Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy

Abstract

To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.

A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy

“…To be more specific, water content detection for T. grandis kernels in a sustainable way could reduce the loss of the T. grandis kernels after picking and improve the economic benefits of the relevant industry. The sustainable testing technology of agricultural products and the sustainable development of agriculture would inevitably promote the sustainable development of humans [3].…”