牛奶主要成分的测定是评价牛奶品质的重要标准。 国家相关部门已经制定了一系列较为详尽的规范以保证牛奶等乳制品的质量安全, 但传统的检测方法操作复杂、 费时耗力并导致环境污染, 难以满足当代乳制品生产和消费的快速检测需要。 将衰减全反射-傅里叶变换红外光谱(ATR-FTIR)技术与相对湿度(RH)调控系统相结合, 建立了一种在RH连续下降条件下测量不同种类牛奶红外光谱的方法; 为牛奶等乳制品的原位无损检测、 种类区分、 品质分析等提供了新的途径。 (1)选取伊利品牌纯牛奶、 臻浓牛奶、 脱脂纯牛奶、 高钙低脂奶、 舒化牛奶5类牛奶为研究对象, 在RH连续下降的条件下, 采集不同种类牛奶样品在蒸发浓缩过程中的红外光谱, 对其主要营养成分进行峰位归属和定性分析, 仅需要微升级样品, 就可在短时间内提取样品浓缩过程中水、 碳水化合物、 脂肪、 蛋白质等主要成分的光谱信息, 实现对不同类别市售牛奶化学成分的较全面表征; (2)采用NWUSA软件对所得红外光谱数据进行建模分析, 选取4 000~400 cm-1波段为变量对所得光谱数据进行主成分分析(PCA), 并评估模型对不同类别牛奶的鉴别能力。 所得PCA分析数据在同组内聚集度良好, 不同组内坐标轴上相距较远, 说明模型选取合理可靠, 具有代表性。 实验中共使用了75份牛奶样品, 其中生产日期、 产地为随机因素, 牛奶的种类和品牌为固定因素。 结果表明, 该方法具有操作简便、 反应灵敏、 光谱质量高、 无损测量等优点, 适用于牛奶等乳制品的原位、 快速、 无损鉴别分析。

The determination of the main components of milk is an important criterion for evaluating the quality of milk. Relevant national departments have formulated a series of relatively detailed specifications to ensure the quality and safety of milkand other dairy products. However,thetraditional detection methods are often complex, time-consuming and labor-intensive. Some even cause environmental pollution, making it difficult to meet the rapid detection needs of contemporary dairy production and consumption. In this study, the portable attenuated total reflectionFourier transform infrared spectroscopy (ATR-FTIR) technique was combined with relative humidity (RH) control system to establish a method to measure the infrared spectra of different kinds of milk under the condition of continuous decline of RH. This method provides a new way for non-destructive testing, classification and quality analysis of milk products. The main contents include:(1)Selecting five types of milk of YiLi brand (pure milk, Zhennong milk, skimmed pure milk, high-calcium low-fat milk, and Shuhua milk) as research objects. Whose infrared spectra in the process of evaporation and concentration were collected under continuous decline of RH, and the peak position attribution and qualitative analysis of main nutritional components were carried out. It only takes a few microliters of milk samples for us to obtain the spectral information of the main components, such as water, carbohydrates, fats, and proteins, during the sample concentration process in a short time and achievea relatively comprehensive characterization of the chemical components of milk; (2) Using NWUSA software to build the model and process the infrared spectral data, choosing 4 000～400 cm-1 band as the variables to perform PCA and evaluating the identification ability of the model for different types of milk, it shows that the data of PCA process are well aggregated in the same group. The coordinate axes in different groups are far apart, indicating that the model selection is both reliable and representative. A total of 75 milk samples were used in the experiment, in which the production date and place were random factors, and the type and brand of milk were fixed factors. The results show that the proposed method has the advantages of simple operation, sensitive response, high spectral quality and non-destructive measurement, which is suitable for in-situ, rapid and non-destructive identification and analysis of milk and other dairy products.