Rapid Detection of Freshness in Tan-Lamb Mutton Based on Hyperspectral Imaging Technology

The freshness of Tan mutton is an important index of its quality and safety, and it is also a key link in the quality control of meat products. Total Volatile Basic Nitrogen (TVB-N) is the main chemical information which can effectively reflect the loss of freshness of Tan mutton. However, the traditional detection method of TVB-N must destroy the samples, the detection process is tedious, the man-made influencing factors are large, and the test result is lack of objectivity and consistency. Hyperspectral imaging technology which is a non-destructive method meets the needs of modern detection technologies for multi-source information fusion that has been widely used in the field of food safety. This paper used visible/near-infrared spectroscopic imaging technology (400 similar to 1000 nm) combined with dynamics and chemometrics methods and computer programming to achieve the rapid detection of TVB-N concentration and prediction of safe storage period during the storage period of Tan mutton(15 degrees C). The research contents were as follows: The average spectral data for each sample area of interest were extracted and the monte carlo algorithm was selected to eliminate the abnormal samples. The X-Y symbiotic distance (Sample set partitioning based on joint X-Y distances, SPXY) was used to divide the mutton set into the correction set and the prediction set. Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG), Standard Normalized Variate(SNV), normalization (Normalization) and baseline calibration (Baseline) were used to preprocess the original spectral data. 21 and 6 feature wavelengths were extracted by the Campetitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). In order to simplify the model and improve the accuracy of prediction of the model, the SPA algorithm was used to perform secondary extraction of selected feature wavelengths of CARS and 14 feature wavelengths were selected. A PLSR model with TVB-N concentration was established based on the extracted characteristic wavelengths, and the SNV-CARS-SPA-PLSR model was preferred to have a higher prediction ability (R-c(2)=0.88, RMSEC=2.51, R-p(2)=0.65, RMSEP=2.11) Meanwhile, a dynamic model of mutton TVB-N change and storage time could be established. Finally, the dynamic model of spectral absorbance value and storage time of mutton were established by combining the optimized spectral model with the dynamic first order reaction model, and predicte the storage time, and the PLS-DA model was realized to discriminate the storage time of mutton (the correction set discriminant accuracy rate was 100%, and the prediction set is 97%). The result showed that visible/near-infrared hyperspectral imaging technology in combination with dynamics and chemometrics methods and computer programming could effectively detect TVB-N index of mutton rapidly and non-destructively, and be realized to monitor the quality and safety of mutton and provide a theoretical reference for developing on line defection equipment.