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. 2026 Oct 5;11(41):62606–62615. doi: 10.1021/acsomega.6c03525

A Smartphone-Based Silver Nanoparticle Assay for Detecting Enhanced Gamma-Aminobutyric Acid Content in Germinated Rice via Metabolic Modulation

Pan Tongraung 1, Ladnaphone Hadaoheuang 1, Piyathida Piyasakulwong 1, Pichamon Phonraksa 1, Supakan Rattanakon 1,*
PMCID: PMC13688463  PMID: 42923339

Abstract

This study aimed to increase the γ-aminobutyric acid (GABA) content in rice grains and develop surface-modified silver nanoparticles (AgNPs) as a simple colorimetric chemosensor for its detection. To stimulate GABA production, rice grains from the Kor Kho 6 (RD6) and Khao Dawk Mali 105 (KDML105) cultivars were treated with various solutions targeting the GABA metabolic pathways. Results demonstrated that simultaneously supplementing the GABA substrate with 2% (w/v) monosodium glutamate and inhibiting GABA transaminase with a 2% (w/v) rosmarinic acid solution yielded the highest GABA content. For quantitative detection of GABA, surface-modified AgNPs with citrate (Cit-AgNPs) were synthesized and characterized using UV–visible spectrophotometry, transmission electron microscopy (TEM), and dynamic light scattering (DLS). The Cit-AgNPs successfully quantified GABA levels, as evidenced by a colorimetric shift from yellow to green. This transition corresponded to a decrease in absorbance at 393 nm and the emergence of a shoulder peak in the 450–550 nm region. This sensing mechanism was subsequently integrated with the PhotoMetrix smartphone application, offering a practical and accessible platform for GABA quantification. Analysis of RGB color intensity revealed a linear relationship between the green color intensity and GABA concentration in the range of 50–500 ppm (R 2 = 0.9757). The assay demonstrated a limit of detection (LOD) of 44.20 ppm and a limit of quantification (LOQ) of 147.00 ppm. Furthermore, the GABA content measured in rice grains using this smartphone-based method closely aligned with the standard high-performance liquid chromatography (HPLC) results, exhibiting an error margin of approximately 5%. These findings indicate that this smartphone-based method holds significant potential for practical application in resource-limited testing facilities.

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Introduction

Rice (Oryza sativa L.) plays a crucial role in global food consumption, serving as a staple food for more than 3.5 billion people worldwide. Although polished rice is widely preferred because of its soft texture and desirable flavor, the milling process removes the rice germ and bran, which contain many essential nutrients. To address this nutrient loss, germinated brown rice has emerged as an alternative product that offers improved quality and favorable taste. During the germination process, γ-aminobutyric acid (GABA), a nonprotein amino acid, increases in concentration. GABA serves as an essential component of brain metabolism, acting as an inhibitory neurotransmitter in the mammalian central nervous system. The consumption of GABA-enriched germinated brown rice has been associated with several health benefits. , These reported benefits include relief from stress and anxiety, regulation of blood pressure, lipid- and glucose-level management, improvement of memory, and suppression of cancer-cell proliferation. These potential health advantages have led to increased interest in germinated brown rice as a healthier product.

The GABA-enriched method in grain crops by germination has been reported in many studies. − There are various metabolic pathways involved in the biosynthesis of GABA during the germination of grain crops. Mainly, GABA can be synthesized through the GABA shunt, which involves the decarboxylation of glutamate (Glu) by glutamate decarboxylase (GAD) to form GABA in the cytoplasm. Subsequently, GABA is transported to the mitochondria and converted to succinic semialdehyde by GABA transaminase (GABA-T). Previous studies reported an increase in GAD activity during the germination of brown rice, oats, and soybeans, , indicating an upregulation of the GABA shunt pathway. Additionally, supplementing the concentration of Glu, which serves as the substrate for GAD in the GABA shunt, was also found to increase GAD activity in yeast and rice germ. , On the other hand, inhibiting GABA-T was another way to increase the GABA content in grains. Rosmarinic acid extracted from lemon balm was reported as an effective in vitro inhibitor of rat brain GABA-T activity. Combining GABA substrate supplementation with GABA-T inhibition represents a promising metabolic modulation strategy to further enhance the GABA content in rice grains.

