Ahmed Md Wadud, Alam Sreezan, Khaliduzzaman Alin, Emmert Jason Lee, Kamruzzaman Mohammed
The Grainger College of Engineering, College of Agricultural, Consumer and Environmental Sciences, Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
J Sci Food Agric. 2025 Aug 15;105(10):5550-5562. doi: 10.1002/jsfa.14290. Epub 2025 Apr 17.
Eggshell strength is crucial for ensuring high-quality eggs, reducing breakage during handling, and meeting consumer expectations for freshness and integrity. Conventional methods of eggshell strength measurement are often destructive, time-consuming and unsuitable for large-scale applications. This study evaluated the potential of near-infrared (NIR) spectroscopy combined with explainable artificial intelligence (AI) as a rapid, non-destructive method for determining eggshell strength. Various multivariate analysis techniques were explored to enhance prediction accuracy, including spectral pre-processing and variable selection methods.
Principal component analysis and partial least squares discriminant analysis effectively classified eggs based on a threshold shell strength of 30 N. Regression models, including partial least squares regression, random forest (RF), light gradient boosting machine and K-nearest neighbors, were evaluated. Using only 14 selected variables, the RF model achieved a very good prediction performance with of 0.83, root mean square error of prediction of 1.49 N and ratio of prediction to deviation of 2.44. The Shapley additive explanation approach provided insights into variable contributions, enhancing the model's interpretability.
This study demonstrated that NIR spectroscopy, integrated with explainable AI, is a robust, non-destructive and environmentally sustainable approach for eggshell strength prediction. This innovative method holds significant potential for optimizing resource utilization and enhancing quality control in the egg industry. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
蛋壳强度对于确保鸡蛋质量、减少处理过程中的破损以及满足消费者对新鲜度和完整性的期望至关重要。传统的蛋壳强度测量方法通常具有破坏性、耗时且不适用于大规模应用。本研究评估了近红外（NIR）光谱结合可解释人工智能（AI）作为一种快速、无损测定蛋壳强度方法的潜力。探索了各种多元分析技术以提高预测准确性，包括光谱预处理和变量选择方法。
主成分分析和偏最小二乘判别分析基于30 N的阈值蛋壳强度有效地对鸡蛋进行了分类。评估了回归模型，包括偏最小二乘回归、随机森林（RF）、轻梯度提升机和K近邻。仅使用14个选定变量，RF模型就实现了非常好的预测性能，决定系数为0.83，预测均方根误差为1.49 N，预测与偏差比为2.44。Shapley加法解释方法提供了对变量贡献的见解，增强了模型的可解释性。
本研究表明，结合可解释AI的近红外光谱是一种用于蛋壳强度预测的强大、无损且环境可持续的方法。这种创新方法在优化鸡蛋行业资源利用和加强质量控制方面具有巨大潜力。© 2025作者。《食品与农业科学杂志》由John Wiley & Sons Ltd代表化学工业协会出版。
