Rapid In Situ Analysis of Plant Emission for Disease Diagnosis Using a Portable Gas Chromatography Device

Wearable electrochemical sensors for plant small-molecule detection

Small molecules in plants - such as metabolites, phytohormones, reactive oxygen species (ROS), and inorganic ions - participate in the processes of plant growth and development, physiological metabolism, and stress response. Wearable electrochemical sensors, known for their fast response, high sensitivity, and minimal plant damage, serve as ideal tools for dynamically tracking these small molecules. Such sensors provide producers or agricultural researchers with noninvasive or minimally invasive means of obtaining plant signals. In this review we explore the applications of wearable electrochemical sensors in detecting plant small molecules, enabling scientific assessment of plant conditions, quantification of environmental stresses, and facilitation of plant health monitoring and disease prediction.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

Microchip gas chromatography column using magnetic beads coated with polydimethylsiloxane and metal organic frameworks

Micro gas chromatography (μGC) using microfabricated silicon columns has been developed in response to the requirement for portable on-site gas analysis. Although different stationary phases have been developed, repeatable and reliable surface coatings in these rather small microcolumns remains a challenge. Herein, a new stationary phase coating strategy using magnetic beads (MBs) as carriers for micro column is presented. MBs modified with organopolysiloxane (MBs@OV-1) and a metal organic framework (MBs@HKUST-1) are deposited in on-chip microcolumns assisted with a magnetic field with an optimized modification process. MBs@OV-1 column showed a minimum HETP of 0.074 cm (1351 plates/m) of 62 cm/s. Mixtures of volatile organic compounds are successfully separated using MBs carried stationary phase which demonstrates that this technique has good chromatographic column efficiency. This method not only provides a novel coating process, washing and characterization of the stationary phases but also establishes a straightforward strategy for testing new absorbent materials for μGC systems.

Ultrathin Silica Integration for Enhancing Reliability of Microfluidic Photoionization Detectors

Microfluidic photoionization detectors (μPIDs) based on silicon chips can rapidly and sensitively detect volatile compounds. However, the applications of μPID are limited by the manual assembly process using glue, which may outgas and clog the fluidic channel, and by the short lifetime of the vacuum ultraviolet (VUV) lamps (especially, argon lamps). Here, we developed a gold-gold cold welding-based microfabrication process to integrate ultrathin (10 nm) silica into μPID. The silica coating enables direct bonding of the VUV window to silicon under amicable conditions and works as a moisture and plasma exposure barrier for VUV windows that are susceptible to hygroscopicity and solarization. Detailed characterization of the silica coating was conducted, showing that the 10 nm silica coating allows 40-80% VUV transmission from 8.5 to 11.5 eV. It is further shown that the silica-protected μPID maintained 90% of its original sensitivity after 2200 h of exposure to ambient (dew point = 8.0 ± 1.8 °C), compared to 39% without silica. Furthermore, argon plasma inside an argon VUV lamp was identified as the dominant degradation source for the LiF window with color centers formation in UV-vis and VUV transmission spectra. Ultrathin silica was then also demonstrated effective in protecting the LiF from argon plasma exposure. Lastly, thermal annealing was found to bleach the color centers and restore VUV transmission of degraded LiF windows effectively, which will lead to future development of a new type of VUV lamp and the corresponding μPID (and PID in general) that can be mass produced with a high yield, a longer lifetime, and better regenerability.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Utilizing volatile organic compounds for early detection of Fusarium circinatum

Fusarium circinatum, a fungal pathogen deadly to many Pinus species, can cause significant economic and ecological losses, especially if it were to become more widely established in Europe. Early detection tools with high-throughput capacity can increase our readiness to implement mitigation actions against new incursions. This study sought to develop a disease detection method based on volatile organic compound (VOC) emissions to detect F. circinatum on different Pinus species. The complete pipeline applied here, entailing gas chromatography-mass spectrometry of VOCs, automated data analysis and machine learning, distinguished diseased from healthy seedlings of Pinus sylvestris and Pinus radiata. In P. radiata, this distinction was possible even before the seedlings became visibly symptomatic, suggesting the possibility for this method to identify latently infected, yet healthy looking plants. Pinus pinea, which is known to be relatively resistant to F. circinatum, remained asymptomatic and showed no changes in VOCs over 28 days. In a separate analysis of in vitro VOCs collected from different species of Fusarium, we showed that even closely related Fusarium spp. can be readily distinguished based on their VOC profiles. The results further substantiate the potential for volatilomics to be used for early disease detection and diagnostic recognition.

