Simultaneous Determination of Multiple Amino Acids in Different Organs of Selenium-Enriched Radishes by High-Performance Liquid Chromatography

Abstract

Accurate profiling of amino acids and selenoamino acids is crucial for evaluating the nutritional quality of selenium-enriched crops. To provide a reliable and accessible tool for routine food monitoring, this study employed pre-column derivatization high performance liquid chromatography (HPLC) method for the simultaneous determination and compositional analysis of 17 standard amino acids, selenocystine (SeCys₂), and selenomethionine (SeMet) in various organs of selenium-enriched radish. Chromatographic separation was performed using a C18 column and a mobile phase of sodium acetate buffer (pH 5.25) and acetonitrile under gradient elution, with diode array detection (DAD) at 360 nm. Method validation demonstrated excellent linearity (R²) ≥ 0.995 for all 19 amino acids within their tested ranges. The limits of detection (LODs) and limits of quantitation (LOQs) were 0.06 to 0.21 mg/L and 0.19 to 0.68 mg/L, respectively. The spike recoveries ranged from 88.2% to 101.7%, while the intra-day and inter-day relative standard deviations (RSDs) were ≤3.09% and ≤4.25%, respectively. The levels of total, essential, selenoamino and taste-active amino acids in the leaves exceeded those in the taproot, with the highest total content of 2398.41 mg/kg found in leaves at the primary growth stage of the taproot. The total content of selenoamino acids ranged from 2.65 to 6.78 mg/kg. This method enables the simultaneous quantification of various amino acids, including selenoamino acids, in different organs of selenium-enriched radish throughout its entire growth period, providing a theoretical basis for the development of selenium-fortified products.

1. Introduction

This work aims to develop a cost-effective high-performance liquid chromatography (HPLC-DAD) method—coupled with mild Proteinase K extraction and pre-column derivatization using 2,4-dinitrochlorobenzene (DNCB)—for the simultaneous quantification of 19 amino acids, including critical selenoamino acids (SeMet and SeCys₂). Furthermore, this study seeks to systematically evaluate the spatial and developmental dynamics of these amino acids across different organs and growth stages of selenium-enriched radishes. Amino acids serve as both key indicators for evaluating food nutritional quality and core precursor substances that determine food flavor through the Maillard reaction [1]. Amino acids inherently elicit distinct taste sensations, including sourness, sweetness, bitterness, and umami. Consequently, taste-active amino acids, such as aspartic acid (Asp), glutamic acid (Glu), and alanine (Ala), play a pivotal role in the development of food flavor [2]. The composition and content of amino acids confer unique flavor characteristics to foods, directly influencing their sensory quality [3]. Selenium (Se) is an essential trace element for the human body that can substitute for sulfur atoms in sulfur-containing amino acids [4]. It possesses physiological functions including immune regulation, maintenance of thyroid homeostasis, and antioxidant activity [5]. Radish (Raphanus sativus L.), an annual or biennial herbaceous plant belonging to the genus Raphanus of the Brassicaceae family [6], holds significant economic, medicinal, and nutritional value [7,8], and is one of the most popular cruciferous vegetables [9]. Radish leaves are abundant in nutrients, which contribute to neutralizing free radicals and mitigating cellular damage induced by oxidative stress [10]. Furthermore, they enhance iron absorption, thereby ameliorating anemia [11,12]. Notably, isothiocyanates present in the leaves exhibit potent anticarcinogenic, chronic disease-preventive, and cytoprotective properties [13,14]. The taproot offers a crisp, tender texture, easy digestibility, and distinctive flavor, making it widely favored by consumers [15,16]. Additionally, radish exhibits strong selenium accumulation capacity [17]. Selenomethionine (SeMet) and selenocystine (SeCys₂) represent the two primary and most readily detectable organic selenium forms in selenium-enriched cruciferous vegetables [18,19]. They possess high bioavailability and low toxicity, making them the most ideal dietary sources of selenium for humans [20]. Selenomethionine, in particular, can be directly incorporated into the protein synthesis process, playing a crucial role in various human physiological functions [21]. Currently, the quantification of SeMet and SeCys₂ typically relies on hyphenated techniques such as high-performance liquid chromatography coupled with inductively coupled plasma mass spectrometry (HPLC-ICP-MS) [22], whereas standard amino acids are generally determined using other analytical instruments. Evaluating the nutritional quality of selenium-enriched crops using these distinct and separate analytical platforms is time-consuming, labor-intensive, and involves high operational costs. Therefore, establishing a unified and efficient analytical method capable of simultaneously determining SeMet, SeCys₂, and multiple standard amino acids holds significant importance. Such an approach not only streamlines the analytical workflow and reduces costs but also provides a more comprehensive evaluation of both the flavor characteristics and the selenium-associated nutritional value of selenium-enriched vegetables.

Various analytical methods are available for the determination of amino acids, including the use of amino acid analyzers (AAA) [23,24,25], capillary electrophoresis (CE) [26], gas chromatography (GC) [27], high-performance liquid chromatography (HPLC) [28,29,30,31], ion-exchange chromatography (IEC) [32], and liquid chromatography-tandem mass spectrometry (LC-MS) [33] Among these, AAA, LC-MS and HPLC are the most frequently employed techniques. In recent years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as a powerful and cutting-edge technique in the field of amino acid analysis. LC-MS/MS enables rapid, highly sensitive, and highly specific quantification and profiling of amino acids in complex food matrices. It typically eliminates the need for time-consuming derivatization steps, thereby significantly improving detection efficiency and reducing reagent consumption [34]. However, despite its superior analytical performance, LC-MS/MS presents certain limitations for routine, large-scale agricultural and food quality monitoring. The instrumentation is exceedingly expensive and requires highly specialized operational expertise. Furthermore, it is susceptible to matrix effects, which often necessitates the use of costly isotopically labeled internal standards [35]. Although the amino acid analyzer is a dedicated instrument for amino acid determination, the highly specialized nature of its post-column derivatization typically necessitates a standalone analytical system [36]. Consequently, pre-column derivatization coupled with high-performance liquid chromatography (HPLC) using C8 or C18 reversed-phase columns demonstrates significant advantages for routine laboratory applications. This approach enhances subsequent chromatographic separation and detection by pre-derivatizing amino acids. Its core advantages include stable derivatized products, streamlined and efficient workflows, and relatively low overall analytical costs. These characteristics collectively position it as one of the most favored practical analytical methods in this field [37,38]. Current HPLC-based methods for determining amino acid content primarily focus on standard amino acids. Hui et al. [39] employed reverse-phase high-performance liquid chromatography to measure multiple standard amino acids in three types of eggplant fruit following aqueous extraction. Shi et al. [40] hydrolyzed Morinda officinalis samples with hydrochloric acid, derivatized with phenyl isothiocyanate and triethylamine to determine multiple standard amino acids. However, there are limited reports on the simultaneous determination of various amino acids, including SeMet and SeCys₂, in different organs of selenium-enriched vegetables during their growth stages using pre-column derivatization-HPLC. This study provides a highly accessible, cost-effective, and robust analytical method. It meets the sensitivity requirements for quantifying standard amino acids and SeMet/SeCys₂ in selenium-enriched crops. This method makes the simultaneous analysis of standard and seleno-amino acids highly accessible, offering a practical, efficient, and reliable alternative for routine monitoring in broad agricultural and food laboratories lacking expensive LC-MS/MS systems.

