The development of fluorometric paper-based analytical devices (fPADs) integrated with smartphone for fluorometric quantification of ethanol in an instrument-free and portable setup is described. The NAD+-dependent alcohol dehydrogenase immobilized within chitosan modified paper substate was utilized as a bio-recognition element and enzymatically generated NADH was used as a fluorescent probe. 3D-printed imaging setup which houses a paper chip holder and UV-light emitting device (LED) was developed for rapid, accurate capture of the fluorescent images. The biocompatible chitosan layer covering the paper provides a feasible environment for enzyme immobilization and enhances the fluorescence signal. The developed fPADs exhibited high sensitivity for ethanol detection and has a linear range for ethanol detection from 17 µM to 8.75 mM (R2 =0.99). Additionally, the fPADs were applied to quantify ethanol in four different wine samples including red, white, rose, and sparkling wines successfully. Moreover, the fPADs produce reproducible signals without loss of enzyme activity for at least 14 days and ~80% activity remained till 28 days. Thus, the proposed approach can provide a facile, affordable, portable, and instrument-free tool for the onsite quantification of ethanol in real samples and is applicable for food quality controls.
Biosensing Techniques , Chitosan , Ethanol , Smartphone , Biosensing Techniques/methods , Fluorescent Dyes
Agricultural value chains worldwide provide essential support to livelihoods, ecosystem services, and the growing bioeconomy. The coronavirus disease 2019 (COVID-19) pandemic slowed down or reversed decades of agricultural growth and exposed the vulnerabilities of farmers and the food insecure in Africa, thus reiterating the need to build resilience, agility, and adaptability for a sustainable agriculture. Existing social, political, environmental, and economic challenges demonstrate that a path to faster sustainable growth is increased productivity through improved input quality, of which technical inputs are a part. This work presents a perspective calling for African innovative competence in technological and methodological applications and solutions as part of the most critical area of a holistic organization for social progress. It finds that while performances of previous agricultural transformation efforts offer insights for future directions, novel pathways fitting to the diversity of situations and contexts on the continent are needed. These may include vertical agriculture in land-constrained regions to grow high-value products, ocean or sea farming in coastal regions, development of multiple-harvesting crops, and self-replicating plants. Developing standards that integrate current scientific methodologies and technologies with indigenous knowledge for agricultural growth and disaster management will bring the complementary benefits of both worlds into optimal development.
Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson's correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility.
In the current study, we have utilized semi-enclosed, leak-proof, microfluidic paper-based analytical devices (µPAD's) modified with isatin conjugated chitosan as specific colorimetric reagent for the detection of proline. Proline is one of the globally accepted stress biomarker in plants and also one of the prominent amino acid present in wine and some processed food. Quantification of proline is regularly required in agriculture field, food and wine industries. Specific interaction of isatin with proline, uniform film forming ability of chitosan which results in uniform coloration and the presence of leak-proof layer which prevent the diffusion of colorimetric reagent deeper resulted in enhancement of color signal intensity at the reaction zone were utilized. Further, the images of the µPAD's were captured using smartphone with 3D printed imaging box which houses smartphone and µPAD's. This platform utilizes smartphone flash for uniform illumination and ensures constant positioning of µPAD's to capture images. This greatly enhances the sensitivity and accuracy of our platform. Compared to previously published highly sensitive multi-layer, paper-based platform for detection of proline, current method has enhanced detection range (â¼7 fold) and has comparable limit of detection of 23.75 µM. Moreover, the developed µPAD's platform has reduced optimum reaction temperature and time compared to previous work. The developed paper based platform was utilized for evaluation of proline content in young Arabidopsis plants which are subjected to water stress for 5 days. The devised paper-based methods have the potential to be applicable for the on-site evaluation of various stresses in plants.
Arabidopsis , Chitosan , Isatin , Arabidopsis/chemistry , Colorimetry/methods , Indicators and Reagents , Lab-On-A-Chip Devices , Paper , Proline
Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system's internal environment with the highest occurring in May. In May, an average change of -0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.
