Better milk safety control can offer important means to promote public health. However, few technologies can detect different types of contaminants in milk simultaneously. In this regard, the present work proposes a single-drop Raman imaging (SDRI) strategy for semiquantitation of multiple hazardous factors in milk solutions. By developing SDRI strategy that incorporates the coffee-ring effect (a natural phenomenon often presents in a condensed circle pattern after a drop evaporated) for sample pretreatment and discrete wavelet transform for spectra processing, the method serves well to expose typical hazardous molecular species in milk products, such as melamine, sodium thiocyanate and lincomycin hydrochloride, with little sample preparation. The detection sensitivity for melamine, sodium thiocyanate, and lincomycin hydrochloride are 0.1 mg kg, 1 mg kg, and 0.1 mg kg, respectively. Theoretically, we establish that the SDRI represents a novel and environment-friendly method that screens the milk safety efficiently, which could be well extended to inspection of other food safety.

Digital labeled Raman spectroscopy enables nondestructive detection of triclosan in hand soaps, revealing the feasibility of digital separation in practice.

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The discrimination of chicken spoilage is extremely important in order to guarantee the quality and safety of chicken before consumption, and requires an efficient and reliable method. We aimed to find a rapid and accurate method to discriminate the level of spoilage of chicken, and have developed a gas Fourier transform infrared spectroscopy (G‐FTIR) methodology based on analysing volatiles from chicken samples. In this method, the volatiles of chicken samples were first measured by the G‐FTIR spectrometer. After that, a chemometrics method combining principal component analysis (PCA) with support vector machine (SVM) was developed to encode the G‐FTIR spectra. With the combination of G‐FTIR spectra and the chemometrics method, a relationship between the spoilage levels of chicken and the G‐FTIR spectra of volatiles was established. As a result, the spoilage levels of chicken samples were accurately distinguished with a prediction accuracy of 100%, for samples stored at both normal (20 °C) and cold (4 °C) temperatures. The results obtained were correlated with the routine total volatile basis nitrogen (TVB‐N) method for validation. In addition, The G‐FTIR detection process for each sample could be performed within 3 min, requiring no chemical reagents. The G‐FTIR methodology performed well for the rapid discrimination of chicken spoilage, which could also be extended to the detection of other spoiled foods.

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