Non-targeted high-resolution mass spectrometry study for evaluation of milk freshness-Supplementary Material

Supplementary material to the article "Non-targeted high-resolution mass spectrometry study for evaluation of milk freshness-Supplementary Material"

Abstract:
Milk freshness is an important parameter for both consumers’ health and quality of milk-based products.
Up to now there have been neither analytical methods nor specific parameters to uniquely define milk freshness from a complete and univocal chemical perspective. In this study, 8 molecules were selected and identified as responsible for milk aging, using a liquid chromatography–high-resolution mass spectrometry approach followed by chemometric data elaboration.
For model setup and marker selection, 30 high-quality pasteurized fresh milk samples were collected directly from the production site and analyzed immediately and after storage at 2 to 8°C for 7 d. The markers were then validated by challenging the model with a set of 10 milk samples, not previously analyzed. Our results demonstrated that the markers identified within this study can be successfully used for the correct classification of non-fresh milk samples, complementing and successfully enhancing parallel evaluations obtainable through sensory measures.

Description of Supplementary Material:

SECTION 1 – MULTIVARIATE DATA ELABORATION
The final PCA (negative mode) and the OPLS-DA (negative mode) scores plot of the results obtained are presented in figures S1-S2.
The variance of the x and y variables explained by the model (R^2 X (cum) and R^2 Y (cum)), and the cumulative predicted variation in the Y matrix (Q^2 (cum)) are reported below each figure.

Figure S1. ESI - PCA scores plot of milk samples. Blue dots: Fresh Samples; green dots: “7 days” samples
Figure S2. ESI - OPLS‐DA scores plot of the fresh samples against the “7 day” samples

SECTION 2 – COMPOUNDS IDENTIFICATION
Figure S3. Trends of the identified molecules through all the time points: Mean area values (+/- Standard Error) of the target compounds through the time points. Time points: blue bar, “t zero”; yellow bar, “1 day”; orange bar, “2 days”; green bar, “3 days”; violet bar, “4 days”; red bar, “7 days”

SECTION 3 – SENSORIAL EVALUATIONS
Table S1: List of the tasted sample
Table S2: Resume of the panelist’s evaluations

补充材料：《非靶向高分辨率质谱法评估牛奶新鲜度的研究——补充材料》摘要：牛奶的新鲜度是关乎消费者健康及牛奶制品品质的关键参数。迄今为止，尚无分析方法和特定参数能够从全面且独特的化学角度对牛奶新鲜度进行明确界定。在本研究中，通过液相色谱-高分辨率质谱法结合化学计量学数据处理，选定了8种分子，并确认其为牛奶老化的责任分子。为模型构建和标记选择，直接从生产现场收集了30份高质量巴氏杀菌新鲜牛奶样本，并在即时分析以及2至8°C储存7天后进行分析。随后，通过一组10份未经分析的牛奶样本对模型进行验证。研究结果证实，本研究中确定的标记可以成功用于对非新鲜牛奶样本进行正确分类，从而补充并有效提升通过感官评估获得的并行评估结果。补充材料描述：第一部分——多变量数据加工：展示最终主成分分析（负模式）和偏最小二乘判别分析（负模式）结果得分图，如图S1-S2所示。模型解释的x和y变量的变异性（R^2 X (cum) 和 R^2 Y (cum)）以及Y矩阵中累积预测的变异（Q^2 (cum)）分别列于每幅图下方。图S1. ESI-PCA得分图展示牛奶样本。蓝色点：新鲜样本；绿色点：“7天”样本。图S2. ESI-OPLS-DA得分图展示新鲜样本与“7天”样本之间的比较。第二部分——化合物鉴定：图S3. 鉴定分子随时间点的趋势：目标化合物在时间点上的平均面积值（±标准误差）。时间点：蓝色条形，t零；黄色条形，1天；橙色条形，2天；绿色条形，3天；紫色条形，4天；红色条形，7天。第三部分——感官评估：表S1：品尝样本列表。表S2：评委评估总结。