Introduction

Plant leaves serve as the primary site for photosynthesis and the foundation for a multitude of food products1. Plant leaves are the foundation of numerous economically significant crops. For instance, tea leaves are valued for their flavor and aroma2,3,4, while tobacco leaves are crucial to the tobacco market5. Leaf quality is critical, as it directly affects the potency and efficacy of derived products. China, as a leading agricultural producer, cultivates a vast array of economic plants consumed domestically and internationally6. The quality directly affects the nutritional value, flavor, and overall appeal of the end products. As consumer demand shifts towards high-quality plant products, there is a growing imperative to enhance leaf quality across all agricultural sectors. Efforts on adopting innovative technologies and precision farming to optimize plant growth and quality, ensuring sustainable production.

The biochemical compositions of plant leaves, such as starch, are critical metrics for assessing their quality and maturity, underscoring the importance of rapid, accurate assessment methods in the grading and marketing of agricultural produce. For example, tobacco leaf starch is an important indicator for measuring the quality and maturity of tobacco leaves7,8. Rapid and accurate measurement of tobacco starch is of practical significance for the quality grading of tobacco.

Several standard methods are available for detecting starch in real samples, each with its unique advantages and applications. The iodine test is one of the most widely used methods due to its simplicity and reliability9,10. This method relies on the color change that occurs when iodine interacts with starch. However, this method is not highly specific for starch, as other glucosyl residues, such as maltodextrins, can also produce false-positive results9. Another approach involves the use of vibrational spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy, combined with chemometrics for rapid and non-destructive identification and classification of starch types11. In addition, chromatographic methods, including high-performance size-exclusion chromatography (HPSEC) and high-performance anion-exchange chromatography (HPAEC), are employed to characterize the fine molecular structures of starch12,13. These methods provide detailed information on the molecular weight distribution and branching patterns of starch, although they disadvantage in that they either requires specific instruments or complex and time-consuming.

In the existing colorimetric methods, the phenol-sulfuric acid method14 and the anthrone-sulfuric acid method15,16 are two well-established methods for determining carbohydrates. Due that phenol is a highly toxic substance that can cause severe chronic health effects, including cancer, neurological damage, and reproductive issues, it is excluded from further evaluations, following the concept toward green chemisty. The anthrone-sulfuric acid method is a highly sensitive technique for detecting low concentrations of carbohydrates. In this method, starch in plant materials undergoes a dehydration reaction in the presence of concentrated sulfuric acid, forming furan compounds. These furan compounds then react with anthrone to produce a blue-green colored complex that can be detected at 620 nm17,18. In terms of pretreatment, considering the large amount of pigments such as chlorophyll contained in tobacco leaves, and since the color of chlorophyll is close to the color of the sulfuric acid-anthrone reaction system, measures were taken to prevent interference with the colorimetric accuracy. This study uses ethanol solvent to extract lutein, chlorophyll, and carotenoids from tobacco leaves19. An appropriate amount of sodium chloride to the ethanol, and ultrasonication was employed to enhance the removal effect of chlorophyll a and chlorophyll b20. After the removal of pigments, perchloric acid is used to extract starch from tobacco leaves21.

Simultaneously, traditional colorimetric methods rely on specialized laboratory instruments for analysis and require skilled personnel for operation and maintenance. This can lead to cumbersome workloads when processing a large number of samples, especially when using cuvettes and spectrophotometers to measure absorbance. With the development of electronic information technology, the popularity and performance of smartphones have greatly improved, and their camera capabilities and accuracy have reached a level comparable to professional-grade cameras. Colorimetry based on smartphone cameras can be considered a low-cost and reliable portable solution22. With the rapid advancement of smartphone technology, visual detection based on smartphone cameras has emerged as a powerful and versatile tool in chemical analysis. For instance, Kayani et al. implemented visual detection of Fe³⁺ and ascorbic acid using smartphone-based colorimetry, with the aid of a red luminescent europium-based metal-organic framework (Eu-MOF)23. The Eu-MOF exhibited intense and stable red emission fluorescence when excited at 270 nm. Such effect was significantly quenched upon exposure to folic acid. Based on this principle, they successfully determined the concentrations of Fe³⁺ in water samples and ascorbic acid (AA) in orange juice samples. The relative standard deviations (RSD) for both were less than 3%, achieving satisfactory detection results. In another work, a dual-mode detection sensor using nitrogen-doped carbon dots (N-CDs) synthesized via a one-step hydrothermal method from 6,9-diamino-2-ethoxyacridine lactate24. The N-CDs exhibited fluorescence quenching in response to Fe³⁺ (5–50 µM, LOD = 290 nM) and fluorescence recovery upon addition of AA to the N-CDs-Fe³⁺ system (linear range 40–90 µM, LOD = 0.69 µM). The sensor’s feasibility was demonstrated using a smartphone with an RGB Color Picker for visual detection of Fe³⁺ and AA. Moreira et al. used the Bradford method, a colorimetric detection method, to detect protein concentration in samples and compared the detection accuracy of smartphone cameras with that of plate readers, showing that the accuracy of smartphone cameras is fully comparable to plate readers25. By optimizing rapid extraction steps and integrating smartphone cameras with color intensity extraction procedures, operational complexity can be significantly reduced, and detection efficiency can be increased.