Detection and quantification of GABA content are generally performed by using high-performance liquid chromatography (HPLC) due to its high accuracy and sensitivity. However, this method requires expensive equipment, specialized training, and is often time-consuming. This can limit its practicality for some applications, especially those that require rapid analysis or lack access to sophisticated laboratory facilities. Chromogenic sensors and enzyme-based sensors are promising alternative approaches to detect GABA content utilizing a colorimetric approach. , However, the synthesis of these sensors is complicated and environmentally unfriendly. Silver nanoparticles (AgNPs) can be used as chromogenic sensors with several advantages, especially simple synthesis under mild conditions. , AgNPs have distinctive color-changing properties depending on their size and structure due to surface plasmon resonance (SPR). A previous study reported the detection of GABA in food supplements using modified-surface AgNPs with citrate. This method can determine the concentration of GABA based on a vivid color change in the solution. The detection range for GABA about 100–500 ppm with 57.7 ppm of limit of detection (LOD) was primarily obtained from UV–vis spectroscopy. However, it still requires expensive instruments and is difficult to use for rapid analysis for GABA detection.

Consider developing portable sensing devices or test kits that utilize the color change of AgNPs as an indicator of the GABA content. These devices could potentially be designed to work with smartphones, making them accessible for field applications. − The color change can be analyzed by image analysis software on a smartphone such as PhotoMetrix, which analyzes the red, green, and blue (RGB) color intensities of the image. A previous study reported that surface-modified AgNPs with citrate and tannic acid were used to quantify the content of Cr3+ in food supplements. The PhotoMetrix application monitored the intensity of red color appearing in the image, which was directly related to the Cr3+ content in food supplements. The surface-modified AgNPs with citrate, melamine, and EDTA were also reported on quantification of Mn2+ in water samples using PhotoMetrix. For the quantification of GABA, surface-modified AgNPs with citrate were also used as mentioned above, and the GABA content in food supplements was determined based on a spectroscopic technique. To the best of our knowledge, there has been no report on the detection of GABA using AgNPs in conjunction with a rapid smartphone-based method.

While advancing biochemical enrichment strategies is essential for producing GABA-elevated functional rice, the practical implementation and optimization of these techniques are frequently bottlenecked by conventional verification methods such as UV–vis and HPLC. To overcome this analytical limitation, this study introduces an integrated produce-and-verify platform that pairs functional grain enrichment with a rapid analytical companion. Therefore, this research aims to increase the GABA content in rice grains by supplementing Glu and rosmarinic acid and to develop a method for quantifying GABA in rice by combining modified-surface AgNPs and smartphone-based methods. To validate our method, GABA in all samples was also determined using standard HPLC. This work bridges the gap between agricultural biotechnology and accessible digital diagnostics, enabling immediate screening and reliable quantification directly from treated grain extracts, which provides a more cost-effective, easy-to-use, and deployable approach for resource-limited settings to monitor GABA in rice grains.

Materials and Methods

Chemicals and Materials

All reagents were analytical grade and used without further purification. Silver nitrate (AgNO3, ≥99.0%, product code: 209139), sodium borohydride (NaBH4, ≥99%, product code: 213462), sodium citrate dihydrate (Na3C6H5O7·2H2O, ≥99%, product code: S1804), gamma-aminobutyric acid (NH2(CH2)3COOH, ≥99%, product code: A2129), 2-hydroxynaphthaldehyde (HOC10H6CHO, product code: H45353), monosodium glutamate (C5H8NO4Na, ≥99%, product code: G1626), rosmarinic acid (C18H16O8, ≥98%, product code: R4033), boric acid (H3BO3, ≥99.5%, product code: B6768), glacial acetic acid (CH3COOH, ≥99%, product code: A6283), and sodium acetate (CH3COONa, ≥99%, product code: S2889) were purchased from Sigma-Aldrich. All metal salts used for the interference study (including KCl (≥99.0%, product code: 9541), CaCl2 (≥96.0%, product code: C5670), Zn­(ClO4)2 (product code: 401439), MnCl2 (≥99.0%, product code: 244589), and Fe­(ClO4)3 (product code: 309281)) were also obtained from Sigma-Aldrich. Solvents used for HPLC analysis, including ethanol (≥99.9%, product code: AR1069) and methanol (≥99.9%, product code: LC1115), were obtained from RCI Labscan (Ireland). All aqueous solutions were prepared using Milli-Q deionized water (18.2 MΩ·cm). Kor Kho 6 (RD6) and Khao Dawk Mali 105 (KDML105) rice grains were obtained from the Rice Department, Bangkok, Thailand.

