Smartphone-Based Food Diagnostic Technologies: A Review

Towards citizen science in food chemistry: Nitrite detection in high-fat food by integrating solid-phase extraction and electromembrane extraction on a microfluidic paper-based sensor

Citizen science-based food quality survey is a new demand in monitoring and management of food quality with development of preventive medicine. In this work, a point-of-care testing (POCT) method was developed specifically for end users to detect nitrite in high-fat food. To avoid the preparation of complex samples in laboratory, a microfluidic paper-based analytical device (μPAD) integrated with solid-phase extraction (SPE) for fat removal and electromembrane extraction (EME) for nitrite separation in high-fat food samples was constructed. For same purpose, a laboratory-free pretreatment method for high-fat food was established, and smartphone-assisted colorimetric sensor was used as a detector for image capture and data analysis. The POCT method constructed specifically for end users showed a limit of detection (LOD) of 1.1 mg/kg. Overall, this paper provides new insights into citizen science as a valuable resource tool for researchers, policymakers, and public interested in understanding and addressing the challenges in food chemistry.

Bio-nanoparticles sensor couple with smartphone digital image colorimetry and dispersive liquid-liquid microextraction for aflatoxin B1 detection

A novel nanobiosensor-based colorimetric method was developed by integrating ZnO nanoparticles functionalized with curcumin, dispersive liquid-liquid microextraction (DLLME), and smartphone digital image colorimetry for the sensitive detection of aflatoxin B1 (AFB1) in baby food samples. The unique combination of biologically-derived ZnO nanoparticles with curcumin created a sensing platform, while DLLME provided efficient pre-concentration of the target analyte. A custom-designed portable colorimetric box enabled standardized image capture and analysis using a smartphone camera and colorimetric software. Under optimized conditions using chloroform as the extraction solvent and acetonitrile as the disperser solvent, the method achieved a remarkable limit of detection of 0.09 μg/kg within linear concentration range of 0-1 μg/L. The calibration curves demonstrated excellent linearity (R2 > 0.9906) with high precision (RSD < 5.52%). The method was successfully validated using baby food samples, achieving high recoveries (89.8-94.2%). This innovative integration of nanobiosensing, microextraction, and smartphone technology offers a rapid, highly sensitive, and cost-effective platform for on-site AFB1 detection in food safety applications, particularly beneficial for resource-limited settings.

Green Microextraction, Paper-Based Technique, and Smartphone Sensing for Sustainable Determination of Nicotinamide in Pharmaceuticals and Blood Samples

This study presents the development of two simple, sensitive, and selective microextraction and preconcentration procedures for determining nicotinamide (NAM) in pharmaceutical formulations and blood samples. The methods utilize the König reaction, where cyanogen chloride, formed by reacting potassium thiocyanate with sodium hypochlorite in an acidic medium, interacts with NAM and couples with buffered barbituric acid at pH 3.5 to form a pink polymethine dye. This dye is extracted into an isobutanol layer using tetrabutylammonium bromide and exhibits maximum absorption at 560 nm for spectrophotometric quantification. In the smartphone paper-based (SPB) method, the dye is applied to a paper strip, captured by a smartphone, and analyzed for red, green, and blue components. Both methods were thoroughly evaluated for key parameters, demonstrating adherence to Beer's law over a NAM concentration range of 1.0-7.8 µg/mL, with limits of detection of 0.76 µg/mL for the spectrophotometric method and 0.91 µg/mL for the SPB method. Our SPB combines paper-based analysis, smartphone detection, and microextraction for the first time, offering a cost-effective, portable, and sensitive solution for NAM assay in plasma. A comparison with the established high-performance liquid chromatography method, using statistical tests, showed no significant differences, confirming the reliability of these novel methods.

