Non-destructively sensing pork’s freshness indicator using near infrared multispectral imaging technique

• Multispectral imaging system based on near infrared bands was developed.
• Pork freshness was successfully evaluated by NIR multispectral imaging.
• A novel AdaBoost + BPANN algorithm was used for model calibration.

Total volatile basic nitrogen (TVB-N) content is one of core indicators for evaluating pork’s freshness. This paper attempted to non-destructively sensing TVB-N content in pork meat using near infrared (NIR) multispectral imaging technique (MSI) with multivariate calibration. First, a MSI system with 3 characteristic wavebands (i.e. 1280 nm, 1440 nm and 1660 nm) was developed for data acquisition. Then, gray level co-occurrence matrix (GLCM) was used for characteristic extraction from multispectral image data. Next, we proposed a novel algorithm for modeling-back propagation artificial neural network (BP-ANN) and adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, and we compared it with two commonly used algorithms. Experimental results showed that the BP-AdaBoost algorithm is superior to others with the root mean square error of prediction (RMSEP) = 6.9439 mg/100 g and the correlation coefficient (R) = 0.8325 in the prediction set. This work sufficiently demonstrated that the MSI technique has a high potential in non-destructively sensing pork freshness, and the nonlinear BP-AdaBoost algorithm has a strong performance in solution to a complex data processing.