Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation

• Hyperspectral imaging for non-destructive beef eating quality detection is studied.
• Large samples were collected in abattoir production line under industry conditions.
• Beef tenderness and pH value were predicted using support vector machine.
• Singular spectrum analysis was proposed to remove instrumental noise of HSI system.
• Improved prediction performance was achieved by combining SSA in HSI analysis.

Detecting beef eating quality in a non-destructive way has been popular in recent years. Among various non-destructive assessing methods, the feasibility of hyperspectral imaging (HSI) system was investigated in this paper. Hyperspectral images of beef samples were collected in an abattoir production line and used for predicting the beef tenderness and pH value. Support vector machine (SVM) was applied to construct the prediction equation. Before utilizing the original HSI spectral profiles directly, we propose to use singular spectrum analysis (SSA) as a pre-processing approach, where SSA has been proven to be an effective technique for time-series analysis in diverse applications. The results indicate that SSA can remove the instrumental noise of HSI system effectively and therefore improve the prediction performance.