Biosensors and Bioelectronics 260 (2024) 116447 Available online 28 May 2024 0956-5663/© 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Development of the sustainable green nanosensor using corn silk extract for nitrate detection in leafy vegetables Monika Kundu a, * , Prameela Krishnan a , Ananta Vashist a , Shruti Sethi b , Rajesh Kumar c , Gautam Chawla d , Mukesh Kumar Dhillon e a Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India b Division of Food Science & Postharvest Technology, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India c Division of Agricultural Chemicals, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India d Division of Nematology, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India e Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India A R T I C L E INFO Keywords: Green nanosensor Corn silk extract Reduced graphene oxide Nitrate Leafy vegetables ABSTRACT 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- Fe 3 O 4 @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-Fe 3 O 4 @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)/cm 2 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 effec- tiveness of the nanosensor for food quality and safety evaluation. 1. Introduction Nitrate is a highly mobile ion applied as fertilizer or derived by microbiologically induced oxidation of numerous nitrogen-containing chemicals in agriculture. Heavy use of synthetic mineral fertilizers, such as ammonium nitrate, urea, anhydrous ammonia, and ammonium sulphate, in place of manure is very common to fulfil the growing de- mand for the vegetables in market. Due to excessive use of nitrogen fertilizers in agriculture, there can be high levels of accumulation of nitrates in cereals, vegetables and fodder crops (Ahmed et al., 2020). This is caused due to plants absorption of nitrates through their roots and transport them to aboveground parts through the xylem. Plants are source of nitrate exposure to human beings and animals. The accumu- lation of nitrate can be unevenly distributed over various plant parts. Researches studies have highlighted that leaves generally contain more nitrates than roots (Salehzadeh et al., 2020). While nitrate accumulation varies among different species, certain plant families, including Che- nopodiaceae (Spinach, beetroot and Swiss chard) (Colla et al., 2018), Amaranthaceae (amaranthus) (Onyango et al., 2012), Asteraceae (let- tuce) (SoYlemez et a., 2021), Brassicaceae (rucola, radish, and mustard) (Mazahar et al., 2015), and Apiaceae (celery and parsley) (Abd-Elkader and Alkharpotly, 2016), are known nitrate accumulators. Environ- mental factors such as humidity (Zhou et al., 2016), substrate water content (Aukhadieva et al., 2022) temperature (Chung et al., 2004), radiation, and photoperiod (N´ajera and Urrestarazu, 2019), as well as agricultural factors like nitrogen dosage (Ahmed et al., 2020) and availability of other nutrients (Bian et al., 2020), greatly affect the ni- trate content in vegetables. Excessive consumption of nitrate-rich foods * Corresponding author. E-mail address: gkmk07@gmail.com (M. Kundu). Contents lists available at ScienceDirect Biosensors and Bioelectronics journal homepage: www.elsevier.com/locate/bios https://doi.org/10.1016/j.bios.2024.116447 Received 27 March 2024; Received in revised form 16 May 2024; Accepted 27 May 2024