Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics

Safeguarding Public Health: Advanced Detection of Food Adulteration Using Nanoparticle-Based Sensors

Food adulteration, whether intentional or accidental, poses a significant public health risk. Traditional detection methods often lack the precision required to detect subtle adulterants that can be harmful. Although chromatographic and spectrometric techniques are effective, their high cost and complexity have limited their widespread use. To explore and validate the application of nanoparticle-based sensors for enhancing the detection of food adulteration, focusing on their specificity, sensitivity, and practical utility in the development of resilient food safety systems. This study integrates forensic principles with advanced nanomaterials to create a robust detection framework. Techniques include the development of nanoparticle-based assays designed to improve the detection specificity and sensitivity. In addition, sensor-based technologies, including electronic noses and tongues, have been assessed for their capacity to mimic and enhance human sensory detection, offering objective and reliable results. The use of nanomaterials, including functionalized nanoparticles, has significantly improved the detection of trace amounts of adulterants. Nanoparticle-based sensors demonstrate superior performance in terms of speed, sensitivity, and selectivity compared with traditional methods. Moreover, the integration of these sensors into food safety protocols shows promise for real-time and onsite detection of adulteration. Nanoparticle-based sensors represent a cutting-edge approach for detecting food adulteration, and offer enhanced sensitivity, specificity, and scalability. By integrating forensic principles and nanotechnology, this framework advances the development of more resilient food-safety systems. Future research should focus on optimizing these technologies for widespread application and adapting them to address emerging adulteration threats.

Application, challenges and future prospects of recent nondestructive techniques based on the electromagnetic spectrum in food quality and safety

Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.

Fingerprinting based on spectral reflectance and chemometrics - An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon

The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.

Discriminating geographical origins and determining active substances of water caltrop shells through near-infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the geographical origins of the water caltrop shells from different regions of China. Two active substances, the total phenolic content (TPC) and total flavonoid content (TFC) in the water caltrop shells were determined through the technique as well. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was adopted to build the geographical discriminant model. Quantitative analysis models of TPC and TFC were built using partial least squares (PLS) regression. 1st derivative and randomization test (RT) methods were used to optimize the quantitative analysis models. It was found that the geographical discriminant model can correctly recognize the water caltrop shells from different regions of China with a total accuracy of 93.33%. The values of TPC and TFC obtained by the optimized models and the standard method are close. The coefficient of determination (R2) and the ratio of prediction to deviation for the two substances were 0.91, 0.89 and 3.02, 3.02, respectively. The results demonstrated the feasibility of NIRS combined with chemometric methods for the geographical discrimination of water caltrop shells and the quantitative analysis of TPC and TFC in water caltrop shells.