Improvement of predicting precision of oil content in instant noodles by using wavelet transforms to treat near-infrared spectroscopy Original Research Article

Near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, long-range detection, high precision and reliable detection data, etc., is a new and popular quantitative and qualitative analysis method. Whether or not NIRS can be used in practical production, depends on its predicting precision. The oil content of instant noodles was inspected by NIRS and analyzed by wavelet transform, which is a new development in signal treatment method in recent years. The results show that the spectra treated with wavelet transform indicate more effectively the relationship with oil content in instant noodles. Compared with initial, first and second derivatives spectra, wavelet transform of eight-size has the most marked relation with oil content. The predicting precision of four-element regression is the best, with relative error lower by 1.25% and 1.1% and standard error lower by 0.26 and 0.18 than those of initial and second derivative spectra. Therefore, the conclusion of improved predicting precision for quantitative detection of oil content in instant noodles with wavelet transform can be drawn.