A novel strategy of "pick the best of the best" for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy. Issue 8 (19th June 2021) - Li, Jiayi
Abstract: In this paper, a novel strategy of "pick the best of the best" was proposed for the nondestructive identification of different‐origin and adulterated Poria cocos with near‐infrared spectroscopy. First, various preprocessing methods were divided into three classes: baseline correction, scattering and trend correction, and scaling. The single preprocessing methods with the best predictions in each class were selected. Then, the selected preprocessing methods were combined in pairs according to three classes. The pair combination preprocessing methods with the best predictions and also better predictions than single methods were selected. Finally, the selected pair combination preprocessing method was combined with the methods in the unselected class. The three combination preprocessing methods with the best predictions and also better predictions than pair combination methods were selected as the final prediction. With this strategy, the optimized preprocessing combination can be obtained quickly, and the identification accuracy with principal component analysis method can be greatly improved. 0% identification accuracy of adulterated samples and 12.5% identification accuracy of different‐origin samples were obtained with the raw data. However, 100% accuracy of adulterated samples, 93.8% accuracy of calibration dataset, and 75% accuracy of validation dataset can be obtained with the novel strategy. The developed technology can be regarded as a simple, rapid, and accurate nondestructive identification method for different‐origin and adulterated samples, and has a broad application prospect in the future. Abstract : A novel strategy of 'pick the best of the best' was proposed for the nondestructive identification of different‐origin and adulterated Poria cocos with near infrared spectroscopy. 100% accuracy of adulterated samples, 93.8% accuracy of calibration dataset and 75% accuracy of validation dataset can be obtained with the novel strategy.