Abstract: The study aimed to develop an approach to quantify pork in adulterated mutton rolls with different fat ratios based on near infrared spectroscopy combined with chemometrics. A total of 324 samples were prepared with different proportions of fat and pork. Spectra were collected by using a near infrared spectrometer. Savitzky-Golay (SG) smoothing, Savitzky-Golay smoothing-first derivation (SG-1st), Savitzky-Golay smoothing-second derivative (SG-2st), multiplicative scatter correction (MSC), centralized correction (Center), and standard normal variate (SNV) were used to preprocess the original spectral data. A prediction model was established by using partial least squares regression (PLSR) method. Based on the prediction parameters, the optimal pretreatment method was SG-1st. Competitive adaptive weighted sampling (CARS) was used to select the optimal wavebands in order to further enhance the prediction capability of the model. The determination coefficients for calibration and validation sets were 0.983 6 and 0.972 5, respectively. The root mean square errors of calibration and validation (RMSEC and RMSEP) were 0.043 7 and 0.057 7, the ratio of performance to standard deviate (RPD) was 7.62, and the correlation coefficient between the CARS model prediction and the experimental data for test set was 0.913 8. The results showed that by using near infrared spectral analysis, the proportion of pork in adulterated mutton rolls with different fat ratios could be rapidly quantified.

Key words: near infrared spectroscopy, mutton rolls, pork adulteration, quantitative detection, characteristic spectra