气味指纹分析技术在食品质控和风味研究的应用

Applications of Sensor Array Fingerprint Technology in Food Quality Control

着重以食用油、橙汁和奶制品为例,阐述了SensoryArrayFingerprinAtnalyzerMethod(气味指纹分析法)对食品所建立的新型质量控制和风味研究方法。经针对7种储存时间不同的食用油、脂(大豆油、菜籽油、猪油、牛脂、禽类脂、白色油脂、棕榈油)的过氧化值和酸价的分析模型的建立,展示了应用气味指纹分析技术(电子鼻)对食用油品质控制及抗氧化能力比较的新方法,对不同品质的橙汁(半年和一年半的鲜榨汁,和浓缩还原汁)、不同种类的鲜奶(灭菌全脂奶、灭菌半脱脂奶、半脱脂生奶和乳粉还原奶)和真伪某品牌奶粉的区分,表明该技术对复杂介质样品的品质变化具有独特的识别能力。分析结果表明,该分析技术具有以下优点:满意的分析灵敏度和重复性:传感器检测平均重复性为1.32%RSD(n=10),对同一样品测量的重复性为1.4%RSD(n=10),对所有油样的过氧化值和酸价测定具有良好的线性关系,平均相关系数为0.9984。不同品质样品之间区分指数DI的特异性明显(>90)。所有的数据通过化学计量学方法处理,并且该分析方法无需样品前处理。

Fingerprinting technology has become a useful tool for food quality control, to detect overall product quality impacted by coherent effects of many constituents. In this paper, Sensor Array Fingerprint Analyzer is implemented for a stability study of seven edible oils （ Soybean Oil, Canola Oil, Lard, Beef Tallow, Poultry Fat, White grease, Palm Oil ） during storage. The results showed the impacts of storage on increasing amount of PV values and quality of oils. The results corrected very well with standard methods for PV measurement. The methods for oil samples demonstrated good linearity （ R2 =0.9984 for all PV determination for 7 oils ）, and repeatability （ RSD〈2 % ） for both sensor responses and sample PV value determination （ n=10 ） . The technique was also used to differentiate different quality of orange juice （ fresh juice stored for 0.5year and 1.5years, and from concentrated ）, different milk samples （ fresh whole milk, fresh semi-skimmed milk, raw semi-skimmed milk and powder milk ） . All samples with different quality were very well differentiated with differentiation index DI〉90 %. Chemometric methodology was used for all data treatments. Another advantage of the method is that it does not require any sample pre-treatment.