An update on the sample preparation and analytical methods for synthetic food colorants in food products

Colorants, especially synthetic colorants, play a crucial role in enhancing the aesthetic qualities of food owing to their cost-effectiveness and stability against environmental factors. Ensuring the safe and regulated use of colorants is essential for maintaining consumer trust in food safety. Various preparation and analytical technologies, which are continuously undergoing improvement, are currently used to quantify of synthetic colorants in food products. This paper reviews recent developments in analytical techniques for synthetic food colorants, detection and compares the operational principles, advantages, and disadvantages of each technology. Additionally, it also explores advancements in these technologies, discussing several invaluable tools of analysis, such as high-performance liquid chromatography, liquid chromatography-tandem mass spectrometry, electrochemical sensors, digital image analysis, near-infrared spectroscopy, and surface-enhanced Raman spectroscopy. This comprehensive overview aims to provide valuable insights into current progress and research in the field of food colorant analysis.

Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications

The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.

Microbiological and chemical hazards in cultured meat and methods for their detection

Cultured meat, which involves growing meat in a laboratory rather than breeding animals, offers potential benefits in terms of sustainability, health, and animal welfare compared to conventional meat production. However, the cultured meat production process involves several stages, each with potential hazards requiring careful monitoring and control. Microbial contamination risks exist in the initial cell collection from source animals and the surrounding environment. During cell proliferation, hazards may include chemical residues from media components such as antibiotics and growth factors, as well as microbial issues from improper bioreactor sterilization. In the differentiation stage where cells become muscle tissue, potential hazards include residues from scaffolding materials, microcarriers, and media components. Final maturation and harvesting stages risk environmental contamination from nonsterile conditions, equipment, or worker handling if proper aseptic conditions are not maintained. This review examines the key microbiological and chemical hazards that must be monitored and controlled during the manufacturing process for cultured meats. It describes some conventional and emerging novel techniques that could be applied for the detection of microbial and chemical hazards in cultured meat. The review also outlines the current evolving regulatory landscape around cultured meat and explains how thorough detection and characterization of microbiological and chemical hazards through advanced analytical techniques can provide crucial data to help develop robust, evidence-based food safety regulations specifically tailored for the cultured meat industry. Implementing new digital food safety methods is recommended for further research on the sensitive and effective detection of microbiological and chemical hazards in cultured meat.

A digital image-based colorimetric method for measuring free acidity in edible vegetable oils

The standard method used to quantify free acidity (FA) in vegetable oil is neutralization titration, which requires many toxic chemicals and depends on an analyst's experience in detecting endpoints. Here, a digital image colorimetry (DIC) method using a smartphone camera was developed to measure FA in vegetable oils. A cupric acetate solution was used to produce the colorimetric reaction. The coloured solutions were imaged, and R values (from the RGB colour system) were calibrated against the respective FAs in the standards. The FA values of the samples were determined by standard addition calibration. These results were compared to measurements of FA obtained by the standard titrimetric method. An excellent correlation was obtained, with an R2 of 0.98 and a mean absolute error of 0.06%. The chemicals needed for analysis were reduced by approximately 90%. Thus, DIC is a less subjective and more economical method for determining FA in vegetable oils.

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure

This study delves into the use of compact near-infrared spectroscopy instruments for distinguishing between different varieties of barley, chickpeas, and sorghum, addressing a vital need in agriculture for precise crop variety identification. This identification is crucial for optimizing crop performance in diverse environmental conditions and enhancing food security and agricultural productivity. We also explore the potential application of transformer models in near-infrared spectroscopy and conduct an in-depth evaluation of the impact of data preprocessing and machine learning algorithms on variety classification. In our proposed spectraformer multi-classification model, we successfully differentiated 24 barley varieties, 19 chickpea varieties, and ten sorghum varieties, with the highest accuracy rates reaching 85%, 95%, and 86%, respectively. These results demonstrate that small near-infrared spectroscopy instruments are cost-effective and efficient tools with the potential to advance research in various identification methods, but also underscore the value of transformer models in near-infrared spectroscopy classification. Furthermore, we delve into the discussion of the influence of data preprocessing on the performance of deep learning models compared to traditional machine learning models, providing valuable insights for future research in this field.

