Meat adulteration is a worldwide concern. In this paper, a new droplet digital PCR (ddPCR) method was developed for the quantitative determination of the presence of chicken in sheep and goat meat products. Meanwhile, a constant (multiplication factor) was introduced to transform the ratio of copy numbers to the proportion of meats. The presented ddPCR method was also proved to be more accurate (showing bias of less than 9% in the range from 5% to 80%) than real-time PCR, which has been widely used in this determination. The method exhibited good repeatability and stability in different thermal treatments and at ultra-high pressure. The relative standard deviation (RSD) values of 5% chicken content was less than 5.4% for ultra-high pressure or heat treatment. Moreover, we confirmed that different parts of meat had no effect on quantification accuracy of the ddPCR method. In contrast to real-time PCR, we examined the performance of ddPCR as a more precise, sensitive and stable analytical strategy to overcome potential problems of discrepancies in amplification efficiency discrepancy and to obtain the copy numbers directly without standard curves. The method and strategy developed in this study can be applied to quantify the presence and to confirm the absence of adulterants not only to sheep but also to other kinds of meat and meat products.

Currently, the authentication of camellia oil (CAO) has become very important due to the possible adulteration of CAO with cheaper vegetable oils such as rapeseed oil (RSO). Therefore, we report a Fourier transform infrared (FTIR) spectroscopic method for detecting the authenticity of CAO and quantifying the blended levels of RSO. In this study, two characteristic spectral bands (1119 cm⁻¹ and 1096 cm⁻¹) were selected and used for monitoring the purity of CAO. In combination with principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) analysis, qualitative and quantitative methods for the detection of camellia oil adulteration were proposed. The results showed that the calculated I₁₁₁₉/I₁₀₉₆ intensity ratio facilitated an initial check for pure CAO and six other edible oils. PCA was used on the optimized spectral region of 1800–650 cm⁻¹. We observed the classification of CAO and RSO as well as discrimination of CAO with RSO adulterants. LDA was utilized to classify CAO from RSO. We could differentiate and classify RSO adulterants up to 1% v/v. In the quantitative PLSR models, the plots of actual values versus predicted values exhibited high linearity. Root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of the PLSR models were 1.4518–3.3164% v/v and 1.7196–3.8136% v/v, respectively. This method was successfully applied in the classification and quantification of CAO adulteration with RSO.