基于可见-近红外光谱的虾青素/超氧化物歧化酶饲喂鸡蛋鉴别与品质预测

Identification and Rapid Quality Prediction of Astaxanthin/Superoxide Dismutase Fed Eggs Based on Visible-near Infrared Spectroscopy

摘要:

禽蛋是我国农业农村经济支柱产业之一，饲料强化鸡蛋品质快速无损检测对产业发展意义重大。本研究基于可见-近红外光谱技术，探究虾青素(ASTA)和超氧化物歧化酶(SOD)饲料强化鸡蛋的特异性光谱特征，并构建鉴别和品质预测模型。首先，采集ASTA/SOD饲喂鸡蛋与普通鸡蛋在波段500~950nm范围内的透射光谱，并通过理化测定验证其品质差异，结果表明：ASTA组可显著提高鸡蛋蛋白质含量、蛋黄颜色(P<0.05)；在饲喂前期，SOD组可显著提高鸡蛋脂肪含量(P<0.05)，ASTA组和SOD组均能显著降低鸡蛋含水率(P<0.05)。根据透射光谱探究饲喂鸡蛋的特异性光谱特征，通过不同预处理方法结合竞争性自适应重加权(CARS)算法、连续投影法(SPA)及非信息变量剔除(UVE)3种特征选择算法构建了支持向量机(SVM)鉴别模型和偏最小二乘回归(PLSR)模型。结果表明，ASTA/SOD饲喂鸡蛋最优鉴别模型为SG-CARS-SVM，测试集识别率为95.33%。对于ASTA/SOD饲喂鸡蛋蛋白质含量、含水率和脂肪含量这3类关键品质指标,ASTA组最优预测模型分别为FD-CARS-PLSR、Auto-CARS-PLSR和SNV-CARS-PLSR，对应的测试集R2p分别为0.933、0.937和0.889，RMSEP分别为0.250%、0.209%和0.196%；而在SOD组中，最优模型分别为FD-CARS-PLSR、MSC-CARS-PLSR和FD-CARS-PLSR，其测试集R2p分别为0.929、0.824和0.817，RMSEP分别为0.239%、0.310%和0.273%。本研究建立的光谱模型可实现对ASTA/SOD饲喂鸡蛋无损鉴别及品质快速预测，为鸡蛋品质监测与高质量养殖提供支持。

Abstract:

Poultry eggs are one of the pillar industries of China’s rural and agricultural economy. The rapid and non-destructive testing of the quality of feed-fortified eggs is of great significance to the development of the industry. Based on visible-near infrared spectroscopy technology, the specific spectral characteristics of astaxanthin (ASTA) and superoxide dismutase (SOD) feed-fortified eggs were explored, and an identification and quality prediction model was built. Firstly, the transmission spectra of ASTA/SOD fed eggs and ordinary eggs were collected in the 500~950nm range, and their quality differences were verified through physical and chemical measurements. The results showed that the ASTA group could significantly increase egg protein content and egg yolk color (P<0.05). In the early stage of feeding, the SOD group can significantly increase the fat content of eggs (P<0.05), and both the ASTA group and the SOD group can significantly reduce the water content of eggs (P<0.05). Then the specific spectral characteristics of fed eggs were explored based on the transmission spectrum, and three feature selection methods were combined with the competitive adaptive reweighted sampling (CARS) algorithm, the successive projections algorithm (SPA) and the uninformative variables elimination (UVE) through different preprocessing methods. The algorithm constructed a support vector machine (SVM) identification model and a partial least squares regression (PLSR) model. The results showed that the optimal identification model for ASTA/SOD fed eggs was SG-CARS-SVM, and the recognition rate of the test set was 95.33%. For the three key quality indicators of protein content, moisture content and fat content of eggs fed with ASTA/SOD, the optimal prediction models of the ASTA group were FD-CARS-PLSR, Auto-CARS-PLSR and SNV-CARS-PLSR, respectively, and the corresponding test set R2p values were 0.933, 0.937 and 0.889, and RMSEP were 0.250%, 0.209% and 0.196%, respectively;in the SOD group, the optimal models were FD-CARS-PLSR, MSC-CARS-PLSR and FD-CARS-PLSR, respectively, and their test set R2p values were 0.929, 0.824 and 0.817, and RMSEP were 0.239%, 0.310% and 0.273%, respectively. The spectral model established can realize non-destructive identification and rapid quality prediction of ASTA/SOD-fed eggs, providing support for egg quality monitoring and high-quality breeding.