Advanced technologies in biodegradable packaging using intelligent sensing to fight food waste

The limitation of conventional packaging in demonstrating accurate and real-time food expiration dates leads to food waste and foodborne diseases. Real-time food quality monitoring via intelligent packaging could be an effective solution to reduce food waste and foodborne illnesses. This review focuses on recent technological advances incorporated into food packaging for monitoring food spoilage, with a major focus on paper-based sensors and their combination with smartphone. This review paper offers a comprehensive exploration of advanced macromolecular technologies in biodegradable packaging, a general overview of paper-based probes and their incorporation into food packaging coupled with intelligent sensing mechanisms for monitoring food freshness. Given the escalating global concerns surrounding food waste, our manuscript serves as a pivotal resource, consolidating current research findings and highlighting the transformative potential of these innovative packaging solutions. We also highlight the current intelligent paper-based food freshness sensors and their various advantages and limitations. Examples of implementation of paper-based sensors/probes for food storage and their accuracy are presented. Finally, we examined how intelligent packaging can be an alternative to reduce food waste. Several technologies discussed here have good potential to be used in food packaging for real-time food monitoring, especially when combined with smartphone diagnosis.

Recent progress in biosensor regeneration techniques

Biosensors are widely used in various applications, from medical diagnostics to environmental monitoring. Their widespread and continuous use necessitates regeneration methods to ensure cost-effectiveness and sustainability. In the realm of advancing human-centric bioelectronics for continuous monitoring, employing these sensors for real-time, in situ detection of biomarkers presents a considerable challenge. This mini-review examines diverse strategies utilized for the regeneration of biosensors, categorizing them based on their underlying mechanisms and discussing representative works. We explore methods ranging from surface engineering/re-functionalization, chemical treatments, allosteric regulation of bioreceptors, to manipulations of electric/magnetic fields, highlighting their working principles and exemplary studies. The advantages of each method, such as simplicity, high regeneration efficiency, and versatility, are discussed alongside their challenges, including degradation over cycles, limited applicability, and potential damage to sensors. As the demand for continuous and real-time biosensing escalates, the development of efficient and reliable regeneration strategies becomes essential. This mini-review offers an overview of the current landscape of biosensor regeneration, aiming to guide future research and innovations in this area.

Critical importance of RGB color space specificity for colorimetric bio/chemical sensing: A comprehensive study

The use of the RGB color model in colorimetric chemical sensing via imaging techniques is widely prevalent in the literature. However, the lack of specificity in the selection of RGB color space during capture and analysis presents a significant challenge in creating standardised methods for this field and possible discrepancies. In this study, we conducted a comprehensive comparison and contrast of a total of 68 RGB color spaces to evaluate their respective impacts on colorimetric bio/chemical sensing. We explore the impact of dynamic range, sensitivity, and limit of detection, and show that the lack of specificity in RGB color space selection can significantly impact colorimetric chemical sensing by 42-77%. We also explore the impact of underlying RGB comparisons and demonstrate a further 18.3% discrepancy between RGB color spaces. By emphasising the importance of proper RGB color space selection and handling, our findings contribute to a better understanding of this critical area and present valuable opportunities for future research. We further provide valuable insights for creating standardised methods in this field, which can be utilised to avoid discrepancies and ensure accurate and reliable analysis in colorimetric bio/chemical sensing.

New spectrophotometric and smartphone-based colorimetric methods for determination of atenolol in pharmaceutical formulations