Plant production systems such as plant factories and greenhouses can help promote resilience in food production. These systems could be used for plant protection and aid in controlling the micro- and macro- environments needed for optimal plant growth irrespective of natural disasters and changing climate conditions. However, to ensure optimal environmental controls and efficient production, several technologies such as sensors and robots have been developed and are at different stages of implementation. New and improved systems are continuously being investigated and developed with technological advances such as robotics, sensing, and artificial intelligence to mitigate hazards to humans working in these systems from poor ventilation and harsh weather while improving productivity. These technological advances necessitate frequent retrofits considering local contexts such as present and projected labor costs. The type of agricultural products also affects measures to be implemented to maximize returns on investment. Consequently, we formulated the retrofitting problem for plant production systems considering two objectives; minimizing the total cost for retrofitting and maximizing the yearly net profit. Additionally, we considered the following: (a) cost of new technologies; (b) present and projected cost for human labor and robotics; (c) size and service life of the plant production system; (d) productivity before and after retrofit, (e) interest on loans for retrofitting, (f) energy consumption before and after retrofit and, (g) replacement and maintenance cost of systems. We solved this problem using a multi-objective evolutionary algorithm that results in a set of compromised solutions and performed several simulations to demonstrate the applicability and robustness of the method. Results showed up to a 250% increase in annual net profits in an investigated case, indicating that the availability of all the possible retrofitting combinations would improve decision making. A user-friendly system was developed to provide all the feasible retrofitting combinations and total costs with the yearly return on investment in agricultural production systems in a single run.
The growing importance of rice globally over the past three decades is evident in its strategic place in many countries' food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice's physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.
The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000-2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.
Most bone tissue engineering models fail to demonstrate the complex cellular functions of living bone; therefore, most translational studies on bone tissue are performed in live models. To reduce the need for live models, we developed a stimulated microchip model for monitoring protein secretion during osteogenesis using human mesenchymal stem cells (hMSCs). We established a bone microchip system for monitoring the in vitro differentiation and sensing the secreted proteins of hMSCs under a sinusoidal electromagnetic field (SEMF), which ameliorates bone healing in a biomimetic natural bone matrix. A 3 V-1 Hz SEMF biophysically stimulated osteogenesis by activating ERK-1/2 and promoting phosphorylation of p38 MAPK kinases. Exposure to a 3 V-1 Hz SEMF upregulated the expression of osteogenesis-related genes and enhanced the expression of key osteoregulatory proteins. We identified 23 proteins that were differentially expressed in stimulated human bone marrow mesenchymal stem cell secretomes or were absent in the control groups. Our on-chip stimulation technology is easy to use, versatile, and nondisruptive and should have diverse applications in regenerative medicine and cell-based therapies.
Electromagnetic Fields , Mesenchymal Stem Cells , Bone Marrow Cells , Cells, Cultured , Humans , Osteogenesis/physiology
We developed a high-throughput technique for highly sensitive measurement of trace amounts of proline, an indicator of drought stress in plants, using an optimized proline-ninhydrin reaction. In order to do this, proline detection time was minimized by omitting phosphoric acid from the ninhydrin reagent. Chromophore extraction using toluene was also omitted, thus lowering the risks to environment and human health, and allowing the use of readily available polystyrene plates. Proline detection sensitivity was assessed based on the concentration of sulfosalicylic acid in the solution, which indicated that 1% sulfosalicylic acid yielded the best sensitivity and linearity. These findings were applied to a multiwell plate-based multiplex analysis using a dry oven for the simultaneous analysis of a large number of drought-stressed plant samples with trace amounts of proline. The results showed that proline could be effectively detected in plants grown in soil with water content under 5%, demonstrating its potential for diagnosing drought early. The proposed multiwell plate-based multiplex assay is expected to be useful in manifold agricultural applications.
Arabidopsis/metabolism , Ninhydrin/chemistry , Plant Leaves/metabolism , Proline , Proline/analysis , Proline/metabolism , Stress, Physiological , Water
