Identification of Chicken-Derived Ingredients as Adulterants Using Loop-Mediated Isothermal Amplification

Recent advancements with loop-mediated isothermal amplification (LAMP) in assessment of the species authenticity with meat and seafood products

Species adulteration or mislabeling with meat and seafood products could negatively affect the fair trade, wildlife conservation, food safety, religion aspect, and even the public health. While PCR-based methods remain the gold standard for assessment of the species authenticity, there is an urgent need for alternative testing platforms that are rapid, accurate, simple, and portable. Owing to its ease of use, low cost, and rapidity, LAMP is becoming increasingly used method in food analysis for detecting species adulteration or mislabeling. In this review, we outline how the features of LAMP have been leveraged for species authentication test with meat and seafood products. Meanwhile, as the trend of LAMP detection is simple, rapid and instrument-free, it is of great necessity to carry out end-point visual detection, and the principles of various end-point colorimetry methods are also reviewed. Moreover, with the aim to enhance the LAMP reaction, different strategies are summarized to either suppress the nonspecific amplification, or to avoid the results of nonspecific amplification. Finally, microfluidic chip is a promising point-of-care method, which has been the subject of a great deal of research directed toward the development of microfluidic platforms-based LAMP systems for the species authenticity with meat and seafood products.

Cleavable molecular beacon-based loop-mediated isothermal amplification assay for the detection of adulterated chicken in meat

A simple, sensitive, specific and fast method based on the loop-mediated isothermal amplification (LAMP) technique and cleavable molecular beacon (CMB) was developed for chicken authentication detection. LAMP and CMB were used for DNA amplification and amplicon analysis, respectively. Targeting the mitochondrial cytochrome b gene of chickens, five primers and one CMB probe were designed, and their specificity was validated against nine other animal species. The structure of CMB and concentrations of dNTPs, MgSO₄, betaine, RNase H2, primers and CMB were optimized. The CMB-LAMP assay was completed within 17 min, and its limit of detection for chicken DNA was 1.5 pg μL^-1. Chicken adulteration as low as 0.5% was detected in beef, and no cross-reactivity was observed. Finally, this assay was successfully applied to 20 commercial meat products. When combined with our developed DNA extraction method (the extraction time was 1 min: lysis for 10 s, washing for 20 s and elution for 30 s), the entire process (from DNA extraction to results analysis) was able to be completed within 20 min, which is at least 10 min shorter than other LAMP-based methods. Our method showed great potential for the on-site detection of chicken adulteration in meat.

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

Rapid Full-Cycle Technique to Control Adulteration of Meat Products: Integration of Accelerated Sample Preparation, Recombinase Polymerase Amplification, and Test-Strip Detection

Verifying the authenticity of food products is essential due to the recent increase in counterfeit meat-containing food products. The existing methods of detection have a number of disadvantages. Therefore, simple, cheap, and sensitive methods for detecting various types of meat are required. In this study, we propose a rapid full-cycle technique to control the chicken or pig adulteration of meat products, including 3 min of crude DNA extraction, 20 min of recombinase polymerase amplification (RPA) at 39 °C, and 10 min of lateral flow assay (LFA) detection. The cytochrome B gene was used in the developed RPA-based test for chicken and pig identification. The selected primers provided specific RPA without DNA nuclease and an additional oligonucleotide probe. As a result, RPA-LFA, based on designed fluorescein- and biotin-labeled primers, detected up to 0.2 pg total DNA per μL, which provided up to 0.001% w/w identification of the target meat component in the composite meat. The RPA-LFA of the chicken and pig meat identification was successfully applied to processed meat products and to meat after heating. The results were confirmed by real-time PCR. Ultimately, the developed analysis is specific and enables the detection of pork and chicken impurities with high accuracy in raw and processed meat mixtures. The proposed rapid full-cycle technique could be adopted for the authentication of other meat products.

Omics-Based Analytical Approaches for Assessing Chicken Species and Breeds in Food Authentication

Chicken is known to be the most common meat type involved in food mislabeling and adulteration. Establishing a method to authenticate chicken content precisely and identifying chicken breeds as declared in processed food is crucial for protecting consumers' rights. Categorizing the authentication method into their respective omics disciplines, such as genomics, transcriptomics, proteomics, lipidomics, metabolomics, and glycomics, and the implementation of bioinformatics or chemometrics in data analysis can assist the researcher in improving the currently available techniques. Designing a vast range of instruments and analytical methods at the molecular level is vital for overcoming the technical drawback in discriminating chicken from other species and even within its breed. This review aims to provide insight and highlight previous and current approaches suitable for countering different circumstances in chicken authentication.