In this study, radishes grown in the selenium-rich area of Enshi, China, were selected as the raw materials. An analytical method based on ultrasonic extraction and pre-column derivatization of high performance liquid chromatography was adopted to accurately and efficiently determine various amino acids including SeMet and SeCys₂. This study systematically characterized the composition of 19 amino acids in different organs of selenium-rich radishes throughout their growth cycle, providing a scientific basis for evaluating the nutritional quality of selenium-rich vegetables and offering essential data support for the development of selenium-fortified products.

2. Materials and Methods

2.1. Chemicals and Reagents

Seventeen standard amino acid standards (purity ≥ 99%) used in this study were purchased from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China): aspartic acid (Asp), glutamic acid (Glu), histidine (His), serine (Ser), arginine (Arg), glycine (Gly), threonine (Thr), proline (Pro), alanine (Ala), valine (Val), methionine (Met), cystine (Cys₂), isoleucine (Ile), leucine (Leu), phenylalanine (Phe), lysine (Lys), tyrosine (Tyr); selenomethionine (SeMet) (purity ≥ 98%) and selenocystine (SeCys₂) (purity ≥ 98%) were purchased from Stanford Chemical Co., Ltd. (Lake Forest, CA, USA); 2,4-Dinitrochlorobenzene (purity ≥ 99%) was purchased from Shandong Xiya Chemical Industry Co., Ltd. (Linyi, China); acetonitrile (HPLC grade) was purchased from Tedia (Fairfield, OH, USA); sodium acetate, hydrochloric acid, sodium bicarbonate, sodium carbonate, and acetic acid (all of analytical grade) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China); the water used in the experiment was ultrapure water.

2.2. Solution Preparation

Individual standard stock solution (1000 mg/L): An appropriate amount of each amino acid standard was accurately weighed, dissolved, and diluted to volume in a 100 mL volumetric flask using 0.1 mol/L hydrochloric acid (HCl) solution. The prepared solutions were stored at 5 °C for further use.

Mixed standard stock solution (500 mg/L): Appropriate aliquots of each individual standard stock solution were pipetted, mixed thoroughly, and diluted to the final volume with the HCl solution.

Mixed standard solutions: An appropriate volume of the 500 mg/L mixed standard stock solution was diluted with 0.1 mol/L hydrochloric acid to obtain a series of mixed standard solutions with concentrations of 5, 10, 25, 50, 75, and 100 mg/L.

Derivatization solution: 2,4-Dinitrochlorobenzene (1.5000 g) was weighed and dissolved in 6 mL of acetonitrile. The solution was transferred into a 10 mL amber volumetric flask and diluted to volume with acetonitrile to obtain a final concentration of 150 mg/mL.

2.3. Chromatographic Conditions

HPLC analysis was performed on an Agilent 1260 system equipped with a diode array detector (DAD). Separation was carried out using a Kromasil C18 column (4.6 mm × 250 mm, 5 μm). Mobile phase A consisted of 0.030 mol/L sodium acetate buffer (pH 5.25), and mobile phase B was acetonitrile. The detection wavelength was set at 360 nm, column temperature at 30 °C, flow rate at 1.0 mL/min, and injection volume at 10 μL. An optimized gradient elution program was applied to ensure adequate resolution for all 19 analytes and maintain baseline stability, as detailed in

Table 1. Gradient elution program for HPLC analysis.

Time (min)	Flow Rate (mL/min)	Mobile Phase A (%)	Mobile Phase B (%)
0.00	1	88.0	12.0
5.00	1	88.0	12.0
15.00	1	82.0	18.0
20.00	1	70.0	30.0
35.00	1	70.0	30.0
40.00	1	40.0	60.0
45.00	1	40.0	60.0
47.00	1	80.0	20.0
49.00	1	88.0	12.0
50.00	1	88.0	12.0
60.00	1	88.0	12.0

. In actual operation, a 10 min post-run re-equilibration step was performed following each run to ensure column stability. Although the total cycle time is relatively long, this procedure provides a reliable and accessible method for laboratories equipped with HPLC-DAD systems.

2.4. Data Processing and Statistical Analysis

Chromatographic data processing was performed using OriginPro 2021 software (OriginLab Corp., Northampton, MA, USA). All experimental measurements were conducted in triplicate, and the results are expressed as the mean ± standard deviation (SD). Statistical analysis was conducted using IBM SPSS Statistics 27.0 software (IBM Corp., Armonk, NY, USA). The significance of differences among groups was evaluated via one-way analysis of variance (ANOVA) followed by Duncan’s post hoc test, with the significance level set at p < 0.05.

2.5. Sample Collection and Preparation

Selenium-enriched radishes were harvested between September 2024 and January 2025 from the selenium-enriched region of Bajiao Nanhe, Enshi, China. The planting soil at the sampling site had an average background selenium content of 0.76 mg/kg, classifying it as a typical naturally selenium-rich soil. To accurately assess the dynamics of selenium accumulation and amino acid metabolism across different developmental stages, the growth period of the selenium-enriched radishes was strictly divided into five stages based on days after sowing (DAS) and morphological characteristics: seedling stage (~20 DAS), rosette stage (~40 DAS), initial fleshy root stage (~60 DAS), fleshy root expansion stage (~80 DAS), and mature stage (~100 DAS). To account for natural biological variation among individual plants and ensure statistical reliability, five independent, healthy plants with uniform growth were randomly selected at each stage to serve as biological replicates (n = 5). For the latter three stages, the plants were separated into leaves and taproots. After sampling, the specimens were first washed with tap water to remove surface soil, then rinsed three times with ultrapure water. After the surface moisture was blotted with filter paper, the samples were dried in an oven at 65 °C for 12 h to a constant weight. The dried samples were ground using a micro-pulverizer, passed through a 100-mesh sieve, and stored in sealed containers for subsequent analysis.

