AI Article Synopsis
- The study introduced a rapid and non-destructive technique using near-infrared spectroscopy to differentiate between various minnan oolong tea varieties.
- The researchers collected 210 samples from different plantations and acquired NIR spectra across specific wavelength ranges to analyze the tea types.
- Utilizing principal component analysis (PCA) for model creation, they found that the multiplicative scatter correction (MSC) improved classification accuracy to 96% for calibration samples and about 90% for prediction samples, highlighting the method's effectiveness and practical applications.
Article Abstract
The present paper presented a fast and non-destructive method for the discrimination of minnan oolong tea varieties by near-infrared spectroscopy technology. Two hundred ten samples including Tieguanyin, Huangjingui, Benshan, Maoxie and Meizhan were collected in different tea plantations of Minnan. NIR spectra of 1,100-1,300 nm and 1,640-2,498 nm were successfully obtained. Prediction model was built by principal component analysis (PCA), and the effects of multiplicative scatter correction (MSC) and standard normal variate (SNV) on the model were observed and compared. It was indicated that the effect of MSC on the model was superior for the effect of SNV because the classification accuracy of model for the calibration samples reached 96%, and this number to the prediction samples was about 90%. These results demonstrated that the near-infrared spectroscopy method established could be an efficient and accurate way for the discrimination of minnan oolong teas and would have a strong practical value.
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