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| 近红外光谱技术快速检测奶酪及其制品的核心质量指标 |
| Rapid Assessment of Core Quality Indicators in Cheese and Cheese Products Using Near-Infrared Spectroscopy |
| 投稿时间：2025-03-12 修订日期：2025-06-27 |
| DOI： |
| 中文关键词: 奶酪 蛋白质 脂肪 水分 近红外光谱技术 快速检测 |
| 英文关键词:Cheese Protein Fat Moisture content Near infrared spectroscopy technology Quick detection |
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| 中文摘要: |
| 目的 针对传统化学检测方法在奶酪制品质量控制中的时效性缺陷，探索使用近红外光谱设备快速检测奶酪及其制品核心质量指标的快速检测技术的应用方向，验证近红外光谱技术对奶酪品质指标实现快速检测的可行性。方法 通过实验选择市场上常见的奶酪棒、芝士片、奶油芝士、马苏里拉等4类再制奶酪产品，分建模组和验证组，选择一个品牌的设备使用建模组样品进行建模实验；使用4个不同厂家近红外同步开展国家标准检测与多设备近红外检测对比分析。结果 在建模层面，通过对比偏最小二乘法（PLS）等算法的建模机理，成功构建蛋白质（14.2-28.6%）、脂肪（18.5-35.4%）、水分（38.7-52.1%）及pH值（5.3-6.8）等核心指标的多维预测模型，决定系数R2提升至0.928-0.972。在验证层面，不同奶酪产品分别使用4类近红外光谱研究仪器的蛋白质、脂肪、水分项目检测结果与国家标准方法测定的奶酪营养成分的平均偏差除1号设备的马苏里拉的蛋白质偏差超过10%外，其他营养成分国标等同性实验符合要求，且精密度符合国家标准方法要求。结论 基于当前四种设备的验证，使用近红外光谱设备快速检测奶酪及其制品核心质量指标的快速检测技术的应用可行，近红外光谱技术在奶酪组成成分的快速检测方法可作为传统化学检测方法提供有效的补充，为高效率、低成本、环保的检测提供更多的可能性。 |
| 英文摘要: |
| ABSTRACT: Objective To address the timeliness limitations of traditional chemical detection methods in cheese product quality control, the application direction of using near-infrared (NIR) spectroscopy equipment for rapidly detecting core quality indicators of cheese and its products was explored, and the feasibility of NIR spectroscopy for rapid detection of cheese quality indicators was verified. Methods Four types of common processed cheese products (cheese sticks, cheese slices, cream cheese, Mozzarella) were selected for experimentation and divided into modeling and validation sets; modeling experiments were conducted using samples from the modeling set on a single-brand instrument; synchronous national standard detection and multi-instrument NIR detection comparative analysis were performed using instruments from four different manufacturers on the validation sets. Results For modeling, multidimensional prediction models for core indicators (protein: 14.2-28.6%, fat: 18.5-35.4%, moisture: 38.7-52.1%, pH: 5.3-6.8) were successfully constructed by comparing the modeling mechanisms of algorithms like partial least squares (PLS), with the coefficient of determination (R2) increased to 0.928-0.972. For validation, the average deviations between the NIR detection results (protein, fat, moisture) obtained using four types of NIR spectrometers on different cheese products and those determined by national standard methods met equivalence requirements except for the protein deviation (>10%) of Mozzarella on instrument No.1; precision also met the requirements of national standard methods. Conclusion The application of NIR spectroscopy equipment for rapid detection of core quality indicators in cheese and its products is feasible; NIR spectroscopy technology can serve as an effective supplement to traditional chemical detection methods for rapid analysis of cheese composition, offering greater potential for high-efficiency, low-cost, and environmentally friendly testing. |
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