Conventional strong acid hydrolysis is highly prone to causing the oxidative degradation of unstable selenium-containing amino acids. Therefore, a mild Proteinase K digestion method was employed in this study to maximize the structural preservation of SeMet and SeCys₂. The specific extraction procedure was as follows:

A 0.2500 g portion of the vegetable sample was weighed into a 10 mL centrifuge tube, followed by the addition of 5 mL of purified water and 20 mg of Proteinase K. After vortexing, the mixture was subjected to ultrasound-assisted enzymatic extraction in a thermostatic water bath at 40 °C for 60 min. The mixture was then centrifuged at 8000 r/min for 20 min. The supernatant was collected, filtered through a 0.45 μm membrane, and concentrated to a volume of 500 μL. For the derivatization step, a 100 μL aliquot of either the mixed standard solution or the sample extract was transferred into a 5 mL centrifuge tube. To provide an alkaline environment, 200 μL of a sodium carbonate-sodium bicarbonate buffer solution (0.50 mol/L, pH 9.0) was added, followed by 100 μL of 2,4-dinitrochlorobenzene (DNCB, 150 mg/mL) serving as the pre-column derivatization reagent. The mixture was incubated in a thermostatic water bath at 80 °C for 90 min in the dark. Upon completion of the reaction, 50 μL of 10% (v/v) acetic acid was added. The solution was diluted to a final volume of 1 mL with purified water and vortexed thoroughly. Finally, the mixture was filtered again through a 0.45 μm membrane and transferred into a chromatography autosampler vial for subsequent analysis.

3. Results and Discussion

3.1. Selection of Chromatographic Conditions

3.1.1. Determination of the Mobile Phase

Sodium phosphate-acetonitrile [29] and acetate-acetonitrile [30] systems are frequently employed as mobile phases for the HPLC analysis of amino acids in previous literature. This study compared the separation performance of these two buffer systems. The results indicated comparable resolution between them. However, since the elution of amino acids requires a high proportion of the organic phase (acetonitrile), phosphate buffers are prone to salting-out precipitation in high-concentration organic solvents. This phenomenon can lead to increased system pressure and irreversible damage to the column and pump. In contrast, the sodium acetate buffer not only maintains adequate buffering capacity at the target pH but also exhibits better solubility in acetonitrile, resulting in a more stable system. Therefore, based on considerations of system compatibility and column longevity, the sodium acetate buffer-acetonitrile system was selected.

3.1.2. Determination of Flow Rate and Column Temperature

Column temperature directly influences the viscosity of the mobile phase and the thermodynamic partition coefficients of solutes between the stationary and mobile phases. Separation performance was evaluated at 20, 25, and 30 °C using a standard mixture where the concentration of each amino acid was 25 mg/L. At 20 °C, structurally similar isoleucine and leucine could not be effectively separated due to a slower mass transfer rate. When the temperature was raised to 25 °C, co-elution was still observed for valine, methionine, and selenocystine. At 30 °C, the decreased mobile phase viscosity and enhanced mass transfer efficiency enabled all critical pairs of the 19 amino acids to achieve baseline separation (resolution factor, R_s > 1.5) with symmetric peak shapes (Figure 1a).

Furthermore, according to the Van Deemter equation, flow rate directly impacts column efficiency and mass transfer behavior. Three flow rates (0.8, 1.0, and 1.2 mL/min) were compared (Figure 1b). At 0.8 mL/min, longitudinal diffusion caused peak broadening, resulting in the co-elution of proline/alanine and isoleucine/leucine. When the flow rate was increased to 1.2 mL/min, the elevated mass transfer resistance within the stationary phase compromised the separation of isoleucine/leucine. Additionally, the faster flow rate reduced the residence time of analytes in the UV detector cell, leading to decreased peak heights and responses for late-eluting amino acids. At 1.0 mL/min, the system effectively balanced the effects of diffusion and mass transfer resistance, achieving baseline separation for all critical pairs (R_s > 1.5) while maintaining sharp peak shapes and adequate sensitivity for late-eluting components. Therefore, 1.0 mL/min was selected as the optimal flow rate.

3.1.3. Determination of Detection Wavelength

Following the pre-column derivatization, DNCB-amino acid derivatives exhibiting strong ultraviolet absorption were generated. A full-wavelength scan ranging from 200 to 400 nm revealed that the maximum absorption wavelengths (λ_max) of these 19 amino acid derivatives were consistently located near 360 nm. Selecting 360 nm not only provided the optimal response intensity but also effectively avoided interferences from the mobile phase and background matrix in the low-wavelength region, thereby improving the signal-to-noise ratio and ensuring the accuracy of quantitative results.

3.2. Selection of Extraction Conditions

Representative composite radish matrix samples (spiked at a level of 30 mg/kg, with three replicates per treatment) were utilized to evaluate the overall recoveries of various amino acids—encompassing both extraction efficiency and subsequent derivatization efficiency—using four commonly employed extraction solvents or reagents (water, 15% (v/v) HCl, Proteinase K, and 10% (v/v) HNO₃). Considering the significant differences in the initial release capacities of endogenous amino acid backgrounds among the extraction agents, both unspiked and spiked matrices were processed in parallel for each extraction condition, and the final recoveries were calculated by strictly subtracting the corresponding endogenous background values. The results for the different extraction methods are expressed as the “mean ± standard deviation (SD)” and are detailed in

Table 2. Overall recoveries of various amino acids with different extraction solvents (mean ± SD, n = 3).

Analyte	Overall Recovery (%)
	Water	15% (v/v) HCl	Proteinase K	10% (v/v) HNO3
Asp	89.7 ± 1.4 b	88.4 ± 1.5 b	94.4 ± 1.3 a	90.3 ± 1.3 b
Glu	90.6 ± 1.1 c	92.9 ± 1.0 b	95.8 ± 0.9 a	88.8 ± 1.2 c
His	87.8 ± 0.7 b	85.9 ± 0.9 c	90.4 ± 0.7 a	88.5 ± 0.6 b
Ser	91.3 ± 0.3 d	92.6 ± 0.4 c	95.5 ± 0.4 a	94.2 ± 0.3 b
Arg	90.8 ± 1.0 b	90.7 ± 1.1 b	93.6 ± 1.0 a	89.9 ± 0.9 b
Gly	87.3 ± 1.3 c	90.8 ± 1.1 b	94.8 ± 1.1 a	91.9 ± 1.1 b
Thr	90.5 ± 0.8 b	87.9 ± 0.6 c	92.4 ± 0.7 a	86.4 ± 0.7 d
Pro	86.4 ± 1.4 b	87.6 ± 1.3 b	91.8 ± 1.3 a	88.3 ± 1.1 b
Ala	87.9 ± 1.5 b	87.5 ± 1.4 b	93.3 ± 1.4 a	89.4 ± 1.6 b
Cys2	89.9 ± 1.0 c	92.3 ± 0.9 b	95.2 ± 1.0 a	90.1 ± 0.8 c
Val	88.8 ± 0.6 c	89.5 ± 0.8 c	92.3 ± 0.4 a	90.6 ± 0.4 b
Met	90.3 ± 1.1 b	91.7 ± 0.9 b	94.3 ± 0.9 a	85.9 ± 1.6 c
Ile	90.6 ± 0.7 b	89.4 ± 0.8 b	93.6 ± 0.6 a	93.2 ± 0.5 a
Leu	87.4 ± 1.1 c	88.7 ± 0.9 bc	92.0 ± 0.9 a	89.5 ± 0.9 b
Phe	90.4 ± 0.7 b	92.4 ± 0.7 a	93.2 ± 0.4 a	89.5 ± 0.6 b
Lys	90.3 ± 0.8 c	90.5 ± 0.7 c	95.8 ± 0.8 a	93.5 ± 0.9 b
Tyr	90.6 ± 1.0 c	93.6 ± 0.8 b	95.7 ± 0.7 a	93.8 ± 0.6 b
SeCys2	86.3 ± 1.6 c	89.8 ± 1.9 b	95.4 ± 1.8 a	85.6 ± 1.5 c
SeMet	88.6 ± 1.5 b	90.3 ± 1.4 b	94.3 ± 1.3 a	88.5 ± 1.5 b

