Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning

Abstract In order to the quickly and nondestructively detect whether starch is adulterated in minced chicken meat, a novel method combining hyperspectral imaging (HSI) technique with transfer learning was proposed in this study. First of all, hyperspectral images of minced chicken meat with different mass fractions of starch were collected and spectral information of the samples in the range of 400.89–1000.19 nm was extracted. Then, the hyperspectral data was preprocessed via continuous wavelet transform (CWT), which transformed the pixel‐level hyperspectral data into two‐dimensional spectrograms. Furthermore, classification model for identifying starch in minced chicken meat was constructed based on the GoogLeNet network pretrained on ImageNet data set. Finally, the support vector machine (SVM) model and the convolutional neural network (CNN) model without transfer learning were established for comparison. The results indicated that the model based on GoogLeNet network had a higher classification accuracy, up to 98.6%. Therefore, this study demonstrates the feasibility of the detection of starch in minced chicken meat based on HSI technique and transfer learning. Practical applications The identification of starch in the minced chicken meat is of particular significance for maintaining the market order and safeguarding the human health. An innovative method based on hyperspectral imaging technique and transfer learning to identify whether the minced chicken meat mixed with starch was proposed in this study. The method achieved quickly, nondestructively and accurately detection of starch in minced chicken meat.