Novel spectrophotometric and smartphone-based colorimetric methods were developed and validated for the estimation of atenolol (ATE) in pharmaceutical formulations. The measurement procedure is based on the de-diazotization reaction, in which ATE is able to inhibit the diazotized sulfanilic acid from reacting with 8-hydroxy quinoline (8-HQ) in a basic medium. As a result, the formation of red-orange color azo-dye is hindered, and the color intensity is decreased proportionally to concentration of ATE. In spectrophotometric method the azo-dye color fate was monitored at 495 nm. While in smartphone-based colorimetric (SBC) method the captured image in the design processed by RGB App and transferred to the absorbance. The reactant concentrations were optimized using a central composite design (CCD) and response surface method. The methods exhibit good linearity in the 8.0 to 60.0 µg mL-1 range with no significant effect of interferences. The spectrophotometric method yields a linear equation with a slope of 0.0187 (R2 = 0.9993), a limit of detection (LOD) of 1.28 µg mL-1, and a limit of quantification (LOQ) of 4.28 µg mL-1. On the other hand, the smartphone-based colorimetric (SBC) method demonstrates a linear equation with a slope of 0.0127 (R2 = 0.9965), an LOD of 2.13 µg mL-1, and an LOQ of 7.09 µg mL-1. Analyzing ATE in pharmaceutical tablets was utilized to validate the applicability of the developed methods, and the results were statistically compared with those obtained by the HPLC method using the t-test and F-test.

Optical fiber speckle spectrometer based on reversed-lens smartphone microscope

Smartphones are a potentially powerful platform for scientific instruments. Here, we demonstrate speckle spectroscopy with smartphone-level hardware. This technique promises greater performance thresholds than traditional diffraction gratings. Light is injected into an optical fiber and the emergent speckle patterns are imaged by a reversed-lens smartphone camera. The smartphone then uses an algorithm, running on a mobile computing app, to determine, in less than one second, the (hitherto unknown) input spectrum. We reconstruct a variety of visible-wavelength (470-670 nm) single and multi-peaked spectra using a tunable source. The latter also include a metameric pair, i.e., two spectra that are different, yet represent colors that are indistinguishable to the human eye.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Compact and ultracompact spectral imagers: technology and applications in biomedical imaging

Significance

Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging.

Aim

With this review paper, researchers will (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.

Approach

We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of "compact," we included a table of dimensions and weights for systems that met our definition.

Results

There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry-Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings.

Conclusions

Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Luminescent Guests Encapsulated in Metal-Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review

Metal-organic frameworks (MOFs) have excellent applicability in several fields and have significant structural advantages, due to their open pore structure, high porosity, large specific surface area, and easily modifiable and functionalized porous surface. In addition, a variety of luminescent guest (LG) species can be encapsulated in the pores of MOFs, giving MOFs a broader luminescent capability. The applications of a variety of LG@MOF sensors, constructed by doping MOFs with LGs such as lanthanide ions, carbon quantum dots, luminescent complexes, organic dyes, and metal nanoclusters, for fluorescence detection of various target analyses such as ions, biomarkers, pesticides, and preservatives are systematically introduced in this review. The development of these sensors for portable visual fluorescence sensing applications is then covered. Finally, the challenges that these sectors currently face, as well as the potential for future growth, are briefly discussed.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

Rapid Point-of-Care Quantification of Human Serum Albumin in Urine Based on Ratiometric Fluorescence Signaling Driven by Intramolecular H-Bonding

Human serum albumin exerts multifunctions, such as maintaining the oncotic pressure of plasma, carrying hydrophobic molecules, and acting as the most important antioxidant in the blood. Lower serum albumin levels are linked to several cardiovascular diseases, and dysfunction of albumin reabsorption in the kidney is linked to liver disease, renal disorder, and diabetes. Albumin is thus a powerful diagnostic and prognostic marker; however, its quantification in urine by readily affordable tools is challenging owing to its very low concentration. To address this issue, we developed a ratiometric fluorescent probe with multiple advantages through a systematic structure variation of a benzocoumarin fluorophore and, further, a prototype of a smartphone-based point-of-care device. We determined albumin levels in urine and observed that a smoking person has notably higher urine albumin than a nonsmoking person. The cheap device provides a promising tool for albumin-associated disease diagnosis in communities with limited resources.