Instrumentation

High-performance liquid chromatography (HPLC, Agilent Technologies 1260 Infinity) was used to quantify the GABA content. UV–visible spectroscopy measurements were carried out using a UV–visible spectrophotometer (Shimadzu, Tokyo, Japan) from 300 to 800 nm using a quartz cuvette (1 cm path length). The determination of particle size, surface charge, and distribution of the synthesized AgNPs was performed using a Zetasizer Nano ZSP (Malvern Instruments Ltd., Malvern, UK) and by transmission electron microscopy (TEM, Philips TECNAI 20). The pH of all solutions was measured by using a pH meter (Mettler Toledo FiveEasy Plus FP20).

Rice Samples and Treatment Conditions

The two most consumed rice cultivars in Thailand, RD6 and KDML105, in rice grain form, were used in this study. Rice grains were sterilized with chlorine gas (household bleach with HCl) for 1 h. Samples in the ungerminated condition were indicated as no-soaking samples. Rice grains of each cultivar were treated with different conditions. For normal germinated condition, rice samples were soaked in deionized water at room temperature and placed in the dark to allow the germination process for 72 h. For the germinated condition with solutions related to the pathway of GABA metabolism, rice grains were soaked in monosodium glutamate (MSG) serving as a substrate for GAD in the GABA shunt and soaked with rosmarinic acid (RA) for inhibiting GABA-T. The treatment solutions included 1% (w/v) MSG, 2% (w/v) MSG, 2% (w/v) MSG with 1% RA (w/v), and 2% MSG (w/v) with 2% RA (w/v). After the treatments, all samples were rinsed with deionized water for 3 s, then dried in a hot-air oven at 60 °C for 1 h before grinding to powder.

Extraction of GABA from Rice

The GABA in rice samples was extracted by following a previous method with some modifications. , Briefly, rice powder (0.50 g) was placed in a microcentrifuge tube followed by the addition of 2 mL of 80% (v/v) ethanol, and the mixture was shaken on a vortex mixer for 5 min. The sample was centrifuged (13,500 rpm) for 10 min, and the upper portion was collected and dried on a rotary evaporator until the complete evaporation of ethanol from the extract. The dried residue was dissolved in methanol/deionized water (1:1) for further determination of GABA content.

Detection of GABA by HPLC

The modified derivatization process of GABA with 2-hydroxynaphthaldehyde (HN) was carried out. , Initially, the extracted solution (1 mL) was mixed with 0.6 mL of 1 M borate buffer (pH 8) and then treated with 1 mL of HN (0.3% (w/v) in methanol). The mixing solution was heated at 80 °C for 10 min in a water bath. The solution was allowed to cool to room temperature, and then the volume was adjusted to 5 mL with methanol. The derivatized solution was filtered through a 0.45-μm membrane filter before injecting the sample into the HPLC. The solution (10 μL) was injected onto a Phenomenex C18 (150 mm × 4.6 mm, 5 μm) and eluted with methanol:water (66:34 v/v) at a flow rate of 0.5 mL/min. The detection wavelength was set at 330 nm.

Synthesis and Characterization of Cit-AgNPs

Citrate-capped silver nanoparticles (Cit-AgNPs) were synthesized by mixing 39 mL of 0.64 mM AgNO3 with 1 mL of a citrate solution and stirring for 20 min. Subsequently, 0.1% (w/v) NaBH4 was added to the mixture, and the mixture was then stirred continuously for 1 h to complete the reduction process. The size and morphology of Cit-AgNPs were investigated using high-resolution transmission electron microscopy (TEM), UV–visible spectrometry, and dynamic light scattering nanoparticle size analysis (DLS). The characterization of Cit-AgNPs was conducted after the solution was allowed to stand at room temperature for more than 30 days.