Different lowercase letters in the same row indicate significant differences (p < 0.05).

. It should be noted that the values presented herein represent the overall recovery of the analytical process, as different extraction agents affect not only the initial release of analytes from the matrix but also the efficiency of the subsequent derivatization reaction.

As shown in Table 2, the extraction using Proteinase K yielded the highest overall recoveries for all tested amino acids, demonstrating good reproducibility with relative standard deviations (RSDs) of less than 4%. Statistical analysis (one-way analysis of variance, ANOVA) indicated that, although there was no significant difference between Proteinase K and the second-optimal solvent for Ile and Phe, Proteinase K exhibited a clear statistical advantage (p < 0.05) for the vast majority of amino acids, particularly the sensitive selenium-containing amino acids. This highlights a key methodological advantage of the established protocol. Conventional amino acid extraction frequently employs strong acid hydrolysis, which can lead to severe degradation or oxidation of unstable target analytes, especially the selenium-containing amino acids selenomethionine (SeMet) and selenocystine (SeCys₂). In contrast, the application of Proteinase K provides a relatively mild enzymatic digestion environment that effectively protects these sensitive compounds, thereby ensuring the reliability of the quantitative analysis. Therefore, Proteinase K was selected as the optimal extraction agent for subsequent analyses.

3.3. Selection of Derivative Conditions

This study investigated the effects of different derivatization temperatures (50, 60, 70, 80, 90, and 100 °C) and derivatization times (60, 70, 80, 90, 100, and 110 min) on the peak areas of amino acid derivatives, with the results shown in Figure 2. Regarding the derivatization temperature, as the temperature gradually increased from 50 °C to 100 °C, the peak areas of 17 amino acid derivatives showed a continuous increasing trend, except for SeMet and His; Leu was selected as the representative of these 17 components in Figure 2a. Conversely, when the derivatization temperature exceeded 80 °C, the peak areas of SeMet and His both increased continuously. Regarding the derivatization time, the peak areas of all amino acid derivatives showed an increasing trend as the time extended. When the derivatization time was no less than 90 min, the peak areas of the amino acid derivatives stabilized, with no statistical difference among the 90, 100, and 110 min groups. Figure 2b illustrates the variation in the total peak area of the 19 amino acids over time. Taking into account both the protection of heat-labile components and the efficiency of the derivatization reaction, the optimal derivatization temperature and time selected for the experiment were 80 °C and 90 min, respectively.

3.4. Selectivity and Chromatographic Separation

The composition and content of amino acids were determined using the optimized method. To evaluate the selectivity and specificity of the method, a reagent blank (ultrapure water subjected to the same derivatization procedure), a mixed standard solution, and actual selenium-enriched radish samples were analyzed. The chromatograms (Figure 3) demonstrated that baseline separation was achieved for the 19 target amino acids. A comparison of the chromatograms revealed that, although a massive absorption peak corresponding to the derivatization reagent (or its by-products) appeared between 16 and 18 min, this peak did not co-elute with any of the target amino acids. Importantly, no interfering peaks from the derivatization reagent or sample matrix were observed at the specific retention times of all target analytes. Furthermore, because the high-performance liquid chromatography (HPLC) system was equipped with a diode array detector (DAD), a peak purity check was performed for all target analytes in the selenium-enriched radish samples. The spectral homogeneity at the peak start, apex, and end confirmed that each chromatographic peak represented a single component, indicating the absence of co-eluting impurities from the sample matrix. The elution order and retention times of the amino acids in the actual samples were consistent with those in the mixed standard solution. Therefore, this method is well-suited for the qualitative and quantitative analysis of these 19 amino acids in real samples.

3.5. Robustness and System Suitability

According to standard guidelines for chromatographic method validation, the robustness of the established method was evaluated by making small, deliberate variations to the optimized method parameters. The varied parameters included column temperature (28, 30, and 32 °C), derivatization time (88, 90, and 92 min), mobile phase pH (5.25 ± 0.2), and mobile phase composition (acetonitrile proportion altered by ± 2%). Under these varied conditions, the standard solution was analyzed in triplicate, and the relative standard deviations (RSDs) of the retention times and peak areas for the amino acid derivatives were calculated. The results showed that the RSDs for retention times and peak areas were below 2.5% and 5.0%, respectively. Furthermore, the minimum resolution between adjacent peaks remained above 1.5. These findings indicate that the proposed chromatographic method meets the requirements for routine analysis and possesses acceptable robustness and reliability, even under slight fluctuations in operating conditions.

In addition, to ensure the overall reliability of the analytical procedure, critical system suitability parameters were evaluated. Carry-over was assessed by injecting a blank solvent immediately after the highest concentration standard solution. In the blank chromatogram, apart from the expected derivatization reagent peak, no quantifiable peaks were observed at the specific retention times of the target analytes, confirming the absence of target carry-over effects. Under the optimized conditions, the minimum resolution (R_s) between adjacent amino acid derivative peaks was 1.6, achieving baseline separation. The tailing factors (T_f) for all analytes ranged from 0.92 to 1.18, indicating good peak symmetry that allows for accurate integration.