Advances, applications, and limitations of portable and rapid detection technologies for routinely encountered foodborne pathogens

Traditional foodborne pathogen detection methods are highly dependent on pre-treatment of samples and selective microbiological plating to reliably screen target microorganisms. Inherent limitations of conventional methods include longer turnaround time and high costs, use of bulky equipment, and the need for trained staff in centralized laboratory settings. Researchers have developed stable, reliable, sensitive, and selective, rapid foodborne pathogens detection assays to work around these limitations. Recent advances in rapid diagnostic technologies have shifted to on-site testing, which offers flexibility and ease-of-use, a significant improvement from traditional methods' rigid and cumbersome steps. This comprehensive review aims to thoroughly discuss the recent advances, applications, and limitations of portable and rapid biosensors for routinely encountered foodborne pathogens. It discusses the major differences between biosensing systems based on the molecular interactions of target analytes and biorecognition agents. Though detection limits and costs still need further improvement, reviewed technologies have high potential to assist the food industry in the on-site detection of biological hazards such as foodborne pathogens and toxins to maintain safe and healthy foods. Finally, this review offers targeted recommendations for future development and commercialization of diagnostic technologies specifically for emerging and re-emerging foodborne pathogens.

Recent Advances in Nanomaterial-Based Sensing for Food Safety Analysis

The increasing public attention on unceasing food safety incidents prompts the requirements of analytical techniques with high sensitivity, reliability, and reproducibility to timely prevent food safety incidents occurring. Food analysis is critically important for the health of both animals and human beings. Due to their unique physical and chemical properties, nanomaterials provide more opportunities for food quality and safety control. To date, nanomaterials have been widely used in the construction of sensors and biosensors to achieve more accurate, fast, and selective food safety detection. Here, various nanomaterial-based sensors for food analysis are outlined, including optical and electrochemical sensors. The discussion mainly involves the basic sensing principles, current strategies, and novel designs. Additionally, given the trend towards portable devices, various smartphone sensor-based point-of-care (POC) devices for home care testing are discussed.

Site-splitting inhibition and near-infrared luminescence properties of Cr3+ activated magnetoplumbite SrAl12O19 modified by La and Mg

Near-infrared phosphor-converted light-emitting diodes (pc-LEDs) are attractive in application like in vivo imaging and nondestructive examination in food industry. Plenty of aluminium oxides with high melting point and rigidity have been researched towards high photoluminescence quantum yield and thermal stability. In this work, a series of Cr³⁺ activated La, Mg modified SrAl₁₂O₁₉ phosphors, i.e. Sr_0.7La_0.3Mg_0.3Al_11.7O₁₉, are prepared via a solid-state reaction route. The site splitting and structural evolution towards relative thermodynamic stability in the modified host are discussed, Cr³⁺ ions with multisite occupation in the host show intense zero-phonon-line (ZPL) emission and tunable broadband emission. The optimized phosphor exhibits excellent luminescence thermal stability, with 98% residual integrity intensity of that at room temperature at 473 K. The results provide a reference for developing novel NIR-emitting magnetoplumbite phosphor with high efficiency and excellent thermal quenching resistance.

An improved spectral estimation method based on color perception features of mobile phone camera

We use the mobile phone camera as a new spectral imaging device to obtain raw responses of samples for spectral estimation and propose an improved sequential adaptive weighted spectral estimation method. First, we verify the linearity of the raw response of the cell phone camera and investigate its feasibility for spectral estimation experiments. Then, we propose a sequential adaptive spectral estimation method based on the CIE1976 L*a*b* (CIELAB) uniform color space color perception feature. The first stage of the method is to weight the training samples and perform the first spectral reflectance estimation by considering the Lab color space color perception features differences between samples, and the second stage is to adaptively select the locally optimal training samples and weight them by the first estimated root mean square error (RMSE), and perform the second spectral reconstruction. The novelty of the method is to weight the samples by using the sample in CIELAB uniform color space perception features to more accurately characterize the color difference. By comparing with several existing methods, the results show that the method has the best performance in both spectral error and chromaticity error. Finally, we apply this weighting strategy based on the CIELAB color space color perception feature to the existing method, and the spectral estimation performance is greatly improved compared with that before the application, which proves the effectiveness of this weighting method.

