School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, PR China; School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Xiyuan Road 279, Suzhou 215008, Jiangsu, PR China.
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, PR China.
Food Chem. 2021 May 1;343:128515. doi: 10.1016/j.foodchem.2020.128515. Epub 2020 Oct 31.
The maturity level of eggs during pickling is conventionally assessed by choosing few eggs from each curing batch to crack open. Yet, this method is destructive, creates waste and has consequences for financial losses. In this work, the feasibility of integrating electronic nose (EN) with reflectance hyperspectral (RH) and transmittance hyperspectral (TH) data for accurate classification of preserved eggs (PEs) at different maturation periods was investigated. Classifier models based solely on RH and TH with EN achieved a training accuracy (93.33%, 97.78%) and prediction accuracy (88.89%; 93.33%) respectively. The fusion of the three datasets, (EN + RH + TH) as a single classifier model yielded an overall training accuracy of 98.89% and prediction accuracy of 95.56%. Also, 52 volatile compounds were obtained from the PE headspace, of which 32 belonged to seven functional groups. This study demonstrates the ability to integrate EN with RH and TH data to effectively identify PEs during processing.
在腌制过程中，鸡蛋的成熟度通常通过从每个腌制批次中选择几个鸡蛋来打开进行评估。然而，这种方法具有破坏性，会产生浪费，并导致经济损失。在这项工作中，研究了将电子鼻 (EN) 与反射高光谱 (RH) 和透射高光谱 (TH) 数据集成，用于准确分类不同成熟度的皮蛋 (PE) 的可行性。仅基于 RH 和 TH 的分类器模型与 EN 分别实现了 93.33%的训练准确性和 88.89%的预测准确性；97.78%和 93.33%。将三个数据集（EN+RH+TH）融合为单个分类器模型，整体训练准确性为 98.89%，预测准确性为 95.56%。此外，从 PE 头部空间获得了 52 种挥发性化合物，其中 32 种属于七个功能组。这项研究表明，能够将 EN 与 RH 和 TH 数据有效结合，用于加工过程中 PE 的有效识别。