Detection of GABA by AgNPs

An aliquot of 1.00 mL of the synthesized Cit-AgNPs was mixed thoroughly with 1.00 mL of GABA extract from rice and 0.30 mL of 0.7 M acetate buffer (pH 3.8). The mixture was allowed to react for 8 min prior to image acquisition. The sensing mechanism relies on the adsorption of GABA onto the silver nanoparticles and the subsequent electrostatic aggregation, resulting in a visible color change from yellow to green (Figure a). To eliminate ambient light interference, image acquisition was performed inside a white studio box (22 cm width × 23 cm height × 40 cm depth). Solutions were illuminated by a constant LED light source positioned 20 cm above the samples, and images were captured from a side-view perspective at a fixed focal length of 6 cm (Figure b).

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Schematic representation of the complete smartphone-based silver nanoparticle assay. (a) Overview of the sensing mechanism, highlighting GABA adsorption onto silver nanoparticles, subsequent aggregation, and the resulting color change. (b) The controlled physical imaging setup within the studio box. (c) The digital image processing and data analysis workflow.

For the digital image processing and data analysis flow to quantify GABA content (Figure c), the Cit-AgNPs system was coupled with the PhotoMetrix application to measure RGB (Red, Green, Blue) intensity values from the images taken by the application. Within the software, a defined Region of Interest (ROI) of 32 × 32 pixels was selected for each image extraction. The calibration curve was established utilizing the calibration option within the application’s univariate analysis mode. Subsequently, the sampling option (as the prediction module) was utilized to determine the exact GABA concentrations in the unknown samples. To eliminate systematic bias and ensure data reliability, a single smartphone (Android, Vivo V2043) was used for all image captures. All images were captured directly through the built-in camera interface of the PhotoMetrix application. This software functions as an in situ chemical analysis and colorimetry tool. It utilizes the main camera to capture the sample images and the acquired images are then instantly decomposed into their RGB values.

Data Analysis

Data were collected as three replicates of rice samples for each treatment. For statistical analysis, data are shown as mean ± standard deviation (SD). The GABA content in the different groups was analyzed by two-way ANOVA. Differences were significant when the p value was less than 5% (p < 0.05).

Results and Discussion

Increase in GABA Content in Germinated Rice with Cyclic Treatment

The rice grains of RD6 and KDML105 cultivars were treated with solutions targeting the GABA metabolic pathway for 72 h. These treatments were compared to a control group of unsoaked rice grains (no soaking). The experimental groups were designated as water, 1% MSG, 2% MSG, 2% MSG + 1% RA, and 2% MSG + 2% RA. Figure illustrates the resulting GABA content for each treatment as quantified by HPLC. The obtained HPLC chromatogram (Figure S1) exhibits a pattern of the GABA derivative with HN that is consistent with previous research. A standard curve for GABA was generated across a concentration range from 0.0097 to 97 mM (Figure S2), demonstrating a strong linear relationship (R 2 = 0.9994). A two-way ANOVA revealed significant differences for treatment, variety, and their interaction in the GABA content (Table S1).

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GABA content in rice grains of RD6 and KDML105 cultivars germinated with solutions related to the pathway of GABA metabolism for 72 h as determined by HPLC.

Across both varieties, grains subjected to the soaking solution exhibited higher GABA levels compared to the unsoaked control. This elevation is attributed to the stimulation of GABA metabolic modulator driven by enhanced GAD activity. , These findings align with previous studies demonstrating that steeping brown rice in water effectively increases GABA content. , Supplementing with glutamate (Glu) via 2% (w/v) MSG serving as a substrate for GAD in the GABA shunt was found to have higher GABA levels compared to 1% (w/v) MSG. These MSG concentrations were chosen because they induce normal to mild salt stress that maximizes GABA accumulation without causing the severe osmotic stress that would otherwise inhibit rice grain germination. In a comparison between soaking rice grains in water and 2% (w/v) MSG, the results showed that GABA content increased from 105.84 to 133.20 ppm for RD6 and from 131.38 to 146.13 ppm for KDML105. Therefore, 2% (w/v) MSG was chosen to further complement different concentrations of RA. The combination of supplementing the GABA substrate and inhibiting GABA-T with 2% (w/v) RA showed the highest GABA levels, at 191.86 ppm for RD6 and 174.72 ppm for KDML105. This method can be applied in functional food development to increase the GABA content in rice grains.