Fluorescent sensor based on pyrroloquinoline quinone (PQQ)-glucose dehydrogenase for glucose detection with smartphone-adapted signal analysis

A new nano-structured platform for fluorescent analysis using PQQ-dependent glucose dehydrogenase (PQQ-GDH) was developed, particularly using a smartphone for transduction and quantification of optical signals. The PQQ-GDH enzyme was immobilized on SiO₂ nanoparticles deposited on glass microfiber filter paper, providing a high load of the biocatalytic enzyme. The platform was tested and optimized for glucose determination using a wild type of the PQQ-GDH enzyme. The analysis was based on the fluorescence generated by the reduced form of phenazine methosulfate produced stoichiometrically to the glucose concentration. The fluorescent signals were generated at separate analytical spots on the paper support under wavelength (365 nm) UV excitation. The images of the analytical spots, dependent on the glucose concentration, were obtained using a photo camera of a standard smartphone. Then, the images were processed and quantified using software installed in a smartphone. The developed biocatalytic platform is the first step to assembling a large variety of biosensors using the same platform functionalized with artificial allosteric chimeric PQQ-dependent glucose dehydrogenase activated with different analytes. The future combination of the artificial enzymes, the presently developed analytical platform, and signal processing with a smartphone will lead to novel point-of-care and end-user biosensors applicable to virtually all possible analytes.

Smartphone based aptasensors as intelligent biodevice for food contamination detection in food and soil samples: Recent advances

Nowadays, the integration of conventional analytical approaches with smartphones has been developed novel, emerging and affordable devices for improving on-site detection platforms in the fields of food safety. Smartphone-based aptasensors as the next generation of portable aptasensing technique has attracted considerable attention as it offers a semi-automated user interface that can be exploited by inexpert characters. Wireless data transferability is an undeniable advantage that home-testing platforms have as well as it can suggest high computational power. In addition, these types of biodevices can provide real-time monitoring in terms of exchanging digital networks in real-time. To elaborate, the ability of smartphones to connect through the Internet is one of the most critical advantages of smartphone-based aptasensor that can be uploaded to Cloud databases and results can be disseminated as spatio-temporal maps across the globe. This review focused on the recent progress and technical breakthroughs of aptasensor on the smartphone as a groundbreaking enterprise in the field of biochemical analysis, importantly in the aspect of the combination of different types of biosensors including electrochemical, optical and colorimetric. In our opinion, this review can broaden our understanding of using smartphones as a portable sensing approach by addressing the current challenges and future perspectives.

Bluetooth-Connected Pocket Spectrometer and Chemometrics for Olive Oil Applications

Unsaturated fatty acids are renowned for their beneficial effects on the cardiovascular system. The high content of unsaturated fatty acids is a benefit of vegetable fats and an important nutraceutical indicator. The ability to quickly check fat composition of an edible oil could be advantageous for both consumers and retailers. A Bluetooth-connected pocket spectrometer operating in NIR band was used for analyzing olive oils of different qualities. Reference data for fatty acid composition were obtained from a certified analytical laboratory. Chemometrics was used for processing data, and predictive models were created for determining saturated and unsaturated fatty acid content. The NIR spectrum also demonstrated good capability in classifying extra virgin and non-extra virgin olive oils. The pocket spectrometer used in this study has a relatively low cost, which makes it affordable for a wide class of users. Therefore, it may open the opportunity for quick and non-destructive testing of edible oil, which can be of interest for consumer, retailers, and for small/medium-size producers, which lack easy access to conventional analytics.

