Development of electrochemical biosensor based on CNT-Fe3O4 nanocomposite to determine formaldehyde adulteration in orange juice.

An electrochemical biosensor was developed to determine formaldehyde (HCHO) adulteration commonly found in food. The current responses of various electrodes based on multiwalled carbon nanotubes (CNTs) and synthesized nanocomposite (CNT-Fe3O4) were measured using cyclic voltammetry. The nanocomposite based biosensor shows comparatively high sensitivity (527 µA mg/L-1 cm-2), low detection limit (0.05 mg/L) in linear detection range 0.05-0.5 mg/L for formaldehyde detection using formaldehyde dehydrogenase (FDH) enzyme. In real sample analysis, the low obtained RSD values (less than 1.79) and good recovery rates (more than 90%) signify an efficient and precise sensor for the selective quantification of formaldehyde in orange juice. The developed biosensor has future implications for determining formaldehyde adulteration in citrus fruit juices and other liquid foods in agri-food chain to further resolve global food safety concerns, control unethical business practices of adulteration and reduce the widespread food borne illness outbreaks.

Green nanosensor for precise detection of formaldehyde in fruits and vegetables extract.

Present study reports fabrication of a low cost and eco-friendly formaldehyde nanosensor based on green magnetite nanoparticles synthesized using Mango (Mangifera indica L.) tree leaves extract. The formaldehyde is found in air, water and food. When inhaled or consumed formaldehyde has carcinogenic effects on human health. In this study the cyclic voltammetry technique was used to characterize the performance of the nanosensor. The green nanosensor fabricated in this study, to detect formaldehyde, demonstrated good sensitivity (193.4 µA mg-1 Lcm-2) in linearity range 0.03-0.5 mg/L with low threshold detection limit (0.05 mg/L). The green nanosensor also showed shelf life of four weeks without considerable change in the initial peak oxidation current. The real sample analysis was performed in various fruits and vegetables (Litchi chinensis, Syzygium cumini, Solanum lycopersicum and Cucumis sativus). The recovery rates were more than 93 % in sample extracts for formaldehyde detection. The comparison of the nanosensor for detection of formaldehyde with the colorimetric sensor revealed that the green nanosensor reproducibility (RSD = 1.8 %) is better than colorimetric sensor (RSD = 3.23 %). The results from the comparative studies of green nanosensor with colorimetric sensor established the potential of the green nanosensor as a forefront technology for futuristic smart detection of formaldehyde.

Development of the sustainable green nanosensor using corn silk extract for nitrate detection in leafy vegetables.

Nitrate is prevalent in environment and present in foods of plant origin as part of nitrogen cycle. It is now one of the most pervasive and persistent contaminants in animal food chain. Present work is focussed on development of a novel green nanosensor using corn silk extract for nitrate detection in leafy vegetables (Spinacia oleracea, Amaranthus viridis and Amaranthus cruentus). The green reduced graphene oxide (rGO) and a nanocomposite (G-Fe3O4@rGO) was synthesized for the first-time using corn silk extract and used for fabrication of the nanosensor. Various characterization techniques were used to expose the optical, crystallographic and surface morphology details of the nanosubstrates. Electrochemical studies of the fabricated nanosensor were conducted using the electrochemical impedance spectroscopy (EIS) technique. The performance of NiR/G-Fe3O4@rGO/ITO green nanosensor was the best, in terms of the electrochemical performance parameters among different fabricated nanosensors in the study. The developed green nanosensor demonstrated high sensitivity of 122.1 Ohm/log(mg/L)/cm2 and lower limit of detection 0.076 mg/L for detection of nitrate in leafy vegetables. The green nanosensor exhibited higher recovery rates (>86%) and high precision in nitrate detection in leafy vegetables (RSD <5.2%). Validation studies were conducted with HPLC technique also. The results of green nanosensor were found in good agreement with HPLC studies (p < 0.05) highlighting the market acceptability with usefulness and effectiveness of the nanosensor for food quality and safety evaluation.

Sensitivity analysis of cultivar parameters to simulate wheat crop growth and yield under moisture and temperature stress conditions.

Sensitivity of cultivar input parameters were characterised on the outputs of yield and growth variables using a web based crop simulation model Web InfoCrop Wheat. The crop model was assessed for each combination of seventeen input cultivar parameters tested under moisture and temperatures stress conditions in four different ecological regions. Three model outputs, total dry matter at harvest, grain yield at harvest and duration of the crop were chosen for subsequent evaluation. The most dominant cultivar parameters were identified to be TPOPT (Optimum Temp), TTVG (Thermal time for germination to 50% Flowering), KDFMAX (Extinction coefficient of leaves at flowering), GNOCF (Slope of storage organ number/m2 to dry matter during storage organ formation), POTGWT (Potential storage organ weight) and PHOTOSENS (Photoperiod sensitivity) which were associated with growth, thermal time accumulation, leaf area index, grain number and photosensitivity. Comparison of crop simulations with all the cultivar parameters incorporated from the experimental observations and those with only the most sensitive cultivar parameters incorporated was performed. Outputs of the crop simulation were significantly correlated with results from the field experiments. The present study could save time and effort in generating all the cultivar parameters required to perform the crop simulation under moisture and temperature stress conditions. The most significant cultivar parameters (TPOPT, TTVG, KDFMAX, GNOCF, POTGWT and PHOTOSENS) identified through the sensitivity analysis conducted in this study could significantly simulate the crop growth and yield of wheat.