Characterization and Optimization of Cit-AgNPs

The UV–visible spectrum of the synthesized Cit-AgNPs, exhibiting an SPR peak at 393 nm, is shown in Figure a. Upon interaction with GABA, the signal intensity of this primary SPR peak at 393 nm decreased significantly from 0 to 30 min, which corresponds to nanoparticle aggregation and the visually observed color transition of the solution from yellow to green (Figure b). Concurrently, the time-dependent data revealed a slight shift of this primary peak toward longer wavelengths, along with the distinct emergence of a shoulder peak in the 450–550 nm region. The formation of this shoulder peak is an optical signature of interparticle plasmon coupling, confirming the active electrostatic aggregation of the nanoparticles rather than light scattering from precipitation. Characterization of the Cit-AgNPs by TEM showed that Cit-AgNPs had a spherical shape with an average size of 26.8 nm, and they were dispersed in solution (Figure a) corresponding to a zeta potential of −45.0 mV (Figure S3a). To evaluate the replicability of the synthesis process, three independent batches of Cit-AgNPs were prepared under identical optimized conditions. UV–vis spectrophotometry and TEM analysis revealed high batch-to-batch consistency, with the characteristic SPR peak consistently appearing at 393 nm and an average particle size of 22.6 ± 3.8 nm across all batches. Furthermore, the stability of the synthesized Cit-AgNPs was closely monitored over time. The Cit-AgNPs were very stable for longer than 1 month with less than a 1% decrease in the initial absorbance (Figure S4). This sustained stability is attributed to the strong electrostatic repulsion provided by the citrate capping, as evidenced by a highly negative zeta potential of −45.0 mV.

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Optical characterization and time-dependent aggregation of Cit-AgNPs. (a) UV–vis absorption spectrum of synthesized Cit-AgNPs showing an SPR peak at 393 nm. (b) Time-dependent UV–vis absorption spectra comparing Cit-AgNPs (0 ppm GABA) with GABA-induced aggregation (300 ppm GABA) monitored from 0 to 30 min.

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TEM images of (a) Cit-AgNPs without GABA (inset: statistical particle-size distribution histogram), and (b) aggregated Cit-AgNPs clusters formed upon interaction with 300 ppm GABA.

At pH 3.8, the negatively charged surface of the Cit-AgNPs engaged in strong electrostatic interactions with the positively charged GABA molecules. The acidic pH of 3.8 is critical for this detection mechanism, as it ensures that the amine groups of GABA are highly protonated (−NH3 +), providing the necessary positive charge to facilitate binding. The result from TEM also confirmed that the particles of Cit-AgNPs induced by GABA were aggregated (Figure b) with a corresponding zeta potential of −22.5 mV (Figure S3b), thereby confirming the formation of the complex. To corroborate the TEM measurements and provide a thorough analysis of particles in solution, dynamic light scattering (DLS) was performed (Figure S5). The DLS size distribution curves provide quantitative evidence of the sensing mechanism. The dispersed Cit-AgNPs exhibited a narrow size distribution, confirming the absence of initial aggregation. Upon the addition of GABA, immediate electrostatic coupling occurred, causing the diameter distribution to shift to 43.68 nm. Following a 30 min incubation, multiparticle aggregation occurred, shifting the diameter into the hundreds of nanometers. Consistent with a previous study, the protonated amine groups on GABA act as molecular bridges that induce the aggregation of the citrate-capped AgNPs.

The results for the optimal buffer concentration and reaction time for effective colorimetric sensing of GABA using Cit-AgNPs are shown in Figure . Different concentrations of acetate buffer at pH 3.8 ranging from 0.1 to 1.0 M were studied with 50, 250, and 400 ppm of GABA. The intensity of the green color from the captured images was plotted against different concentrations of buffer acetate. The optimal concentration of acetate buffer was 0.7 M which can differentiate the intensity of the green color treated with 50, 250, and 400 ppm of GABA (Figure a). Subsequently, the time required for the signal to reach saturation was determined by comparing the decrease in SPR absorbance at 393 nm every 30 s after mixing the colloidal suspension with 0, 100, and 300 ppm of GABA (Figure b). The absorbance gradually decreased and stabilized after 8 min; thus, an 8 min reaction time was selected as the standard reaction time to ensure complete signal saturation for all subsequent GABA detection.

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Optimal conditions for (a) different concentrations of buffer acetate from the intensity of green color and (b) reaction time on the SPR absorbance. Error bars represent the SD of three independent measurements (n = 3).