3.6. Linearity, Limits of Detection, and Limits of Quantification

Method validation of the established analytical procedure was performed in accordance with the ICH Q2(R2) guidelines. Under the optimized chromatographic conditions, standard solutions of 19 amino acids at known concentrations were analyzed. Calibration curves were constructed by plotting the peak area (y) against the corresponding amino acid concentration (x), from which the linear regression equations for all 19 amino acids were obtained. Six concentration levels were used for the calibration curves, with each calibration point analyzed in triplicate (n = 3). The relative standard deviations (RSDs) of the peak areas were all below 5.0%, indicating good instrumental stability. The instrumental limits of detection (LOD) and instrumental limits of quantification (LOQ) were determined based on signal-to-noise (S/N) ratios of 3 and 10, respectively, using standard solutions. Baseline noise was estimated using the peak-to-peak measurement method around the specific retention time window of the target analytes. Furthermore, the calculated LOQ values were experimentally verified by analyzing standard solutions and spiked actual matrix samples at the corresponding LOQ levels. The results demonstrated acceptable precision (RSD < 15%) and accuracy (85–112%). As summarized in

Table 3. Analytical performance parameters for the quantification of 19 amino acids. (n = 3).

Analyte	Regression Equation	Slope 95% CI a	R2	Instrumental LOD (mg/L)	Instrumental LOQ (mg/L)
Asp	y = 18,532x + 1289.9	446	0.9997	0.09	0.30
Glu	y = 13,129x − 8135.8	482	0.9993	0.12	0.39
His	y = 8778.7x + 137.98	366	0.9991	0.13	0.42
Ser	y = 3677x − 420.65	51	0.9999	0.21	0.68
Arg	y = 13,542x − 25,336	1250	0.9956	0.13	0.42
Gly	y = 19,798x − 24,920	912	0.9989	0.17	0.56
Thr	y = 2934.0x − 15,402	187	0.9979	0.17	0.56
Pro	y = 11,801x − 1394.9	568	0.9988	0.20	0.66
Ala	y = 8839.5x + 723.54	562	0.9979	0.20	0.66
Cys2	y = 1422.8x − 1478.8	56	0.9992	0.20	0.66
Val	y = 11,655x + 2511.1	724	0.9980	0.18	0.58
Met	y = 9750.2x − 9622.3	271	0.9996	0.21	0.68
Ile	y = 11,045x − 16,319	869	0.9968	0.13	0.42
Leu	y = 15,444x − 2715.6	371	0.9997	0.13	0.42
Phe	y = 5831.8x − 15,436	81	0.9999	0.20	0.66
Lys	y = 16,410x − 3385.7	394	0.9997	0.20	0.66
Tyr	y = 9524.2x − 15,970	783	0.9965	0.18	0.58
SeCys2	y = 10,623x − 5425.1	809	0.9970	0.06	0.19
SeMet	y = 14,602x − 77,269	1235	0.9963	0.08	0.26

Note: ^a denotes the confidence interval of the slope.

, all 19 amino acids exhibited good linearity, with correlation coefficients (R²) ≥ 0.995. The instrumental LOD and LOQ values ranged from 0.06 to 0.21 mg/L and from 0.19 to 0.68 mg/L, respectively.

3.7. Recovery Rate and Precision

The accuracy of the method was evaluated using a spike-and-recovery approach. Recovery experiments were performed by spiking three different concentration levels into unspiked radish matrix samples, for which the endogenous amino acid background concentrations had been previously quantified. The test solutions were prepared according to the established derivatization procedure and analyzed in six replicates (n = 6). The recovery was calculated using the following equation: Recovery (%) = [(Total detected concentration—Endogenous background concentration)/Spiked concentration] × 100. The calculated recoveries and relative standard deviations (RSDs) for all 19 amino acids are summarized in

Table 4. Accuracy (recovery), precision (intra- and inter-day RSDs), and expanded measurement uncertainty for the determination of 19 amino acids in radish matrix (n = 6).

Analyte	Spiked Conc. (mg/kg)	Recovery (%)	Intra-Day RSD (%)	Inter-Day RSD (%)	Expanded Uncertainty (%, k = 2)
Asp	20.02	101.5	0.81	1.35	3.2
	40.01	96.3	1.23	1.78	5.6
	60.11	100.2	2.01	2.65	5.3
Glu	20.12	99.3	1.22	1.84	3.8
	40.14	97.2	0.88	1.42	4.3
	60.03	91.3	1.46	2.10	10.9
His	20.01	100.1	1.34	1.95	3.9
	40.03	99.4	0.74	1.12	2.3
	60.15	90.3	0.93	1.56	11.6
Ser	20.13	99.8	0.89	1.44	2.9
	40.08	93.1	1.89	2.55	9.5
	60.17	98.3	1.34	1.92	4.3
Arg	20.03	99.2	2.01	2.68	5.4
	40.09	89.2	1.98	2.50	13.4
	60.08	88.5	1.28	1.87	13.8
Gly	20.02	98.1	2.23	2.94	6.3
	40.12	92.3	0.99	1.63	9.5
	60.08	97.5	1.58	2.25	5.3
Thr	20.15	92.22	1.98	2.75	10.5
	40.06	93.3	0.87	1.38	8.2
	60.08	94.5	2.88	3.56	9.5
Pro	20.13	96.5	1.56	2.15	5.9
	40.02	101.3	0.98	1.55	3.4
	60.14	98.0	2.94	3.65	7.6
Ala	20.13	100.5	2.27	2.88	5.8
	40.02	98.8	2.48	3.25	6.6
	60.13	97.4	3.09	4.25	9.0
Cys2	20.01	89.8	1.53	2.12	12.5
	40.15	90.4	2.44	3.10	12.7
	60.10	98.4	2.56	3.35	6.9
Val	20.03	98.6	1.32	1.88	3.9
	40.09	94.3	0.88	1.45	7.2
	60.10	97.4	2.13	2.85	6.4
Met	20.01	97.8	1.68	2.30	5.2
	40.09	98.3	0.67	1.15	3.0
	60.12	98.8	1.49	2.05	4.3
Ile	20.13	96.3	2.05	2.80	7.0
	40.03	90.4	1.98	2.65	12.3
	60.11	98.9	1.40	1.98	4.1
Leu	20.16	93.7	1.93	2.60	8.9
	40.02	91.1	1.23	1.75	10.9
	60.11	93.9	1.50	2.14	8.2
Phe	20.08	92.5	1.87	2.55	10.1
	40.06	91.2	2.56	3.30	12.1
	60.08	95.5	2.80	3.60	8.9
Lys	20.04	101.7	1.65	2.35	5.1
	40.08	101.3	2.22	2.95	6.1
	60.06	98.4	2.19	2.84	5.9
Tyr	20.06	100.5	1.62	2.20	4.4
	40.08	97.4	2.09	2.76	6.3
	60.18	98.6	1.37	1.92	4.2
SeCys2	20.04	88.2	2.01	2.85	14.8
	40.08	96.9	2.06	2.90	6.8
	60.16	89.6	2.01	3.45	13.8
SeMet	20.07	95.3	1.45	3.25	8.5
	40.02	94.3	2.51	2.15	7.9
	60.12	96.1	1.89	2.60	6.9

. The recoveries ranged from 88.2% to 101.7%, with intra-day RSDs ranging from 0.81% to 3.09%. Additionally, the intermediate precision (inter-day precision) was evaluated by analyzing the spiked samples over three consecutive days. The inter-day RSDs ranged from 1.12% to 4.25%. These results demonstrate that the established method provides satisfactory accuracy and precision, making it suitable for the reliable determination of these 19 amino acids in selenium-enriched radish samples.

