基于可见–近红外光谱的鸡蛋胆固醇含量无损检测研究

Non-destructive Detection of Egg Cholesterol Content Based on Visible – Near-infrared Spectroscopy

摘要:

鸡蛋作为优质蛋白质来源在全球膳食结构中占据重要地位,但因其含有胆固醇而引发的健康争议持续受到消费者关注。传统检测方法需破坏样本且耗时费力,难以满足禽蛋行业大规模分级需求。为实现胆固醇含量的快速无损检测,本研究采用可见近红外光谱技术,结合最小二乘支持向量机(LSSVM)、偏最小二乘回归(PLSR)、随机森林(RF)等方法构建鸡蛋胆固醇含量的回归预测模型。通过采集135枚新鲜鸡蛋在横放(L)、尖端朝下(S)、钝端朝下(B)3种姿态的透射光谱,结合多元散射校正(MSC)、标准正态变换(SNV)等预处理方法,并系统比较竞争性自适应重加权采样(CARS)、连续投影算法(SPA)及非信息变量消除(UVE)3种特征选择算法对模型性能的影响。结果显示:光谱采集姿态显著影响预测精度,其中横放(L)姿态采集的光谱建立模型性能最优;经KS数据划分后SNV-CARS-LSSVM模型性能最佳,其训练集决定系数R2c达0.9190,测试集决定系数R2p为0.9174;训练集RMSE为60.8682mg/(100g),测试集RMSE为41.0119mg/(100g);表明本文建立的光谱模型可实现对鸡蛋胆固醇含量的快速无损精准化预测,为鸡蛋品质监测与消费者选择提供技术支撑。

Abstract:

As a high-quality protein source, egg plays an important role in the global dietary structure. However, the health controversy caused by their cholesterol content has continuously attracted consumers?? attention. Traditional detection methods require sample destruction, which are time-consuming and laborintensive, and thus cannot meet the needs of large-scale grading in the poultry egg industry. To achieve rapid and non-destructive detection of cholesterol content, the visible – near-infrared spectroscopy technology was employed, combined with methods such as the least squares support vector machine (LSSVM), partial least squares regression ( PLSR ), and random forest, to construct regression prediction models for egg cholesterol content. Transmission spectra of 135 fresh eggs were collected under three postures: horizontal placement ( L), tip-down ( S), and blunt-end down ( B). Combined with preprocessing methods such as multiplicative scatter correction ( MSC) and standard normal variate transformation ( SNV), the effects of three feature selection algorithms, i. e. , competitive adaptive reweighted sampling ( CARS), successive projections algorithm ( SPA), and uninformative variable elimination (UVE) on model performance were systematically compared. The results showed that the spectral collection posture significantly affected the prediction accuracy, among which the model built based on the spectra collected in the horizontal (L) posture exhibited the optimal performance. After KS data partitioning, the SNV CARS LSSVM model achieved the best performance, with the coefficient of determination (R2) of the training set reaching 0.9190 and that of the test set being 0.9174; the root mean square error of RMSEC was 60.8682mg/(100g), and the root mean square error of RMSEP was 41.0119mg/(100g). It indicated that the established spectral model can realize rapid, non-destructive and accurate prediction of egg cholesterol content, providing technical support for egg quality monitoring and consumer choices.