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.

The improvement of food safety and the reduction of food loss and waste require the development of new bioanalytical tools that provide chemical information about the composition of food that is of great value for improving traceability and extending the shelf life of food. Herein, a Cu-based metal-organic framework has been synthesized and immobilized onto cellulose paper disks for colorimetric and fluorescent detection and quantification of biogenic amines in food. The color of the nano metal-organic framework changes from green to brown in the presence of low amounts of biogenic amine vapors. Also, the fluorescence emission of the nano metal-organic framework greatly decreases after exposing the cellulose disks to amine vapors. The developed sensing paper disk exhibits a quick response to the presence of volatile biogenic amines, very low detection limits, and great selectivity. Also, the paper sensor was used for real-time monitoring of biogenic amines in bass samples at different temperature conditions, being a highly valuable method for evaluating food freshness and safeguarding food safety.

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments.

Breast milk is an optimal food that covers all the nutritional needs of the newborn. It is a dynamic fluid whose composition varies with lactation period. The neonatal units of hospitals have human milk banks, a service that analyzes, stores, and distributes donated human milk. This milk is used to feed premature infants (born before 32 weeks of gestation or weighing less than 1500 g) whose mothers, for some reason, cannot feed them with their own milk. Here, we aimed to develop near-infrared spectroscopy (NIRS) measures for the analysis of breast milk. For this purpose, we used a portable NIRS instrument scanning in the range of 1396–2396 nm to collect the spectra of milk samples. Then, different chemometrics were calculated to develop 18 calibration models with and without using derivatives and the standard normal variate. Once the calibration models were developed, the best treatments were selected according to the correlation coefficients (r²) and prediction errors (SECVs). The best results for the assayed macronutrients were obtained when no pre-treatment was applied to the NIR spectra of fat (r² = 0.841, SECV = 0.51), raw protein (r² = 0.512, SECV = 0.21), and carbohydrates (r² = 0.741, SECV = 1.35). SNV plus the first derivative was applied to obtain satisfactory results for energy (r² = 0.830, SECV = 9.60) quantification. The interpretation of the obtained results showed the richness of the NIRS spectra; moreover, the presence of specific bands for fat provided excellent statistics in quantitative models. These results demonstrated the ability of portable NIRS sensors in a methodology developed for the quality control of macronutrients in breast milk.

Aflatoxin M1 (AFM1) is one of the most widespread aflatoxins that can be present in the milk of lactating mammals. It can cause carcinogenicity, mutagenesis, teratogenesis, genotoxicity and immunosuppression. The WHO recommends reducing the AFM1 concentration in food products, so the European Commission has set a maximum allowable limit of 0.05 µg L⁻¹ in milk and its products. Thus, there is a need to develop new methodologies to satisfy the demand for reliable, cost-effective, robust and sensitive AFM1 routine controls. In the present work, a competitive phosphorescent immunosensor for AFM1 quantification in milk, based on antibody–antigen recognition and Mn:ZnS quantum dots (d-QDs) as photoluminescent labels, has been developed. Two different assay strategies based on the use of d-QDs as labels of secondary antibodies (direct assay), or of a derivative species of the antigen AFM1-Bovine Serum Albumin (indirect assay) were compared in terms of analytical performance for AFM1 quantification. The best analytical results were obtained with the immunoassay format that uses d-QDs as tags of secondary antibodies (direct assay), and said design was finally selected. The selected immunosensor provided a detection limit for AFM1 quantification of only 0.002 µg L⁻¹, which greatly satisfied the maximum tolerable limit of AFM1 in milk of 0.05 µg L⁻¹. The accuracy, calculated as recovery of AFM1 in fortified skimmed milk samples, ranged from 81 to 90%, with relative standard deviations from 3% to 14%. These results bring to light the good performance of such phosphorescent biosensors as simple and fast alternatives to conventional chromatographic analytical methods.

Ensuring food safety and quality has become a critical priority in response to increasing global demand and regulatory requirements. To address these challenges, the food industry is demanding sensitive, selective, and robust analytical strategies. Among these, non-destructive spectroscopic sensors (NDSS), particularly those based on vibrational spectroscopy such as near infrared (NIR) and Raman spectroscopy, have demonstrated significant potential for rapid, in situ analysis of food matrices without compromising sample integrity. Current research efforts are focused on the miniaturization and cost-effective design of spectroscopic instrumentation, enabling the development of portable devices suitable for real-time food monitoring across the supply chain. In parallel, advanced chemometric techniques and machine learning algorithms are revolutionizing spectral data interpretation, enhancing model calibration, transferability, and predictive reliability. Artificial intelligence approaches in spectroscopic workflows facilitate the extraction of meaningful patterns from large and complex spectral datasets. Together, these advanced vibrational proposals are converging toward the realization of intelligent, real-time decision-making systems that support sustainable, efficient, and safe food production and distribution, redefining the standards of food quality control in modern agri-food systems. Graphical abstract