Furthermore, the measurement uncertainty was evaluated to assess the dispersion of the quantitative results. Based on the method validation data, the relative expanded measurement uncertainty (U, coverage factor k = 2, at a confidence level of approximately 95%) was calculated using a “top-down” approach. Specifically, the uncertainty was estimated according to the precision and accuracy (bias expressed as overall recovery) results presented in Table 4. The evaluated relative expanded uncertainties for the 19 amino acids ranged from 2.3% to 14.8%. These results indicate that the established method provides sufficient reliability and precision for the quantitative analysis of amino acids.

3.8. Sample Stability

The sample stability evaluation was systematically divided into pre-derivatization and post-derivatization phases. To evaluate the pre-derivatization stability, the unspiked raw radish sample extracts were stored at 4 °C in the dark. The peak areas of the amino acids were monitored at 0, 12, and 24 h. The RSDs of the peak areas were all less than 5.0%, indicating that the raw extracts remained stable for at least 24 h under refrigeration prior to derivatization. To evaluate the post-derivatization stability, the derivatized actual sample solutions were stored at room temperature in the dark. Aliquots (10 µL) of the solution were injected into the chromatographic system at 0, 2, 4, 6, 8, 12, 24, 48, and 72 h, and the peak areas of the 19 amino acids were recorded. The results showed that the RSDs of the peak areas for each component were all less than 3.50%, demonstrating that the derivatized sample solution maintained good stability within 3 days.

3.9. Analysis of Samples

3.9.1. Analysis of Amino Acid Composition and Content in the Leaves and Taproots of Selenium-Enriched Radishes

Using the optimized method described above, the amino acid profiles in the leaves and taproots of selenium-enriched radishes were determined. The results are presented in

Table 5. (A) Composition and content of amino acids in selenium-enriched radish leaves at different growth stages. (B) Composition and content of amino acids in selenium-enriched radish taproots at different growth stages.

(A)
Constituent		Concentration (mg/kg, mean ± SD)
		Leaves (S1)	Leaves (S2)	Leaves (S3)	Leaves (S4)	Leaves (S5)
EAA	His	56.58 ± 5.14 d	57.37 ± 2.17 d	132.40 ± 8.49 a	101.11 ± 8.41 b	76.24 ± 6.29 c
	Val	142.28 ± 12.32 bc	119.90 ± 4.27 cd	173.76 ± 20.57 b	113.88 ± 8.08 cd	280.11 ± 45.33 a
	Thr	35.02 ± 3.23 c	30.28 ± 3.56 c	86.94 ± 8.88 a	50.20 ± 12.33 b	76.40 ± 10.44 a
	Met	38.41 ± 4.28 c	29.82 ± 1.89 d	63.80 ± 6.30 a	40.63 ± 2.79 bc	44.44 ± 2.24 b
	Ile	24.13 ± 1.68 b	17.50 ± 2.59 c	27.53 ± 1.34 a	14.13 ± 1.10 de	16.67 ± 1.32 cd
	Leu	51.29 ± 2.39 bc	35.97 ± 3.85 d	78.50 ± 5.24 a	48.50 ± 4.23 c	54.50 ± 2.39 b
	Phe	106.22 ± 5.33 c	71.70 ± 5.34 e	183.66 ± 8.07 a	131.47 ± 9.29 b	92.38 ± 6.88 d
	Lys	68.88 ± 4.84 b	19.66 ± 2.49 e	66.33 ± 3.67 b	81.76 ± 8.09 a	64.57 ± 3.28 bc
NEAA	Gly	24.80 ± 1.78 cd	21.20 ± 2.49 cde	80.25 ± 6.40 a	26.80 ± 1.85 c	42.80 ± 5.45 b
	Glu	149.51 ± 17.01 c	147.88 ± 36.98 c	593.31 ± 48.23 a	514.38 ± 17.23 b	525.22 ± 18.26 b
	Pro	29.80 ± 2.09 cd	32.13 ± 3.19 bc	47.80 ± 2.03 a	44.48 ± 1.73 a	35.60 ± 1.69 b
	Ala	56.16 ± 1.48 b	41.11 ± 6.67 c	65.32 ± 3.08 a	54.14 ± 1.59 b	69.89 ± 3.19 a
	Cys2	19.52 ± 2.24 ab	15.51 ± 1.85 c	17.86 ± 1.49 bc	9.94 ± 0.68 d	20.72 ± 2.57 a
	Asp	423.81 ± 34.88 b	367.35 ± 26.98 c	491.49 ± 49.59 a	325.82 ± 28.53 cd	318.78 ± 16.23 d
	Ser	86.81 ± 5.40 cd	77.23 ± 7.09 d	141.02 ± 21.29 a	94.48 ± 6.59 c	113.26 ± 7.35 b
	Arg	58.19 ± 3.20 cd	64.22 ± 5.54 c	93.47 ± 6.67 a	81.59 ± 8.60 b	55.34 ± 2.19 cde
	Tyr	52.28 ± 3.52 a	39.50 ± 2.09 c	48.20 ± 1.49 ab	43.60 ± 3.46 bc	53.54 ± 4.24 a
SeAA	SeCys2	3.25 ± 0.15 a	2.58 ± 0.24 c	2.98 ± 0.13 b	1.66 ± 0.11 de	1.79 ± 0.10 d
	SeMet	3.27 ± 0.26 b	3.03 ± 0.17 bc	3.80 ± 0.32 a	2.80 ± 0.13 c	2.20 ± 0.14 d
SeAA		6.52 ± 0.41 a	5.61 ± 0.41 b	6.78 ± 0.45 a	4.46 ± 0.24 c	3.99 ± 0.24 c
TAA		1430.21 ± 111.22 c	1193.94 ± 119.42 d	2398.41 ± 203.35 a	1781.36 ± 124.82 b	1944.45 ± 139.58 b
EAA		522.81 ± 39.21 c	382.20 ± 26.16 d	812.92 ± 62.56 a	581.68 ± 54.32 c	705.31 ± 78.17 b
SeAA/TAA (%)		0.46 ± 0.01 c	0.47 ± 0.01 b	0.28 ± 0.01 f	0.25 ± 0.01 g	0.21 ± 0.01 h
EAA/TAA (%)		0.37 ± 0.01 d	0.32 ± 0.01 f	0.34 ± 0.01 e	0.33 ± 0.01 ef	0.36 ± 0.01 d
(B)
Constituent		Concentration (mg/kg, mean ± SD)
		Taproots (S3)		Taproots (S4)	Taproots (S5)
EAA	His	29.43 ± 2.37 f		40.12 ± 3.09 e	28.88 ± 2.65 f
	Val	69.73 ± 2.67 e		99.54 ± 3.28 de	126.45 ± 5.82 cd
	Thr	24.40 ± 2.70 c		25.28 ± 2.13 c	25.80 ± 1.26 c
	Met	14.80 ± 2.30 e		25.36 ± 1.76 d	27.20 ± 1.98 d
	Ile	8.94 ± 1.38 f		13.09 ± 1.30 e	13.98 ± 2.09 de
	Leu	7.23 ± 1.69 f		28.50 ± 1.64 e	38.15 ± 2.32 d
	Phe	34.29 ± 3.02 f		70.58 ± 2.21 e	62.21 ± 4.06 e
	Lys	52.68 ± 4.99 d		56.88 ± 3.48 cd	60.85 ± 2.68 bcd
NEAA	Gly	14.09 ± 1.35 f		16.47 ± 1.78 ef	18.76 ± 1.42 def
	Glu	73.71 ± 7.95 d		61.98 ± 5.08 d	82.39 ± 8.03 d
	Pro	11.62 ± 1.08 e		26.80 ± 1.65 d	33.34 ± 2.75 bc
	Ala	19.92 ± 3.06 e		25.47 ± 1.20 de	26.62 ± 3.12 d
	Cys2	8.03 ± 0.84 d		9.92 ± 0.72 d	7.35 ± 0.54 d
	Asp	65.22 ± 5.57 ef		31.38 ± 3.59 f	108.30 ± 5.89 e
	Ser	31.87 ± 6.88 f		26.78 ± 4.49 f	51.36 ± 4.78 e
	Arg	47.09 ± 3.03 ef		41.22 ± 2.34 f	54.73 ± 5.21 de
	Tyr	48.08 ± 3.43 ab		42.33 ± 2.44 c	48.49 ± 2.58 ab
SeAA	SeCys2	1.46 ± 0.09 ef		1.64 ± 0.10 de	1.28 ± 0.08 f
	SeMet	1.42 ± 0.12 e		1.01 ± 0.08 f	1.60 ± 0.11 e
SeAA		2.88 ± 0.21 d		2.65 ± 0.18 d	2.88 ± 0.19 d
TAA		563.30 ± 54.52 f		644.35 ± 42.34 ef	817.73 ± 57.37 e
EAA		241.50 ± 21.12 e		359.35 ± 18.89 d	383.52 ± 22.86 d
SeAA/TAA (%)		0.51 ± 0.01 a		0.41 ± 0.01 d	0.35 ± 0.01 e
EAA/TAA (%)		0.43 ± 0.01 c		0.56 ± 0.01 a	0.47 ± 0.01 b

