Smartphones as tools for equitable food quality assessment

Evaluating smartphone-based optical readouts for immunoassays in human and veterinary healthcare: A comparative study

Recent advances have significantly enhanced the use of smartphone devices for medical diagnostics. This study uses high-resolution cameras in mobile devices to capture and process bioassay images, enabling the quantification of diverse biomarkers across a range of diagnostic tests conducted on 96-well microplates. The study evaluates the effectiveness of this technology through protein quantification techniques and immunoassays that generate colorimetric responses at specific wavelengths. It includes the assessment of bicinchoninic acid and Bradford protein quantification methods, alongside a conventional immunoassay for detecting mare antibodies in colostrum to monitor foal immunodeficiencies. Further application involves the readout of magneto-actuated immunoassays aimed at quantifying bacteria. The results obtained from benchtop spectrophotometry at 595, 562, and 450 nm are compared with those acquired using a smartphone, which identified color intensities in shades of blue, purple, and yellow. This comparison yields promising correlations for the samples tested, suggesting a high degree of accuracy in the smartphone capability to analyze bioassay outcomes. The analysis via smartphone is facilitated by a specific app, which processes the images captured by the phone camera to quantify color intensities corresponding to different biomarker concentrations. Detection limits of 12.3 and 22.8 μg mL-1 for the bicinchoninic acid assay and 36.7 and 45.4 μg mL-1 for the Bradford are obtained for protein quantification using the spectrophotometer and the smartphone app, respectively. For mare's antibodies in colostrum, the values are 1.14 and 1.72 ng mL-1, while the detection of E. coli is performed at 2.0 x 104 and 2.9 × 104 CFU mL-1, respectively. This approach offers further advantages, including wide availability, cost-effectiveness, portability, compared to traditional and expensive benchtop instruments.

Smartphone-based digital image colorimetry for the determination of total capsaicinoid content in chili pepper extracts

A simple smartphone-based digital image colorimetry was proposed for the determination of total capsaicinoid content and the assessment of chili pepper pungency. The biobased solvent D-limonene was used for the first time to isolate analytes. Capsaicinoids were efficiently separated from chili pepper by solid-liquid extraction with D-limonene followed by partitioning of the analytes into the ammonium hydroxide solution to eliminate the matrix interference effect. For colorimetric detection of total capsaicinoid content, a selective chromogenic reaction was performed using Gibbs reagent (2,6-dichloroquinone-4-chloroimide). Measurements were performed using a smartphone-based setup and included image analysis with the program ImageJ. The limit of detection of the proposed procedure was 0.15 mg g-1. The intra-day repeatability did not exceed 10.0 %. The inter-day repeatability was less than 16.5 %. The comparison of the smartphone-based procedure with high-performance liquid chromatography showed satisfactory results.

Simple smartphone-based methods for the determination of bioactive compounds in wine

A set of simple smartphone-based methods of bioactive compounds determination were developed for wine analysis. Procedures for smartphone-based determination of the total content of phenolic compounds, flavonoids, anthocyanins and biogenic amines, as well as measurement of antioxidant activity were developed and fully validated. The experimental setup is based on smartphone and 3D-printed device, though it is very simple and can be conveniently applied in lab and in field. The proposed solutions have satisfactory figures of merit with R2 in the range of 0.9860-0.9981 for linear range. The recoveries were in range 98.6-102%, RSDs up to 4.2% and LODs below 2.3 mg/L. In order to demonstrate the applicability of the proposed procedures, wine samples were analysed using spectrophotometry and newly developed methods. The results of application of smartphone and spectrophotometer are comparable, in terms of validation parameters and measured concentrations in real samples.

Revolutionizing Phenolic Content Determination in Vegetable Oils: A Cutting-Edge Approach Using Smartphone-Based Image Analysis

This study addressed the need for a more accessible and efficient method of analyzing phenolic content in vegetable oils. The research aimed to develop a method that could be widely adopted by both researchers and industry professionals, ultimately revolutionizing the way phenolic content in vegetable oils is analyzed. This study developed a method of determining the total phenolic content (TPC) in vegetable oils using smartphone image analysis in the RGB color model. The method employed a gallic acid calibration solution and demonstrated exceptional determination coefficients for the RGB colors. The R-red color was selected as the basis for the analyses, and the method was statistically equivalent to standard UV/Vis spectrophotometry. The highest TPC was determined in hemp and olive oils, while the lowest was found in rice bran, grapeseed, and macadamia nut oils. This study concluded that smartphone image analysis, mainly using the R component of the RGB color model, was a superior alternative to traditional spectrophotometric methods for determining the TPC in vegetable oils. This innovative approach could revolutionize phenolic content analysis by providing researchers and industry professionals with a cost-effective, safe, and efficient tool. The estimated limit of detection (LOD) of 1.254 mg L-1 and limit of quantification (LOQ) of 3.801 mg L-1 further confirmed the reliability and comparability of the method. With these findings, it was expected that the method would be widely adopted in the future.