Portable comprehensive two-dimensional micro-gas chromatography using an integrated flow-restricted pneumatic modulator

Two-dimensional (2D) gas chromatography (GC) provides enhanced vapor separation capabilities in contrast to conventional one-dimensional GC and is useful for the analysis of highly complex chemical samples. We developed a microfabricated flow-restricted pneumatic modulator (FRPM) for portable comprehensive 2D micro-GC (μGC), which enables rapid 2D injection and separation without compromising the 1D separation speed and eluent peak profiles. 2D injection characteristics such as injection peak width and peak height were fully characterized by using flow-through micro-photoionization detectors (μPIDs) at the FRPM inlet and outlet. A 2D injection peak width of ~25 ms could be achieved with a 2D/1D flow rate ratio over 10. The FRPM was further integrated with a 0.5-m long 2D μcolumn on the same chip, and its performance was characterized. Finally, we developed an automated portable comprehensive 2D μGC consisting of a 10 m OV-1 1D μcolumn, an integrated FRPM with a built-in 0.5 m polyethylene glycol 2D μcolumn, and two μPIDs. Rapid separation of 40 volatile organic compounds in ~5 min was demonstrated. A hybrid 2D contour plot was constructed by using both 1D and 2D chromatograms obtained with the two μPIDs at the end of the 1D and 2D μcolumns, which was enabled by the presence of the flow resistor in the FRPM.

Non-destructive Technologies for Plant Health Diagnosis

As global population grows rapidly, global food supply is increasingly under strain. This is exacerbated by climate change and declining soil quality due to years of excessive fertilizer, pesticide and agrichemical usage. Sustainable agricultural practices need to be put in place to minimize destruction to the environment while at the same time, optimize crop growth and productivity. To do so, farmers will need to embrace precision agriculture, using novel sensors and analytical tools to guide their farm management decisions. In recent years, non-destructive or minimally invasive sensors for plant metabolites have emerged as important analytical tools for monitoring of plant signaling pathways and plant response to external conditions that are indicative of overall plant health in real-time. This will allow precise application of fertilizers and synthetic plant growth regulators to maximize growth, as well as timely intervention to minimize yield loss from plant stress. In this mini-review, we highlight in vivo electrochemical sensors and optical nanosensors capable of detecting important endogenous metabolites within the plant, together with sensors that detect surface metabolites by probing the plant surface electrophysiology changes and air-borne volatile metabolites. The advantages and limitations of each kind of sensing tool are discussed with respect to their potential for application in high-tech future farms.

Breath analysis for detection and trajectory monitoring of acute respiratory distress syndrome in swine

Despite the enormous impact on human health, acute respiratory distress syndrome (ARDS) is poorly defined, and its timely diagnosis is difficult, as is tracking the course of the syndrome. The objective of this pilot study was to explore the utility of breath collection and analysis methodologies to detect ARDS through changes in the volatile organic compound (VOC) profiles present in breath. Five male Yorkshire mix swine were studied and ARDS was induced using both direct and indirect lung injury. An automated portable gas chromatography device developed in-house was used for point of care breath analysis and to monitor swine breath hourly, starting from initiation of the experiment until the development of ARDS, which was adjudicated based on the Berlin criteria at the breath sampling points and confirmed by lung biopsy at the end of the experiment. A total of 67 breath samples (chromatograms) were collected and analysed. Through machine learning, principal component analysis and linear discrimination analysis, seven VOC biomarkers were identified that distinguished ARDS. These represent seven of the nine biomarkers found in our breath analysis study of human ARDS, corroborating our findings. We also demonstrated that breath analysis detects changes 1–6 h earlier than the clinical adjudication based on the Berlin criteria. The findings provide proof of concept that breath analysis can be used to identify early changes associated with ARDS pathogenesis in swine. Its clinical application could provide intensive care clinicians with a noninvasive diagnostic tool for early detection and continuous monitoring of ARDS.

