The aim of the presented study was to evaluate the potential of a headspace fingerprint as a multivariate intrinsic indicator for monitoring temperature history variation that can occur, for example, during in-pack food processing at the product level. Using solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC–MS), we monitored the extracted volatile fraction of a series of 8 well-defined thermally processed broccoli purees, differing only in the maximal process temperature reached. Our results showed that the relative composition of the extracted volatile fraction clearly depended on the processing intensity (as a measure for thermal variation) applied. In addition, this headspace fingerprinting approach, including multivariate data analytical approaches, allowed a swift selection of specific intrinsic fingerprint markers. The evaluation of the concentration of each of these markers allows to discriminate between the different processing intensities. However, we suggest to perform a linear combination of the information from relevant identified intrinsic fingerprint markers given the increased reliability of a multiple response indicator. The presented approaches are a promising proof-of-principle that has potential to be exploited for monitoring other processing non-uniformities as well.

Industrial relevance

Thermal treatment is by far still the most commonly used preservation method for food with a high water content. In-pack retort treatment commonly results in some temperature non-uniformity occurring during the treatment at both the reactor and/or the product level. While the use of thermocouples for acquiring time-temperature history is often an excellent solution, the use of intrinsic food components as indicators can be of great advantage for process monitoring. So far usually pre-selected single component intrinsic indicators were suggested. In this work, we are presenting the utilization of the headspace fingerprint as a multivariate intrinsic indicator instead of using a single compound. From the industrial point of view, we present an innovative concept of monitoring product specific extracted volatile fraction, followed by selection of specific intrinsic fingerprint markers and linearly combining the information from those markers. While the advanced data analysis for selection of the fingerprint markers will have to be done per specific product, only the linear combination of the specific data from the markers can be used for routine analysis. Such concept can provide increased reliability due to the utilization of a non-pre-selected multiple response indicator with a relatively feasible monitoring of the markers using gas chromatography mass spectrometry (GC–MS).

中文翻译：

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顶空指纹作为监测包装内热过程温度变化的潜在多元内在指标：西兰花菜泥的案例研究

本研究的目的是评估顶空指纹作为多变量固有指标的潜力，以监测温度历史变化，例如在产品级包装食品加工过程中可能发生的变化。使用固相微萃取气相色谱质谱仪（SPME-GC-MS），我们监测了一系列8种定义明确的热加工西兰花泥的提取挥发分，仅在达到的最高工艺温度上有所不同。我们的结果表明，提取的挥发物组分的相对组成明显取决于所施加的加工强度（作为热变化的量度）。此外，这种顶空指纹方法，包括多元数据分析方法，可以快速选择特定的固有指纹标记。对每种标记物浓度的评估可以区分不同的处理强度。但是，鉴于多重响应指标的可靠性提高，我们建议对来自相关的已识别内在指纹标记的信息进行线性组合。提出的方法是有希望的原理证明，也有可能被利用来监视其他处理不均匀性。

行业相关性

迄今为止，热处理仍然是含水量高的食品最常用的保存方法。袋内杀菌处理通常导致在处理过程中在反应器和/或产物水平上发生一些温度不均匀性。尽管使用热电偶来获取时间-温度历史记录通常是一个很好的解决方案，但是将食物的固有成分用作指示剂可能对过程监控具有很大的优势。到目前为止，通常建议使用预选的单组分内在指标。在这项工作中，我们将顶空指纹的利用作为多元内在指标，而不是使用单一化合物。从工业的角度来看，我们提出了一种创新的概念，可以监控特定于产品的提取挥发分，然后选择特定的固有指纹标记，然后线性组合这些标记中的信息。虽然必须针对每个特定产品进行用于选择指纹标记的高级数据分析，但是只有来自标记的特定数据的线性组合才能用于常规分析。由于利用了非预先选择的多重响应指标，并使用气相色谱质谱（GC-MS）对标记进行了相对可行的监控，因此这种概念可以提供更高的可靠性。只有来自标记的特定数据的线性组合才能用于常规分析。由于利用了非预先选择的多重响应指标，并使用气相色谱质谱法（GC-MS）对标记进行了相对可行的监测，因此这种概念可以提供更高的可靠性。只有来自标记的特定数据的线性组合才能用于常规分析。由于利用了非预先选择的多重响应指标，并使用气相色谱质谱（GC-MS）对标记进行了相对可行的监控，因此这种概念可以提供更高的可靠性。