In our previous study, we proposed a LIGHt smartphone colorimetry framework using smartphone colorimetry and validated its application in the detection of proteins in plant leaves26. The central goal of LIGHt, as demonstrated in Fig. 1, is to improve the efficiency and throughput of tobacco leaf quality monitoring. The specific advantages of LIGHt smartphone colorimetry are in several aspects: First, “L” stands for low-cost, which refers to that this method is significantly cheaper compared to traditional expensive spectral instruments; Second, “I” stands for immediate, which refers to the stable and effective colorimetric method introduced, overcoming the cumbersome operation of traditional analytical instrumental methods. Meanwhile, it reduces the requirements for the environment, enabling on-site detection in the field; Third, “G” stands for general, which refers to the adaptability of smartphone colorimetry to the detection of various substance concentrations based on the colorimetric principle. Lastly, “Ht” refers to high-throughput, which highlights the ability of smartphone colorimetry to collect sample information in batches and perform large-scale repetitive experiments.

Fig. 1
figure 1

Device components and characteristics of LIGHt smartphone colorimetry.

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This study aims to achieve low-cost, immediate, general, and high-throughput (LIGHt) total starch determination of plant leaves using smartphone colorimetry. The study evaluates the reproducibility, detection limit, and recovery rate of the LIGHt smartphone colorimetry and demonstrates the application of LIGHt on real tobacco leaf samples, comparing the advantages and disadvantages of traditional plate reader absorbance detection with the developed method. The results showed that the LIGHt smartphone colorimetry is expected to effectively improve the efficiency of real-time monitoring of tobacco leaf quality. The proposed method greatly reduces detection costs and operational complexity. The LIGHt total starch determination can be extended to the detection of starch content in fruits, vegetables, and other foods.

Materials and methods

Materials and reagents

Ten tobacco leaf samples were collected from the Shanghai Tobacco Company (Shanghai, China). Upon collection, the samples were freeze-dried and grounded into a powdered state, and then stored at -20 °C. All water in this study was purified by a Milli-Q 10 ultrapure water purification system (Merck Group, Darmstadt, Germany). Soluble starch standards were purchased from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China), analytical-grade anhydrous ethanol, analytical-grade 98% concentrated sulfuric acid, and analytical-grade 72% perchloric acid were all purchased from the China National Pharmaceutical Group Co., Ltd. (Beijing, China), sodium chloride was purchased from Lingfeng Chemical Reagent Co., Ltd. (Shanghai, China), and anthrone was purchased from Maclin Biochemical Technology Co., Ltd. (Shanghai, China).

The anthrone-sulfuric acid staining solution is prepared by adding 2.000 g anthrone powder to 88% sulfuric acid solution. To prepare the 80% ethanol-saturated sodium chloride solution, 16 g sodium chloride powder is weighed into a 300 mL beaker. Then 50 mL of distilled water is added, and the powder is dissolved with stirring. Then, 200 mL anhydrous ethanol is added, causing the solution in the beaker to become turbid. After standing for 5 min, some of the sodium chloride powder will precipitate at the bottom of the beaker, and the upper solution cleared. The solution is then filtered to obtain the 80% ethanol-saturated sodium chloride solution.

Preparation of starch standard solution

For each batch of tests, fresh starch standard solutions and tobacco leaf sample extracts were prepared. To prepare the starch standard solution, a 2 mg/mL stock solution was first prepared. This standard stock solution is prepared as follows: Weigh 200 mg of soluble starch standard into a 100 mL beaker, add 5 mL of distilled water, then add 65 mL of 52% perchloric acid, followed by 30 mL of distilled water. Stir with a glass rod to completely dissolve the starch to obtain a stock solution of 2 mg/mL. Then, dilute 1 mL of the stock solution with water to finally obtain a starch standard solution of 80 µg/mL. Afterward, the standard stock solution is diluted with water to obtain an 80 µg/mL starch standard solution. Subsequently, a series of fresh standard solutions ranging from 0 µg/mL to 80 µg/mL at a 20 µg/mL interval are prepared for each experiment.