To evaluate the assay’s selectivity, the potential interference of common cations found in rice grains (K+, Ca2+, Zn2+, Mn2+, and Fe3+) was investigated. The different concentrations (50 and 100 ppm) of these cations were tested against a 250 ppm GABA standard using the Cit-AgNP detection method (Figure a). The results demonstrated valence-dependent behavior. Because K+ did not affect the smartphone-based AgNP colorimetric method at 50 and 100 ppm, a series of four higher concentrations (150, 200, 250, and 300 ppm) was tested to determine its upper tolerance limit (Figure b). This analysis revealed that a concentration-dependent increase in ΔGreen intensity occurred as K+ levels rose from 200 to 300 ppm. Conversely, because Fe3+ already induced color change at 50 and 100 ppm, a series of four lower concentrations (10, 20, 30, and 40 ppm) was investigated (Figure c). The data demonstrated that while interference was minimal at 10 ppm, the ΔGreen intensity response increased from 20 to 40 ppm. This targeted investigation confirmed that the sensing system possesses tolerance to monovalent salts up to 150 ppm and divalent salts up to 50 ppm. This interference is attributable to the high charge density of Fe3+ ions, which induces the nonspecific aggregation of the nanoparticles. Similarly, Ca2+, Zn2+, and Mn2+ were found to interfere with the assay at the higher concentration of 100 ppm. Among the tested cations, only K+ exhibited no interference with GABA detection. In natural rice grains, K+ possesses the highest concentration, exceeding 100 ppm, but it does not disrupt the assay. Conversely, the other trace minerals naturally occur at concentrations below 100 ppm, with Fe3+ specifically found at levels lower than 10 ppm. , Therefore, the endogenous concentrations of these ions are insufficient to affect the colorimetric detection of GABA.

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Effect of metal ion interference on 250 ppm GABA detection using Cit-AgNPs. (a) Screening of K+, Ca2+, Zn2+, Mn2+, and Fe3+at 50 and 100 ppm. (b) High-concentration evaluation of K+ (150–300 ppm). (c) Low-concentration evaluation of Fe3+ (10–40 ppm). Error bars represent the SD of three independent measurements (n = 3).

Smartphone-Based Approach for the Detection of GABA Content in Rice

Under the optimal conditions, the Cit-AgNPs were titrated with different concentrations of GABA ranging from 0 to 500 ppm, and the resulting reactions were measured by UV–vis spectrophotometry. As shown in Figure a, the SPR intensity of the colloidal solution at 393 nm decreased with increasing GABA concentration. A calibration curve was plotted between absorption intensity changes A-A0 (λ393) of Cit-AgNPs and the concentrations of GABA in the range of 50–500 ppm (Figure b). The analysis demonstrated a linear relationship with 0.9901 of R 2, 36.90 ppm of LOD, and 123.10 ppm of LOQ.

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(a) UV–visible titration spectra of Cit-AgNPs with increasing concentration (0–500 ppm) of GABA and (b) linear relationship between the absorption intensity changes A–A0 (λ393) of Cit-AgNPs and the concentrations of GABA in the range of 50–500 ppm. Error bars represent the SD of three independent measurements (n = 3).

To develop an accessible method for determining GABA, the color intensity of the Cit-AgNPs with GABA was detected through the PhotoMetrix smartphone application. According to the results in Figure , the green (G) channel was selected for the calibration curve because it exhibited the optimal linear response, yielding a correlation coefficient (R 2) of 0.9757. In contrast, neither the red (R) channel (R 2 = 0.4062) nor the blue (B) channel (R 2 = 0.8981) achieved the linear proportionality required for reliable analytical assay development (Figure S6). This linear relationship between GABA concentration (50–500 ppm) and green color intensity resulted in an LOD of 44.20 ppm and an LOQ of 147.00 ppm. These limits were calculated using the formulas LOD = 3s/slope and LOQ = 10s/slope, respectively, where “s” represents the standard deviation of five independent blank measurements (the mean green color intensity for the blank was 73.2, with an SD of 1.64). Therefore, these results demonstrate that the proposed smartphone-based method offers a simple, straightforward approach for GABA detection.

