Marta Valledor

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.

There is an increasing need to establish reliable safety controls in the food industry and protect public health. Consequently, there are numerous efforts to develop sensitive, robust and selective analytical strategies. As regulatory requirements for food and the concentration or target bi-omarkers 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 are: (1) the min-iaturization 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 spatially screening of quality and contamination; (4) the stronger in-tegration 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 trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments.

There is a growing need for portable and handheld instruments that can facilitate rapid, efficient on-site screening for food contaminants. Several alternatives exist for developing analytical methods to meet these needs; however, they often rely on expensive and powerful laboratory equipment. Recent developments in chemical analysis focus on the use of nanomaterials which, due to their ultra-small size and special properties, offer a highly sensitive and selective response. This paper presents, for the first time, the results of an affordable, portable colour measurement device based on the use of colour sensors and nanostructured metal-organic frameworks (nanoMOFs) for on-site food safety detection. The combination of nanoMOFs and colour sensors provides a highly sensitive, selective and user-friendly approach to detecting contaminants in food. By exploiting the tunable properties of nanoMOFs and the simplicity of colour detection, this device holds great promise for improving food safety monitoring, especially in low-resource settings. It has been successfully tested for in situ detection of loss of freshness of real shrimp samples, yielding excellent results. Additionally, an Internet of Analytical Things (IoAT) architecture has been proposed to extend the capabilities of the colourimetric sensor.

MicroRNAs (miRNAs) represent a class of small noncoding RNAs that are considered a novel emerging class of disease biomarkers in a variety of afflictions. Sensitive detection of miRNA is typically achieved using hybridization-based methods coupled with genetic amplification techniques. Although their sensitivity has improved, amplification techniques often present erroneous results due to their complexity. In addition, the use of these techniques is usually linked to the application of protein enzymes, the activity of which is dependent on the temperature and pH of the medium. To address these drawbacks, an alternative genetic enzyme for the highly sensitive detection of miRNAs is proposed in this work. Multicomponent nucleic acid enzymes (MNAzymes), coupled with the use of DNA-functionalized gold nanoparticles (AuNPs), were used in this study to develop an isothermal signal amplification strategy for visual genetic detection. miR146a, a biomarker of bovine mastitis present in milk, was selected as a model analyte. The developed methodology is easily carried out in 80 min at 50 °C, generating a low visual limit of detection of 250 pM based on the observation of a color change. The methodology was successfully applied to the detection of miR146a in raw cow milk samples. Graphical Abstract

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.

MicroRNAs (miRNAs) have recently emerged as a new class of biomarkers for disease diagnosis and play vital roles in physiological and pathological processes. Here, a novel fiber-optic prototype that allows synchronized optical transmittance measurements at different wavelengths has been designed and fabricated for selective and sensitive miRNAs detection based on a ratiometric colorimetric strategy. The proposed measurement method automatically adjusts the output light power through the LEDs, minimizing eventual differences in brightness and potential losses through the optical fiber without the need expensive optical components. The prototype has been validated for the detection of miRNAs identified as biomarkers of bovine mastitis in dairy cattle. Observation of the color changes from red to blue of the AuNP solution, and therefore on the transmittance signal, caused by miRNA-induced gold nanoparticles (AuNPs), aggregation has been used for rapid detection of such genetic biomarker. The equipment designed and constructed is portable, being the main parameters of the measurement managed with a smartphone, and it could be potentially employed with any other biosensor whose detection strategy is based on changes in the distance between AuNPs.

There is a growing interest in the development of sensitive, portable, and low-cost instrumentation for optical chemical (bio)sensing. Such instrumentation can allow real-time decision-making for industry, farmers, and researchers. The combination of optical fiber schemes, luminescence spectroscopy techniques, and new materials for sensor immobilization has allowed the growth of optical sensors. This article focuses on the development of low-cost optoelectronic instrumentation and measurement strategies for optical chemical (bio)sensing. Most of the articles in this field have focused on the chemical sensors themselves, although few have covered the design process for optoelectronic instrumentation. This article tries to fill this gap by presenting designs for real applications, as carried out by the authors. We also offer an introduction to the optical devices and optical measurement techniques used in this field to allow a full understanding of the applications.

A portable instrumental system was designed for the routine environmental monitoring of toxic volatile organic compounds (VOCs) in atmospheric conditions based on changes in the photoluminescence emission of semiconductor nanoparticles (quantum dots) entrapped in a sol-gel matrix as the solid sensing material. The sol-gel sensing material displayed a long-lived phosphorescent emission, which is quenched in the presence of trace levels of a volatile organic compound (acetone) in gaseous atmospheres. The developed instrument could measure and process the changes in the photoluminescence of the sensing material after exposure to gaseous acetone. The developed prototype device consists of a deep-ultraviolet ligtht-emitting diode (UV LED), which excites the chemical sensing material; an optical filter to remove scattered light and other non-desirable wavelengths; a photomultiplier tube (PMT) to convert the phosphorescence emission of the sensor phase to an electrical signal; and a microcontroller to correlate the signal with the analyte concentration. The developed prototype was evaluated for its ability to measure low levels of gaseous acetone in contaminated atmospheres with high sensitivity (detection limit: 9 ppm). The obtained results show the feasibility of this type of instrument for environmental analytical control purposes.

Gold nanoparticles of different sizes have been synthesized and surface-functionalized with selected RNA probes in order to develop a rapid, low-cost and sensitive method for detection of microRNA146a (miR146a). The strategy is based on the change of colour that can be observed visually after aggregation of the RNA modified-gold nanoparticles (AuNPs) in presence of miR146a. Experimental conditions have been carefully selected in order to obtain a good sensitivity that allows to perform visual detection of microRNA at the nM level, achieving a detection limit of 5 nM. Good repeatability and selectivity versus other sequences that only differ from miR146a in 3 bases was achieved. miR146a has been described as one of the main microRNA involved in the immune response of bovine mastitis, being expressed in tissue, blood and milk samples. The method was successfully applied to the detection of miR146a in raw cow milk samples. The present scheme constitutes a rapid and low-cost alternative to perform highly sensitive detection of microRNA without the need of instrumentation and amplification steps for the early detection of bovine mastitis in the agrofood industry. Schematic representation of the assay based on aggregation of RNA-modified gold nanoparticles (blue) in presence of microRNA146a generating a dark blue spot onto a solid support, versus a pink spot observed in absence of miR146a due to dispersed gold nanoparticles (red).