Multi-Mode Compact Microscopy for High-Contrast and High-Resolution Imaging

We report a multi-mode compact microscope (MCM) for high-contrast and high-resolution imaging. The MCM consists of two LED illuminations, a magnification lens, a lift stage, and a housing with image processing and LED control boards. The MCM allows multi-modal imaging, including reflection, transmission, and higher magnification modes. The dual illuminations also provide high-contrast imaging of various targets such as biological samples and microcircuits. The high dynamic range (HDR) imaging reconstruction of MCM increases the dynamic range of the acquired images by 1.36 times. The microlens array (MLA)-assisted MCM also improves image resolution through the magnified virtual image of MLA. The MLA-assisted MCM successfully provides a clear, magnified image by integrating a pinhole mask to prevent image overlap without additional alignment. The magnification of MLA-assisted MCM was increased by 3.92 times compared with that of MCM, and the higher magnification mode demonstrates the image resolution of 2.46 μm. The compact portable microscope can provide a new platform for defect inspection or disease detection on site.

A Universal Multichannel Platform for Assembling Enzyme-Based Boolean Logic Gates

Concatenated enzyme-based Boolean logic gates activated with 5 chemical input signals were analyzed with a smartphone photo camera. Simultaneous detection of 32 input combinations was conveniently performed using enzyme-modified fiberglass sensing spots generating fluorescence with different intensities for the 0 and 1 binary outputs. The developed technology offers an easy readout method for multi-channel logic systems.

A smartphone-assisted microarray immunosensor coupled with GO-based multi-stage signal amplification strategy for high-sensitivity detection of okadaic acid

Okadaic acid (OA) is one of the main virulence factors of diarrheal shellfish toxins (DSP), which can cause acute carcinogenic or teratogenic effects after ingestion of contaminated shellfish. Therefore, high-sensitivity and fast detection of OA is a key to preventing the occurrence of safety accidents. In this paper, we effectively established a smartphone-assisted microarray immunosensor combined with an indirect competitive ELISA (iELISA) for quantitative colorimetric detection of OA. To further improve the detection sensitivity and match the smartphone imaging, a novel graphene oxide (GO) composite probe was developed to realize the multi-stage signal amplification. The system exhibited a wide linear range for the detection of OA (0.02-33.6 ng ·mL^-1) with low detection limit of 0.02 ng ·mL^-1. The recovery of OA in spiked shellfish samples was in the range of 80%-103.5%, which indicates the good applicability of this biosensor. The whole detection system has advantages of simplicity, low cost, high sensitivity and portability, which is expected to be a powerful alternative tool for on-site detecting and early warning of the pollution of marine products.

Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification

We present a smartphone-based bacterial colony phenotyping instrument using a reflective elastic light scattering (ELS) pattern and the resolving power of the new instrument. The reflectance-type device can acquire ELS patterns of colonies on highly opaque media as well as optically dense colonies. The novel instrument was built using a smartphone interface and a 532 nm diode laser, and these essential optical components made it a cost-effective and portable device. When a coherent and collimated light source illuminated a bacterial colony, a reflective ELS pattern was created on the screen and captured by the smartphone camera. The collected patterns whose shapes were determined by the colony morphology were then processed and analyzed to extract distinctive features for bacterial identification. For validation purposes, the reflective ELS patterns of five bacteria grown on opaque growth media were measured with the proposed instrument and utilized for the classification. Cross-validation was performed to evaluate the classification, and the result showed an accuracy above 94% for differentiating colonies of E. coli, K. pneumoniae, L. innocua, S. enteritidis, and S. aureus.

Improved method for spectral reflectance estimation and application to mobile phone cameras

We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter. Then, the optimal reflectance estimate is determined to minimize the mean-square error between the estimate and the original surface-spectral reflectance. We attempt to further improve the estimation accuracy and develop a novel linear estimator in a more general form than the Wiener estimator. Furthermore, we calibrate the imaging system using a reference standard sample. Finally, experiments are performed to validate the proposed method for estimating the surface-spectral reflectance using different mobile phone cameras.

