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Residues of veterinary antibiotics in honey may be damaging to human health. Surface-enhanced Raman scattering spectroscopy (SERS) is an emerging technology widely applied in food safety. SERS has advantages of enabling fingerprint identification and fast detection, as well as does not require complex pretreatment. Considering the overuse of nitrofurans in honeybee breeding, SERS combined with spectral preprocessing was used to detect nitrofurantoin in honey. By using standardized experimental procedures and improved spectral correction methods, the lowest detection limit of nitrofurantoin was 0.1321 mg/kg. A good linear relationship in the partial least squares regression model was found among spiked samples, which allowed prediction of nitrofurantoin content in honey sample (𝑅 2 𝐶 = 0.9744; 𝑅 2 𝑃 = 0.976; RMSECV = 1.0353 mg/kg; RMSEP = 0.9987 mg/kg). Collectively, these results reliably demonstrated that quantification is more accurate when spectral preprocessing is better controlled. Therefore, this study indicates that SERS could be further implemented in fast and onsite detection of nitrofurantoin in honey for improved food safety.

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Free fatty acids (FFAs) are an important indicator of the freshness and quality of rice. In this study, the vibration response of C-H chemical bonds (-CH 3 , -CH 2 , H-C = C-H) of FFAs in the near-infrared region was determined by analyzing the standard reagent. In addition, the spectral data of different physical forms of rice and chemometrics, such as partial least squares (PLS), synergy interval-PLS, and competitive adaptive reweighted sampling (CARS), were applied to develop an optimal regression model for rice FFAs determination. The performance of the FFAs model established by using the polished rice granule spectrum (PRG) combined with CARS was the best, the correlation coefficients of the calibration set and prediction set were 0.99 (root mean squared errors of the calibration = 2.00 mg/100 g) and 0.98 (root mean squared errors of the prediction = 3.21 mg/100 g), respectively, and the ratio of performance-to-deviation was 4.50. Compared with the rice powder spectral, the PRG spectral can better retain the information of FFAs. The result shows that NIRS can rapidly, nondestructively, and accurately detect FFAs in rice granules, which will help rice business and food regulatory authorities to establish an early warning mechanism of rice aging.Practical Application: Free fatty acids (FFAs) in rice are an important indicator for evaluating the freshness of rice, and their high responsiveness to the deterioration of rice quality. The real-time detection of FFAs in rice can timely adjust the parameters of the rice storage environment, which is very meaningful to ensure the quality of rice.