used for evaluation of the fragrance of ball flavor is feasible.

Key words: pork balls flavor, Sensory evaluation, electronic nose, neural network, gas chromatography-mass spectrometry, partial least squares regression

中图分类号:

TS207.7

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的研究[J]. 食品科学, 0, (): 0-0.

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