Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r² 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r² 0.98 ± 0.01. Thus, the model accuracy ±10–30% is well within the standard safety margins.

巴氏杀菌是食品工业中确保消耗品安全的关键加工方法。当代巴氏杀菌过程的一个主要部分涉及使用快速巴氏杀菌系统，其中液体通过管道系统泵送以在预定时间内加热它们。准确监控应用于产品的热处理量具有挑战性。这种监测有助于确保应用正确的热影响（以巴氏杀菌单位表示），这通常计算为时间和温度的乘积，同时考虑到微生物灭活的可实现性。最先进的方法涉及使用单点温度测量和该温度的保持时间来计算应用的巴氏杀菌单元。对准确性的担忧导致高安全裕度，降低巴氏杀菌产品的质量。在这项研究中，应用的巴氏杀菌水平是使用回归模型估算的，这些模型是在对不同类型和品牌的果汁进行巴氏杀菌时收集的 NIR 光谱数据训练而成的。结合不同的预处理方法训练了几种传统的回归模型，包括一种新颖的预测异常值检测方法。用所有类型果汁的串联数据训练的广义果汁模型证明了 RMSECV ∼2.78 ± 0.09 和 r 的交叉验证分数 结合不同的预处理方法训练了几种传统的回归模型，包括一种新颖的预测异常值检测方法。用所有类型果汁的串联数据训练的广义果汁模型证明了 RMSECV ∼2.78 ± 0.09 和 r 的交叉验证分数 结合不同的预处理方法训练了几种传统的回归模型，包括一种新颖的预测异常值检测方法。用所有类型果汁的串联数据训练的广义果汁模型证明了 RMSECV ∼2.78 ± 0.09 和 r 的交叉验证分数² 0.96 ± 0.01，而单独的果汁模型显示 RMSECV ∼1.56 ± 0.04 和 r ² 0.98 ± 0.01 的平均交叉验证分数。因此，模型精度 ±10–30 %完全在标准安全范围内。