Rapid Multi‐Indicator Quality Evaluation of Ginger Using Genetic Algorithm and Near‐Infrared Spectroscopy

To achieve rapid and comprehensive evaluation of the quality of ginger, a rapid multi‐indicator quality evaluation method based on genetic algorithm and near‐infrared spectroscopy technology is proposed to detect the content of multiple compounds (6‐gingerol, 8‐gingerol, 10‐gingerol, 6‐shogaol, and zingerone). First, the near‐infrared spectra of ginger samples is collected. Then, the spectra is preprocessed to reduce the noise. Next, features of the spectra are extracted through the genetic algorithm where the population initialization and fitness function methods are designed. Finally, the prediction model is generated through regression. Experimental results demonstrate that the proposed method achieves higher R values (0.9052, 0.9107, 0.9269, 0.9843, 0.9030) compared to the traditional PLSR model (0.6666, 0.51, 0.4358, 0.9248, 0.4846) for 6‐gingerol, 8‐gingerol, 10‐gingerol, 6‐shogaol, and zingerone, respectively. Therefore, the proposed method can reduce prediction errors and improve the performance of near‐infrared spectroscopy quantitative analysis model for ginger.