ARDS, confirmed by lung biopsy, was induced in swine, with breath monitored hourly. Seven VOC markers distinguish ARDS, which are the same as those in human ARDS and can predict ARDS onset ∼3 h earlier than clinical adjudication.https://bit.ly/3zIIIMQ

Small-Footprint, Field-Deployable LC/MS System for On-Site Analysis of Per- and Polyfluoroalkyl Substances in Soil

Per- and polyfluoroalkyl substances (PFASs) are emerging environmental pollutants of global concern. For rapid field site evaluation, there are very few sensitive, field-deployable analytical techniques. In this work, a portable lightweight capillary liquid chromatography (capLC) system was coupled with a small footprint portable mass spectrometer and configured for field-based applications. Further, an at-site ultrasound-assisted extraction (pUAE) methodology was developed and applied with a portable capLC/mass spectrometry (MS) system for on-site analysis of PFASs in real soil samples. The influential variables on the integration of capLC with MS and on the resolution and signal intensity of the capLC/MS setup were investigated. The important parameters affecting the efficiency of the pUAE method were also studied and optimized using the response surface methodology based on a central composite design. The mean recovery for 11 PFASs ranged between 70 and 110%, with relative standard deviations ranging from 3 to 12%. In-field method sensitivity for 12 PFASs ranged from 0.6 to 0.1 ng/g, with wide dynamic ranges (1-600 ng/g) and excellent linearities (R² > 0.991). The in-field portable system was benchmarked against a commercial lab-based LC-tandem MS (MS/MS) system for the analysis of PFASs in real soil samples, with the results showing good agreement. When deployed to a field site, 12 PFASs were detected and identified in real soil samples at concentrations ranging from 8.1 ng/g (for perfluorooctanesulfonic acid) to 2935.0 ng/g (perfluorohexanesulfonic acid).

Plant pest surveillance: from satellites to molecules

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics

Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.

Hand-portable HPLC with broadband spectral detection enables analysis of complex polycyclic aromatic hydrocarbon mixtures

Polycyclic aromatic hydrocarbons (PAHs) are considered priority hazardous substances due to their carcinogenic activity and risk to public health. Strict regulations are in place limiting their release into the environment, but enforcement is hampered by a lack of adequate field-testing procedure, instead relying on sending samples to centralised analytical facilities. Reliably monitoring levels of PAHs in the field is a challenge, owing to the lack of field-deployable analytical methods able to separate, identify, and quantify the complex mixtures in which PAHs are typically observed. Here, we report the development of a hand-portable system based on high-performance liquid chromatography incorporating a spectrally wide absorption detector, capable of fingerprinting PAHs based on their characteristic spectral absorption profiles: identifying 100% of the 24 PAHs tested, including full coverage of the United States Environmental Protection Agency priority pollutant list. We report unsupervised methods to exploit these new capabilities for feature detection and identification, robust enough to detect and classify co-eluting and hidden peaks. Identification is fully independent of their characteristic retention times, mitigating matrix effects which can preclude reliable determination of these analytes in challenging samples. We anticipate the platform to enable more sophisticated analytical measurements, supporting real-time decision making in the field.

Field analyses of lavender volatile organic compounds: performance evaluation of a portable gas chromatography-mass spectrometry device

INTRODUCTION

In situ analysis of volatile organic compounds (VOCs) emitted by plants is an important challenge in chemical ecology. The traditional approach usually consists in trapping compounds using dynamic headspace extraction (DHS) in-field, followed by gas chromatography analysis coupled with mass spectrometry (GC-MS and/or GC-FID) in the laboratory.