Strategic Priorities of the Scientific Plan of the European Research Infrastructure METROFOOD-RI for Promoting Metrology in Food and Nutrition

The pan-European distributed Research Infrastructure for Promoting Metrology in Food and Nutrition (METROFOOD-RI) has evolved in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to promote high-quality metrology services across the food chain. The METROFOOD-RI comprises physical facilities and electronic facilities. The former includes Reference Material plants and analytical laboratories (the ‘Metro’ side) and also experimental fields/farms, processing/storage plants and kitchen-labs (the ‘Food’ side). The RI is currently prepared to apply for receiving the European Research Infrastructure Consortium (ERIC) legal status and is organised to fulfil the requirements for operation at the national, European Union (EU) and international level. In this view, the METROFOOD-RI partners have recently reviewed the scientific plan and elaborated strategic priorities on key thematic areas of research in the food and nutrition domain to which they have expertise to contribute to meet global societal challenges and face unexpected emergencies. The present review summarises the methodology and main outcomes of the research study that helped to identify the key thematic areas from a metrological standpoint, to articulate critical and emerging issues and demands and to structure how the integrated facilities of the RI can operate in the first five years of operation as ERIC.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

In-Country Method Validation of a Paper-Based, Smartphone-Assisted Iron Sensor for Corn Flour Fortification Programs

Food fortification in low-income settings is limited due to the lack of simple quality control sensing tools. In this study, we field validated a paper-based, smartphone-assisted colorimetric assay (Nu3Px) for the determination of iron in fortified flours against the gold standard method, atomic emission spectrometry (AES). Samples from commercial brands (n = 6) were collected from supermarkets, convenience stores, and directly from companies in Mexico and characterized using both Nu3Px and AES. Nu3Px’s final error parameters were quantified (n = 45) via method validation final experiments (replication and comparison of methods experiment). Qualitative pilot testing was conducted, assessing Nu3Px’s accept/reject batch decision making (accept ≥ 40 μg Fe/g flour; reject < 40 μg Fe/g flour) against Mexico’s fortification policy. A modified user-centered design process was followed to develop and evaluate an alternative sampling procedure using affordable tools. Variation of iron content in Mexican corn flours ranged from 23% to 39%. Nu3Px’s random error was 12%, and its bias was 1.79 ± 9.99 μg Fe/g flour. Nu3Px had a true mean difference from AES equal to 0 and similar variances. AES and Nu3Px made similar classifications based on Mexico’s policy. Using simple, affordable tools for sampling resulted in similar output to the traditional sampling preparation (r = 0.952, p = 0.01). The affordable sample preparation kit has similar precision to using analytical tools. The sample preparation kit coupled with the smartphone app and paper-based assay measure iron within the performance parameters required for the application to corn flour fortification programs, such as in the case of Mexico.

Recent Advancements in the Technologies Detecting Food Spoiling Agents

To match the current life-style, there is a huge demand and market for the processed food whose manufacturing requires multiple steps. The mounting demand increases the pressure on the producers and the regulatory bodies to provide sensitive, facile, and cost-effective methods to safeguard consumers' health. In the multistep process of food processing, there are several chances that the food-spoiling microbes or contaminants could enter the supply chain. In this contest, there is a dire necessity to comprehend, implement, and monitor the levels of contaminants by utilizing various available methods, such as single-cell droplet microfluidic system, DNA biosensor, nanobiosensor, smartphone-based biosensor, aptasensor, and DNA microarray-based methods. The current review focuses on the advancements in these methods for the detection of food-borne contaminants and pathogens.

Evaluation of Olive Oil Quality with Electrochemical Sensors and Biosensors: A Review

Electrochemical sensors, sensor arrays and biosensors, alongside chemometric instruments, have progressed remarkably of late, being used on a wide scale in the qualitative and quantitative evaluation of olive oil. Olive oil is a natural product of significant importance, since it is a rich source of bioactive compounds with nutritional and therapeutic properties, and its quality is important both for consumers and for distributors. This review aims at analysing the progress reported in the literature regarding the use of devices based on electrochemical (bio)sensors to evaluate the bioactive compounds in olive oil. The main advantages and limitations of these approaches on construction technique, analysed compounds, calculus models, as well as results obtained, are discussed in view of estimation of future progress related to achieving a portable, practical and rapid miniature device for analysing the quality of virgin olive oil (VOO) at different stages in the manufacturing process.