Beef Cut Classification Using Multispectral Imaging and Machine Learning Method

Ang Li; Chenxi Li; Moyang Gao; Si Yang; Rong Liu; Wenliang Chen; Kexin Xu

doi:10.3389/fnut.2021.755007

Beef Cut Classification Using Multispectral Imaging and Machine Learning Method

Front Nutr. 2021 Oct 20:8:755007. doi: 10.3389/fnut.2021.755007. eCollection 2021.

Authors

Ang Li^{1

2}, Chenxi Li^{1

2}, Moyang Gao², Si Yang^{1

2}, Rong Liu^{1

2}, Wenliang Chen^{1

2}, Kexin Xu^{1

2}

Affiliations

¹ State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China.
² School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.

Abstract

Classification of beef cuts is important for the food industry and authentication purposes. Traditional analytical methods are time constraints and incompatible with the modern food industry. Taking advantage of its rapidness and being nondestructive, multispectral imaging (MSI) has been widely applied to obtain a precise characterization of food and agriculture products. This study aims at developing a beef cut classification model using MSI and machine learning classifiers. Beef samples are imaged with a snapshot multi-spectroscopic camera within a range of 500-800 nm. In order to find a more accurate classification model, single- and multiple-modality feature sets are used to develop an accurate classification model with different machine learning-based classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) algorithms. The results demonstrate that the optimized LDA classifier achieved a prediction accuracy of over 90% with multiple modality feature fusion. By combining machine learning and feature fusion, the other classification models also achieved a satisfying accuracy. Furthermore, this study demonstrates the potential of machine learning and feature fusion method for meat classification by using multiple spectral imaging in future agricultural applications.

Keywords: beef cuts; classification; feature fusion; machine learning; multi-spectral imaging.