Studies On Monitoring Of Sausage Qualities Based On Multispectral Imaging Technology

Meat product plays an important role in our diet, and the quality characteristics of which not only affect the health of consumers, but also determine the development of meat industry. Therefore, how to detect these qualities has been one of the researches focuses in food science.At present, many conventional methods based on sensory, chemical, physical, and microbiological approaches have been employed for detecting the quality changes of meat product. However, these methods always suffer certain disadvantages as they are normally ineffective, destructive, and/or high-cost, hindering them from further on-line applications. Consequently, it is of great significance to develop rapid, accurate, and non-destructive detection methods to identify the qualities of meat product. In order to implement this requirement, multispectral images of sausages treated by different processing methods and storage time were captured using multispectral imaging (MSI) system in the range of 405-970 nm, and the information among the acquired images were extracted and analyzed by chemometrics methods such as partial least square regression (PLSR), support vector machine (SVM), principal component analysis (PCA), successive projections algorithm (SPA), and gray level co-occurrence matrix (GLCM). The main achievements of this work are summarized as follow:(1) Based on spectral data, optimized models for hardness and aerobic plate count in sausages could be established by using SVM and PLSR methods, respectively. Thus, the good results of models were obtained with determination coefficient (Rp2) of 0.546 and ratio of prediction to deviation (RPD) of 1.455 for hardness, and Rp2 of 0.891 and RPD of 2.970 for aerobic plate count.(2) Combined textures of principal component images with spectra could improve precision of prediction models for moisture content, water holding capacity, cohesiveness, chewiness, heme rion content, and non-heme iron content in sausages. The Rp2 of these optimization models were 0.899,0.691,0.619,0.728,0.912, and 0.901, respectively, and the RPD of those were 3.027,1.800,1.561,1.770,3.356, and 3.167, respectively. Additionally, there was no correlation between the precision of prediction models and contribution rate of principal component.(3) Distribution of moisture content, water holding capacity, chewiness, aerobic plate count, heme iron content, and non-heme iron content in sausages could be successfully visualized by image processing and interpolation based on model equations. Thereby, the changes of sausage qualities could be visually evaluated by these maps, which demonstrated the superiority of MSI technology.This work suggested that MSI technique could apply to detect sausage qualities as a rapid, precious, and nondestructive method, which provided a feasibility approach for real-time detection of quality changes of sausages during processing and storage. Our results could provide a theory reference for meat industry application of MSI technology, and also reflect the important research value of that.