College of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China.
College of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China.
Meat Sci. 2022 Jun;188:108801. doi: 10.1016/j.meatsci.2022.108801. Epub 2022 Mar 14.
Near infrared spectroscopy (NIR) technology is an effective method for nondestructive prediction of total volatile basic nitrogen (TVB-N) in pork. However, the NIR models lack robustness and often fail when used on a new batch. To handle the problem and obtain better prediction performance, a model updating method based on just-in-time learning (JITL) was proposed in this study. A comprehensive similarity criterion considering both input (spectra) and output (TVB-N content) information was designed. Combining a defined similarity factor, the most relevant samples to new batch samples were selected and a local least square support vector machine model was established in real time based on the selected samples. The results showed that the models updated with JITL approach kept a high predictive performance on new independent batch with prediction error decreasing from 2.95 to 1.60 mg/100 g. The robust models made on selected similar samples combined with JITL model updating strategy can support to make NIR spectroscopy a preferred choice for non-destructive assessment of quality features in pork meat.
近红外光谱（NIR）技术是一种用于无损预测猪肉中总挥发性碱性氮（TVB-N）的有效方法。然而，NIR 模型缺乏稳健性，并且在用于新批次时经常失败。为了解决这个问题并获得更好的预测性能，本研究提出了一种基于即时学习（JITL）的模型更新方法。设计了一种综合相似度标准，同时考虑输入（光谱）和输出（TVB-N 含量）信息。通过定义相似度因子，选择与新批次样本最相关的样本，并基于所选样本实时建立局部最小二乘支持向量机模型。结果表明，采用 JITL 方法更新的模型在新的独立批次上保持了较高的预测性能，预测误差从 2.95 降至 1.60mg/100g。基于选择的相似样本建立的稳健模型与 JITL 模型更新策略相结合，可以支持将近红外光谱技术作为无损评估猪肉质量特征的首选方法。
