Inorganic nanoparticles are a fascinating class of materials which promise great potential in numerous fields, including optical (bio)sensing. Many different kinds of such nanoparticles have been widely used for fluorescent sensing and imaging due to the different merits of fluorescent nanoparticles compared to molecular fluorophores. Progress made in the rational design of nanomaterials also allowed the synthesis of hybrid phosphorescent nanoparticles, that finds growing applications in sensing due to the combination of the interesting size- and shape-dependent properties of nanomaterials with a phosphorescence-type emission. In this review, we intend to highlight some of progress made in this active research area and update the database of various phosphorescent nanoparticles-based sensors on the basis of different sensing targets of interest in environmental, industrial and biomedical areas. Following an introduction and a discussion of merits of the synergy between nanomaterials and phosphorescence detection as compared to molecular luminophores the article assesses the kinds and specific features of nanomaterials often used in phosphorescence sensing. Specific examples on the use of phosphorescence nanoparticles in chemical sensing and bioimaging are given next. A final section intends to provide an overview of the prospects of such type of nanomaterials in the design of future devices for analytical chemistry.

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.