Droplet-based luminescent sensor supported onto hydrophobic cellulose substrate for assessing fish freshness following smartphone readout

In this work, two sensitive droplet-based luminescent assays with smartphone readout for the determination of trimethylamine nitrogen (TMA-N) and total volatile basic nitrogen (TVB-N) are reported. Both assays exploit the luminescence quenching of copper nanoclusters (CuNCs) produced when exposed to volatile nitrogen bases. In addition, hydrophobic-based cellulose substrates demonstrated their suitability as holders for both in-drop volatile enrichment and subsequent smartphone-based digitization of the enriched colloidal solution of CuNCs. Under optimal conditions, enrichment factors of 181 and 153 were obtained with the reported assays for TMA-N and TVB-N, respectively, leading to methodological LODs of 0.11 mg/100 g and 0.27 mg/100 g for TMA-N and TVB-N, respectively. The repeatability, expressed as RSD, was 5.2% and 5.6% for TMA-N and TVB-N, respectively (N = 8). The reported luminescent assays were successfully applied to the analysis of fish samples, showing statistically comparable results to those obtained with the reference methods of analysis.

Simple analytical method for total biogenic amines content determination in wine using a smartphone

A simple, fast, and green smartphone-based procedure for total biogenic amines content determination in wine was developed and validated. Sample preparation and analysis were simplified to make the method suitable for routine analyses even in resource-scarce settings. The commercially available S0378 dye and smartphone-based detection were used for this purpose. The developed method has satisfactory figures of merit for putrescine equivalent determination with R² of 0.9981. The method's greenness was also assessed using the Analytical Greenness Calculator. Samples of Polish wine were analysed to demonstrate the applicability of the developed method. Finally, results obtained with the developed procedure were compared with those previously obtained with GC-MS in order to evaluate the equivalence of the methods.

Analytical applications of smartphones for agricultural soil analysis

Soil is one of the most important farming resources. Appropriate managing of its quality promotes productive and sustainable agriculture. The valuable farm practice in soil quality managing is based on regular soil analysis with the aim of determining the exact amount of nutrients or other chemical, physical, and biological soil properties. Soil analysis usually requires sample collection at the desired sampling depth followed by sample delivery to chemical laboratories. However, laboratory analyses are resource-intensive and costly, and require a lot of time, effort, and equipment. A low-cost, fast, and effective alternative for soil quality control is the application of smartphones to perform chemical analyses directly in the field or on the farm. In this paper, an overview of recent developments on smartphone-based methodologies for agricultural purposes and portable evaluation of soil quality and its properties is presented. The discussion focuses on recent applications of smartphone-based devices for the determination of basic soil parameters, content of organic matter, mineral fertilizers, and organic or inorganic pollutants. Obvious advantages of using smartphones, such as convenience and simplicity of use, and the main shortcomings, such as relatively poor precision of the results obtained, are also discussed. The general trend shows the huge interest from researchers to move the technology into the field with the aim of providing cost-effective and rapid soil analysis. This paper can broaden the understanding of using smartphones for chemical analysis of soil samples, as it is a relatively new area and is expected to be developed rapidly.

Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue

Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy.

Smartphone-based digital image colorimetry for the determination of vancomycin in drugs

A simple smartphone-based digital image colorimetry is proposed for the determination of vancomycin in drugs. The analytical method relied on the reaction of vancomycin with copper(II) in ethanol–water medium with pH 4.3. The reaction resulted in the formation of a blue–grey complex, presenting an absorption maximum at 555 nm. A mobile application was used for smartphone-based analysis to decompose the individual channels of the colour model representations. The determination was performed using three smartphones followed by a comparison of the outcomes with spectrophotometric measurements. The most optimal analytical parameters were achieved for the H channel. The linear ranges obtained for the smartphone-based method proved to be comparable to the spectrophotometric range of 0.044–1.500 g dm−3 and were 0.049–1.500 g dm−3, 0.057–1.500 g dm−3, and 0.040–1.500 g dm−3 for Smartphones 1–3, respectively. Moreover, the determined coefficients of variance (CV, n = 9) and limits of detection (LOD) were 2.3% and 0.015 g dm−3, 6.2% and 0.017 g dm−3, and 2.5% and 0.012 g dm−3, respectively. Whereas for spectrophotometry, the obtained precision, CV was of 0.9% and a LOD of 0.013 g dm−3. The accuracy of the method was verified using model samples, generally the results were obtained with accuracy better than 10.9% (relative error). The method was applied to the determination of vancomycin in drugs. The results obtained by smartphone-based colorimetry did not differ from the expected values for more than 2.6%, were consistent with each other and with the results of spectrophotometric determinations.