Note: Data are presented as mean ± standard deviation (SD) of five biological replicates (n = 5). At the last three growth stages, the leaves and taproots were manually separated. Each biological sample was analyzed with three technical replicates. Importantly, statistical analysis (one-way ANOVA followed by Duncan’s multiple range test) was performed comprehensively across all eight columns (i.e., combining all leaf and taproot stages). Therefore, different superscript letters in the same row indicate significant differences (p < 0.05) across both Table 5A,B.

A,B. Analysis revealed that throughout the growth period, the amino acid contents did not follow a simple accumulative pattern, but rather exhibited complex temporal dynamics. Based on the statistical analysis (Duncan’s multiple range test) presented in Table 5A,B, significant spatial and developmental variations in amino acid contents were observed between the leaves and taproots (p < 0.05). From a physiological metabolism perspective, the leaves serve as the primary organs for photosynthesis and nitrogen metabolism, exhibiting higher metabolic activity than the taproots. Consequently, the amino acid contents in the leaves were consistently and significantly higher than those in the taproots at corresponding growth stages (p < 0.05). Specifically, the total amino acid (TAA) contents in the leaves ranged from 1193.34 to 2398.41 mg/kg, and the essential amino acid (EAA) contents ranged from 382.20 to 812.92 mg/kg.

Throughout the entire growth period, the EAA content in the leaves peaked at the taproot initiation stage (812.92 mg/kg), whereas the corresponding taproots exhibited the lowest EAA content at this exact stage (241.50 mg/kg). This significant contrast reflects the typical “source-sink” metabolic dynamics during a critical developmental transition period in plants. Physiologically, the taproot initiation stage serves as a crucial pivotal point as the plant transitions from vegetative growth to the development of storage organs. During this phase, the leaves, acting as the metabolic “source,” undergo highly active photosynthesis and primary nitrogen metabolism, synthesizing and accumulating large quantities of amino acids to meet the material demands of subsequent development. Conversely, the taproot, functioning as an emerging “sink,” is in the early stages of rapid cell division and tissue construction [41]. Upon arriving at the roots, the amino acids transported from the leaves are rapidly consumed and incorporated into the synthesis of structural proteins and metabolic enzymes. This mechanism thoroughly explains why the taproots at the initiation stage did not exhibit massive accumulation of free EAAs, resulting in the lowest observed values. Among the eight EAAs, Val exhibited the highest average content (140.71 mg/kg), accounting for approximately 28.26% of the total EAA content, whereas Ile had the lowest content (averaging 17.00 mg/kg).

Regarding the accumulation characteristics of selenoamino acids, the SeCys₂ content in selenium-enriched radishes ranged from 1.28 to 3.25 mg/kg, while the SeMet content ranged from 1.01 to 3.80 mg/kg. Overall, as the primary assimilation organs, the leaves consistently exhibited significantly higher absolute contents of selenoamino acids compared to the corresponding taproots throughout the growth period (p < 0.05). Notably, regarding the relative ratio of selenoamino acids to total amino acids (SeAA/TAA), both the leaves and taproots demonstrated a consistent and significant downward trend during the later growth stages (p < 0.05). Specifically, the taproots at the initiation stage (S3) exhibited the highest ratio of 0.51%, which progressively declined to 0.35% by the S5 stage. Similarly, the ratio in the leaves decreased from 0.46% at the S1 stage to 0.21% at the S5 stage. The significant decline in the SeAA/TAA ratio during the later developmental stages indirectly corroborates that as growth progresses, selenoamino acids are gradually converted into bound macromolecules or more stable secondary metabolites. This conversion serves as a crucial autoregulatory mechanism for plants to maintain intracellular selenium metabolic homeostasis [42]. From the perspective of human nutrition and functional food development, SeCys₂ and SeMet are highly bioactive dietary sources of organic selenium. Their stable detection across various radish organs reflects the metabolic capacity of selenium-enriched radishes to biotransform exogenous selenium into organic forms. This distribution pattern of organic selenium within the plant not only underscores the nutritional value of selenium-enriched radishes as a natural dietary selenium supplement, but also provides fundamental data support for subsequently evaluating the quality of selenium-enriched agricultural products and scientifically guiding the cultivation of selenium-enriched radishes.