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Linear relationship between color intensities in the green channel and the concentrations of GABA in the range of 50–500 ppm ,derived from univariate calibration analysis mode of the PhotoMetrix application. Error bars indicate the SD of three independent replicates (n = 3).

To demonstrate the accessibility of the proposed method, GABA from different rice grain samples was extracted. After adding the prepared sample solutions (1.0 mL) into 1.0 mL of Cit-AgNPs, the mixtures were kept for 8 min and the taken images were analyzed via the sampling option. The smartphone detection results of each sample were compared with those of the HPLC standard method (Table ). The level obtained from the proposed method was in close agreement with that obtained by the standard method, with ∼ 5% error compared to the standard method.

1. Comparison Analysis between the Smartphone-Based Method and the HPLC Method for the Quantification of GABA in a 1 mL Extracted Rice Sample Solution.

Rice cultivars Treatments Smartphone-based method (ppm) HPLC method (ppm) % Error Average of % error
RD6 Water 109.51 108.77 0.68 1.64
106.32 104.51 1.73
107.00 109.75 2.50
2% MSG 128.44 129.65 0.93 3.82
128.04 134.72 4.95
107.36 101.67 5.59
KDML105 Water 101.67 97.19 4.61 5.43
124.62 133.06 6.35
125.56 132.61 5.32
2% MSG 115.36 116.57 1.04 5.04
130.87 140.14 6.61
107.18 99.71 7.48
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To further evaluate the accuracy and practical applicability of the proposed method in real matrices, a standard addition recovery study was performed on rice grain extracts. Known amounts of GABA standard (50, 100, and 200 ppm) were spiked into KDML105 sample solutions. As detailed in Table S2, the unspiked KDML105 extract exhibited baseline endogenous GABA concentration of 120.58 ppm. Upon spiking, the analytical recoveries of the added GABA ranged from 103.18% to 107.58%, with relative standard deviations (RSDs) below 12% (n = 3). These recovery rates demonstrate that the Cit-AgNP colorimetric assay, coupled with a smartphone-based method, is reliable for quantifying GABA in real agricultural samples without significant matrix interference (including other amino acids, organic acids, and sugars) and in the high-ionic strength environments of the biological extracts. , Therefore, this method would be a significant advancement in making GABA analysis more accessible for nutritional and health purposes in rice-producing regions.

Conclusion

Overall, this research demonstrates a streamlined approach for the metabolic enhancement and subsequent detection of GABA in rice in resource-limited environments. By targeting the metabolic pathway via precursor supplementation and transaminase inhibition, GABA concentrations in the RD6 and KDML105 cultivars were significantly increased. Crucially, we coupled this agronomic advancement with a newly developed smartphone-based colorimetric platform using Cit-AgNPs to quantify GABA levels. The integration of these two techniques offers a scalable, innovative, and highly practical solution for GABA analysis and future agricultural development.

While the developed Cit-AgNP colorimetric assay and smartphone-based quantification significantly enhance the accessibility of GABA detection, it is important to acknowledge the limitations of the current overall workflow regarding on-site field applications. Although the detection phase bypasses the need for high-end analytical instrumentation such as HPLC, the sample preparation still relies on laboratory infrastructure. The extraction protocol requires the use of organic solvents (ethanol and methanol), which present environmental and safety considerations, necessitating proper chemical waste management. Therefore, the current method is best categorized as a highly accessible tool for resource-limited rather than a strictly field-deployable assay. Future work will need to focus on developing greener, solvent-free, and equipment-independent extraction protocols for full point-of-need GABA quantification.

Supplementary Material

ao6c03525_si_001.pdf (280.5KB, pdf)

Acknowledgments

This research project was supported by Srinakharinwirot University [Fundamental Fund: fiscal year 2023 by National Science Research and Innovation Fund (NSRF)] (Grant no. FF-044/2566).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.6c03525.

  • HPLC chromatograms of gamma-aminobutyric acid (GABA); standard calibration curve for GABA from HPLC; two-way ANOVA results evaluating the significant differences due to treatment; zeta potential of Cit-AgNPs without GABA and with GABA; storage stability profile of synthesized Cit-AgNPs; dynamic light scattering (DLS) size distribution analysis of Cit-AgNPs during GABA detection; linear regression analysis of red and blue color intensities; analytical recovery of GABA spiked into sample (PDF)

The authors declare no competing financial interest.

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