OBJECTIVES

In this study, we evaluated the use of the new portable Torion T-9 GC-MS system for rapid and in situ analysis of VOCs emitted by fine lavender and lavandin species.

MATERIAL AND METHODS

All field analyses were performed using a person-portable low-thermal mass GC system coupled with a miniature toroidal ion trap mass analyser (ppGC-ITMS): Torion T-9 portable GC-MS. Subsequently, multivariate statistical analyses were performed to determine chemical differences between species.

RESULTS

Thirty compounds were separated and detected in all lavender above-ground samples in only 3 min of analysis.

CONCLUSIONS

The portable GC-MS device enabled a rapid in-field distinction of Lavandula species based on their detected volatile profiles.

Characterization of Volatile Organic Compounds of Healthy and Huanglongbing-Infected Navel Orange and Pomelo Leaves by HS-GC-IMS

The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, is the only natural vector of bacteria responsible for Huanglongbing (HLB), a worldwide destructive disease of citrus. ACP reproduces and develops only on the young leaves of its rutaceous host plants. Olfactory stimuli emitted by young leaves may play an important role in ACP control and HLB detection. In this study, volatile organic compounds (VOCs) from healthy and HLB-infected young leaves of navel orange and pomelo were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). A total of 36 compounds (including dimers or polymers) were identified and quantified from orange and 10 from pomelo leaves. Some compounds showed significant differences in signal intensity between healthy and HLB-infected leaves and may constitute possible indicators for HLB infection. Principal component analysis (PCA) clearly discriminated healthy and HLB-infected leaves in both orange and pomelo. HS-GC-IMS was an effective method to identify VOCs from leaves. This study may help develop new methods for detection of HLB or find new attractants or repellents of ACP for prevention of HLB.

Agricultural nanodiagnostics for plant diseases: recent advances and challenges

Crop diseases caused by pathogenic microorganisms pose severe threats to the global food supply. Effective diagnostic tools for timely determination of plant diseases become essential to the assurance of agricultural sustainability and global food security. Nucleic acid- and antibody-based molecular assays are gold-standard methodologies for the diagnosis of plant diseases, but the analyzing procedures are complex and laborious. The prominent physical or chemical properties of nanomaterials have enabled their use as innovative and high-performance diagnostic tools for numerous plant pathogens and other important disease biomarkers. Engineered nanomaterials have been incorporated into traditional laboratory molecular assays or sequencing technologies that offer notable enhancement in sensitivity and selectivity. Meanwhile, nanostructure-supported noninvasive detection tools combined with portable imaging devices (e.g., smartphones) have paved the way for fast and on-site diagnosis of plant diseases and long-term monitoring of plant health conditions, especially in resource-poor settings.

Monitoring of infection volatile markers using CMOS-based luminescent bioreporters

Over the past two decades, whole-cell biosensors (WCBs) have been widely used in the environmental field, with only few applications proposed for use in agricultural. This study describes the development and optimization of a WCB for the detection of volatile organic compounds (VOCs) that is produced specifically by infected potato tubers. First, the effect of calcium-alginate matrix formation (beads vs. tablets) on the membrane uniformity and sensing efficiency was evaluated. Then, important parameters in the immobilization process were examined for their effect on the sensitivity to the presence of VOCs. The highest sensitivity to the target VOC was obtained by 20 min polymerization of bacterial suspension with optical density of 0.2 at 600 nm, dissolved in low-viscosity sodium alginate (1.5% w/v) and exposure to VOC at 4 °C. After optimization, the lowest limit of detection for three infection-sourced VOCs (nonanal, 3-methyl-1-butanol, and 1-octen-3-ol) was 0.17-, 2.03-, and 2.09-mg/L, respectively, and the sensor sensitivity was improved by 8.9-, 3.1- and 2-fold, respectively. Then, the new optimized immobilization protocol was implemented for the CMOS-based application, which increased the sensor sensitivity to VOC by 3-fold during real-time measurement. This is the first step in creating a sensor for real-time monitoring of crop quality by identifying changes in VOC patterns.