Nondestructive detection of S-ovalbumin content in eggs using portable NIR spectrometer and MPA-CARS

The feasibility of applying a low-cost and portable NIR spectrometer to detect the S-ovalbumin content of eggs was investigated in this study, and a model population analysis based competitive adaptive reweighted sampling (MPA-CARS) algorithm was proposed to reduce the dimensionality of spectral data. The NIR spectra of egg samples with different storage periods were collected in the wavelength range of 900-1700 nm. Standard normal variate (SNV) and Savitzky-Golay(1st) derivative were used to preprocess the raw spectral data. Then, CARS and the proposed MPA-CARS were applied to select effective wavelengths from the whole spectral range. Statistical results showed that MPA-CARS had better feature extraction performance than CARS and the number of selected feature wavelengths was less. Support vector machine (SVM), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were used to establish calibration models for predicting S-ovalbumin content, in which the simplified XGBoost model based on MPA-CARS feature wavelengths yielded the best performance, with R-P(2) of 0.906 and RMSEP of 7.799%. Therefore, portable NIR has the potential to be a useful tool for S-ovalbumin content detection. This could help food processing industry to arrange miniaturized NIR sensors to detect egg quality at different points in the egg supply chain. Practical applications S-ovalbumin content is a freshness index of eggs and can affect the quality of processed foods. This study demonstrated the feasibility of using portable NIR spectrometer to detect the S-ovalbumin content of eggs, which can help food processing agencies to implement low-cost NIR sensors to detect egg quality at different stages of the supply chain.

Automated summary

The feasibility of using a low-cost and portable NIR spectrometer to detect the S-ovalbumin content of eggs was investigated in this study. A model population analysis based competitive adaptive reweighted sampling (MPA-CARS) algorithm was proposed to reduce the dimensionality of spectral data. The results showed that MPA-CARS had better feature extraction performance than CARS and required fewer selected feature wavelengths. The simplified XGBoost model based on MPA-CARS feature wavelengths yielded the best performance for predicting S-ovalbumin content.