3.9.2. Analysis of Taste-Active Amino Acid Contents in the Leaves and Taproots of Selenium-Enriched Radishes

Taste-active amino acids fundamentally influence the overall flavor profile and sensory perception of food products. Based on their distinctive taste characteristics, they can be classified into four primary categories: umami amino acids (UAAs), sweet amino acids (SAAs), bitter amino acids (BAAs), and aromatic amino acids (AAAs). Specifically, UAAs include Glu and Asp; SAAs encompass Ser, Gly, Thr, Pro, and Ala; BAAs consist of His, Arg, Val, Met, Leu, Ile, Phe, and Lys; and AAAs comprise Tyr, Cys₂, and Phe. As crucial taste-active compounds, UAAs not only significantly enhance the fullness of the overall food flavor but also serve as an essential material basis for determining the umami and savory characteristics of the products [43]. The analysis revealed that the overall UAA content in selenium-enriched radishes accounted for approximately 39.98% of the total amino acid (TAA) content, representing the primary component defining its taste profile. This amino acid composition pattern, dominated by UAAs, is consistent with previous findings on the taste-active amino acid profiles of flowering Chinese cabbage (Brassica rapa var. parachinensis) [44]. The prominent umami taste effectively masks potential bitterness and astringency, establishing a rich and mellow chemical foundation for the plant. Consequently, this objectively confirms that the plant possesses an excellent savory flavor and high edible value. Spatially and temporally, the UAA content in the leaves at the taproot initiation stage reached a remarkable peak of 1084.00 mg/kg, followed by the leaves at the mature taproot stage (844.01 mg/kg). Regarding the sweet amino acids, the overall SAA contents ranged from 101.90 to 421.33 mg/kg. The highest SAA concentration was also observed in the leaves at the taproot initiation stage (421.33 mg/kg), followed by the mature taproot stage leaves (337.95 mg/kg), while the lowest level was recorded in the taproots at the initiation stage (101.90 mg/kg). Furthermore, the overall contents of BAAs and AAAs ranged from 264.19 to 819.45 mg/kg and 89.70 to 249.72 mg/kg, respectively. As illustrated in Figure 4, it is noteworthy that all four categories of taste-active amino acids synchronously reached their maximum accumulation levels in the leaves during the taproot initiation stage.

According to the statistical analysis (Duncan’s multiple range test), different lowercase letters for the same class of taste-active amino acids indicate significant differences (p < 0.05), as illustrated in Figure 4. Overall, throughout all growth stages, the absolute contents of taste-active amino acids in the leaves were consistently and significantly higher than those in the taproots. Particularly, the leaves at the taproot initiation stage exhibited the strongest accumulation capacity for taste-active amino acids. Remarkably, the UAA content in the leaves at this specific stage was approximately 7.8 times that of the corresponding taproots. These findings indicate that the leaves at the taproot initiation stage are crucial sites for the biosynthesis and accumulation of flavor compounds in the radish leaves highlights their potential as a highly palatable and nutritious dietary resource. Further analysis revealed distinct differences in the composition of taste-active amino acids between the leaves and taproots. The average contents of taste-active amino acids in selenium-enriched radish leaves followed the descending order of UAAs > BAAs > SAAs > AAAs, whereas the order in the taproots was BAAs > UAAs > SAAs > AAAs. This inter-organ compositional discrepancy essentially reflects the functional division of primary nitrogen metabolism and secondary metabolism within the plant. On the one hand, acting as the core “source” organ for nitrogen assimilation, the leaves preferentially synthesize and maintain high levels of primary metabolites, such as Glu and Asp, thereby exhibiting a UAA-dominated profile. On the other hand, the relatively high proportion of BAAs in the taproots is closely related to the unique secondary metabolic pathways of cruciferous plants. The BAA category is rich in branched-chain amino acids (e.g., Val, Leu, and Ile) and sulfur-containing amino acids (e.g., Met), which are essential metabolic precursors for synthesizing glucosinolates—the characteristic flavor compounds of radishes [45]. A study by Yuan et al. [46] also confirmed that the accumulation of total glucosinolates in radish taproots was significantly higher than that in the leaves. Based on the present data, it can be deduced that taproots, functioning as the primary storage “sink” for these sulfur-containing secondary metabolites, naturally require a higher proportion of BAAs as precursor materials. This provides a sound physiological explanation for the significant differences in the taste-active amino acid composition between the taproots and leaves.

4. Conclusions

This study investigated parameters such as the mobile phase system, extraction solvent, and derivatization conditions to develop a pre-column derivatization-high performance liquid chromatography (HPLC) method for the simultaneous determination of 19 amino acids (including Asp, Glu, SeMet, and SeCys₂) in different organs of selenium-enriched radishes. The method was validated and demonstrated good linearity (R² ≥ 0.995) for all 19 amino acids. The instrumental LOD and LOQ values ranged from 0.06 to 0.21 mg/L and from 0.19 to 0.68 mg/L, respectively. Furthermore, the method exhibited high accuracy and precision, with spike recoveries between 88.2% and 101.7%, while the intra-day and inter-day relative standard deviations (RSDs) were ≤3.09% and ≤4.25%, respectively. The optimization of pretreatment methods and HPLC separation conditions achieved effective peak resolution between standard and selenoamino acids, addressing a common gap in traditional methods where selenoamino acids are frequently excluded during multi-amino acid analysis. Practical application results demonstrated that the contents of essential amino acids, selenoamino acids, and taste-active amino acids in the leaves were significantly higher than those in the taproots, particularly at the taproot initiation stage. From a nutritional physiology standpoint, these findings underscore the development potential of radish leaves as a high-quality dietary organic selenium source and provide a theoretical basis for the quality evaluation of selenium-enriched agricultural products. Nevertheless, while the proposed method exhibits reliable suitability for routine quality assessment, it possesses certain limitations. Compared with advanced LC-MS/MS, this method relies on retention time for identification, which shows a slight deficiency in structural confirmation capability and absolute sensitivity for trace-level complex components. Additionally, the pre-column derivatization procedure introduces intrinsic variables, such as minor fluctuations in reagent stability and reaction efficiency, which contribute to random and systematic errors and potentially expand the overall measurement uncertainty. However, considering the high cost and complex maintenance of LC-MS/MS equipment, the HPLC method developed in this study demonstrates core advantages, including high instrument accessibility, simple operation, and low analytical costs, while ensuring satisfactory accuracy. Consequently, its application will contribute to enhancing the level of quality control and safety assessment for selenium-enriched products.