Graphical abstract

A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems

Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.

When smartphone enters food safety: A review in on-site analysis for foodborne pathogens using smartphone-assisted biosensors

Pathogens are one of the supreme threats for the public health around the world in food supply chain. The on-site monitoring is an emerging trend for screening pathogens during the food processing and preserving. Traditional analytical tools have been unable to satisfy the current demands. Smartphones have enormous potentials for achieving on-site detection of foodborne pathogens, with intrinsic advantages such as small size, high accessibility, fast processing speed, and powerful imaging capacity. This review aims to synthesize the current advances in smartphone-assisted biosensors (SABs) for sensing foodborne pathogens, and briefly put forward the problem that consist in the research. We present the role of nanotechnology and recognition modes targeting foodborne pathogens in SABs, and discuss the signal conversion platforms coupling with smartphone. The challenges and perspectives in SABs are also proposed. The smartphone analytics area is moving forward, and it much be subject to careful quality standards and validation.

Smartphone-based portable fluorescence sensor with gold nanoparticle mediation for selective detection of nitrite ions

A simple, portable device for the detection of NO₂^- via a fluorescence method was developed. The proposed device consisted of a dark box containing a blue LED as a low-power excitation light source and a smartphone with a mobile application for RGB analysis as a light detector. Detection was mediated by using synthesized cetyltrimethylammonium bromide-stabilized gold nanoparticles (CTAB-AuNPs). The CTAB-AuNPs were etched with NO₂^- to yield Au³⁺, which catalyzes the oxidation of o-phenylenediamine (OPD) in the presence of H₂O₂ to generate 2,3-diaminophenazine (DAP). Triton X-100 (TX-100) micelles were introduced to improve the DAP fluorescence emission. The fluorescence intensity of DAP was recorded by the smartphone in terms of RGB intensity, which was correlated with the NO₂^- concentration. This method provided a wide linear working concentration range (0.5-100 μM), a limit of detection of 0.17 μM and excellent selectivity for NO₂^- over other anions.

A Soft Coprocessor Approach for Developing Image and Video Processing Applications on FPGAs

Developing Field Programmable Gate Array (FPGA)-based applications is typically a slow and multi-skilled task. Research in tools to support application development has gradually reached a higher level. This paper describes an approach which aims to further raise the level at which an application developer works in developing FPGA-based implementations of image and video processing applications. The starting concept is a system of streamed soft coprocessors. We present a set of soft coprocessors which implement some of the key abstractions of Image Algebra. Our soft coprocessors are designed for easy chaining, and allow users to describe their application as a dataflow graph. A prototype implementation of a development environment, called SCoPeS, is presented. An application can be modified even during execution without requiring re-synthesis. The paper concludes with performance and resource utilization results for different implementations of a sample algorithm. We conclude that the soft coprocessor approach has the potential to deliver better performance than the soft processor approach, and can improve programmability over dedicated HDL cores for domain-specific applications while achieving competitive real time performance and utilization.

The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.

Spot tests: past and present

Microchemistry, i.e., the chemistry performed at the scale of a microgram or less, has its roots in the late eighteenth and early nineteenth centuries. In the first half of the twentieth century a wide range of spot tests have been developed. For didactic reasons, they are still part of the curriculum of chemistry students. However, they are even highly important for applied analyses in conservation of cultural heritage, food science, forensic science, clinical and pharmacological sciences, geochemistry, and environmental sciences. Modern pregnancy tests, virus tests, etc. are the most recent examples of sophisticated spot tests. The present ChemTexts contribution aims to provide an overview of the past and present of this analytical methodology.

Low-Cost and Convenient Microchannel Resistance Biosensing Platform by Directly Translating Biorecognition into a Current Signal

We report a low-cost and convenient microchannel resistance (MCR) biosensing platform that uses current signal to report biorecognition. The biorecognition behavior between targets and biometric molecules (antigens, antibodies, or oligonucleotides) immobilized on magnetic beads and polystyrene (PS) microspheres induces a quantitative change in the unreacted PS microspheres. After magnetic separation, the unreacted PS microsphere solution is passed through the microchannel, leading to an obvious blocking effect, resulting in an increase in resistance, which can in turn be measured by monitoring the electric current. Thus, the biorecognition is directly converted into a detectable current signal without any bulky instruments or additional chemical reactions. The MCR biosensing platform is cost-effective and user-friendly with high accuracy. It can be an appropriate analysis technique for point-of-care testing in resource-poor settings.

Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications

Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.