Classification of Chinese hickory with different aging times using two-dimensional correlation spectral (2DCOS) images combined with transfer learning

[Display omitted] •Integrated 2DCOS with transfer learning and HSI for hickory nut classification based on aging.•Synchronous 2DCOS outperformed asynchronous, and integrative 2DCOS.•Transfer learning models presented good performance in classifying aged hickory nut.•Spectral preprocessing and variable selection enhance the performance. The Chinese hickory (Carya cathayensis Sarg.), highly valued in eastern China for its culinary appeal and nutritional value, is prone to rancidity during storage due to its high content of polyunsaturated fatty acids. This research introduces an innovative method for categorizing Chinese hickory nuts according to their aging durations by integrating two-dimensional correlation spectroscopy (2DCOS) with transfer learning, using hyperspectral imaging technology (HSI). The study captured hyperspectral images of 1,200 hickory kernels at various aging stages, extracting spectral data within the 900–––1670 nm range. Using this spectral data, synchronous, asynchronous, and integrative 2DCOS images were generated. The analytical framework comprised five spectral preprocessing methods:first derivative (FD), second derivative (SD), multiplicative scatter correction (MSC), standard normal variate (SNV), and detrend (DET); four wavelength selection techniques:successive projections algorithm (SPA), iteratively retaining information variables (IRIV), variable combination population analysis (VCPA), and iteratively variable subset optimization (IVSO); five transfer learning models:DarkNet19, DarkNet53, ResNet50, InceptionV3 and Xception; along with two traditional approaches: support vector machine (SVM) and linear discriminant analysis (LDA). This thorough strategy was designed to accurately classify the nuts on the basis of their aging time. The findings revealed that synchronous 2DCOS significantly outperformed asynchronous and integrative forms in classifying aged hickory kernels. The FD-IVSO-2DCOS-DarkNet19 model, employing 292 (62.80 %) wavelength variables, reached an impressive accuracy of 98.75 % in the test set, surpassing the SD-SPA-2DCOS-DarkNet53 model, which used only 40 (8.6 %) wavelength variables and achieved an accuracy of 97.92 %. These models excelled over other 2DCOS approaches that utilized the full wavelength range and significantly outdid the conventional one-dimensional spectral LDA and SVM models, regardless of wavelength scope. The study underscores that synchronous 2DCOS images, when combined with suitable tran