Sample pretreatment

Unlike the preparation procedure of starch standard solutions, the starch from the samples were extracted first. To extract the starch from the sample into the solution, a 0.2 g of sample powder is weighed into a 50 mL centrifuge tube first, followed by adding 10 mL of 80% ethanol-saturated sodium chloride solution. Then the entire solutions were sonicated at room temperature for 5 min. The extraction liquid is then discarded, and the residue is washed with 2 mL of 80% ethanol-saturated sodium chloride solution, with the wash liquid also being discarded. Next, 5 mL of distilled water and 25 mL of 52% perchloric acid solution are added, and the mixture is centrifuged at 4000 r/min for 5 min to obtain the starch extraction solution, which is then ready for further anthrone-sulfuric acid colorimetric reaction.

Anthrone-sulfuric acid colorimetric reaction

An aliquot of 50 µL of the starch extraction solution is taken and transferred into a 10 mL centrifuge tube using a pipette. Then, the starch extracts were diluted with 1950 µL of distilled water, followed by the addition of 6 mL of anthrone-sulfuric acid solution all at once using a pipette. After mixing, the solution is placed in a hot water bath for 5 min. Once it has cooled down to room temperature, 200 µL of the solution is transferred into a Nunc MicroWell transparent 96-well plate (Thermo Fisher Scientific, Waltham, Massachusetts, USA) for the subsequent LIGHt smartphone colorimetric analysis.

Principle of LIGHt smartphone colorimetric analysis

Unlike traditional ultraviolet-visible spectroscopy, smartphones colorimetric analysis relies on computer imaging. Therefore, it is important to first understand how computer images represent color. The use of smartphones to assist in rapid, portable, and accurate colorimetry is a key feature of the rapid detection technology in this study. Most images stored on smart electronic devices such as computers or smartphones are stored in the format of raster images. Any point, which is usually referred to as pixels, in the image is represented by the RGB values, i.e., intensity of the red (R), green (G), and blue (B) primary colors in the pixels represented by integers ranging from 0 to 255. An intensity of 0 indicates no contribution to the color channel, while 255 indicates the maximum intensity of that channel. By changing the degree of mixing of these three color channels, various colors can be created. With the sacrifice of direct and accurate tracking of specific wavelength compared to traditional colorimetry, the advantage of smartphone colorimetry is the ability to select the best-performing color channel (red, green, or blue) as a reference for creating standard curves, or to derive a formula based on RGB values representing colorimetric values. After fitting, the starch concentration of unknown samples can be estimated.

The comparison between RGB color values obtained from smartphone cameras and spectroscopic measurements, illustrated in Fig. 2, is based on the principle that both methods can be used to analyze the color of a sample. RGB values can be comparable to spectroscopic data, despite the differences in the methods: The smartphone sensor captures light in three specific bands corresponding to red, green, and blue, which are integrated values over a range of wavelengths. Similarly, a spectroscope measures light intensity across a continuous range of wavelengths, providing a detailed spectrum. In colorimetry, standard curves are created by measuring known concentrations of a substance and plotting the color intensity (often absorbance) against these concentrations. With smartphone colorimetry, one can select the color channel that best correlates with the concentration of the analyte. This channel can then be used as a reference, similar to how a specific wavelength might be chosen in spectroscopy for the same purpose. The RGB values can be compared to spectroscopic data by creating a mathematical model that relates the RGB values to the concentrations of the substances being measured, much like in spectroscopy, but with limited choice of wavelengths. Spectroscopy provides a detailed spectrum, which allows for the identification and quantification of substances based on their unique absorption or emission profiles. In contrast, smartphone colorimetry provides a more general overview of color. It may be sufficient for certain applications but lacks the detailed spectral information. In summary, while smartphone colorimetry does not provide the same level of detailed spectral information as traditional spectroscopy, it can be a comparable method for certain applications, particularly when used with appropriate calibration and mathematical models. The ability to select the best-performing color channel or to derive formulas based on RGB values allows for the flexible creation of standard curves, making it a valuable tool where spectroscopy may not be feasible or cost-effective.

Fig. 2
figure 2

Demonstration of spectroscopic differences between the LIGHt smartphone colorimetry procedure and conventional spectrophotometry. The approximate range of wavelengths of the blue, green, and red color were shown.

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The anthrone-sulfuric acid method’s reaction produced a blue-green complex. As the wavelength of 620 nm corresponds closely to the absorption of red light, the red color (R value) was the most sensitive among RGB color. A good fitting result was obtained, indicating that the intensity of the red color is linearly related to the starch content. Thus, the anthrone-sulfuric acid method successfully quantifies starch levels in tobacco leaves by correlating them with the intensity of R value in the solution.

Procedure of LIGHt smartphone colorimetric analysis

After sample pretreatment and colorimetric reaction, the LIGHt smartphone colorimetric analysis is carried out. An RGB value extraction device was applied to photograph the 96-well plate containing standard and sample solutions. Next, the image is transferred to a computer, where image color analysis software to extract the red, green, and blue (RGB) data (i.e., the intensity of the three colors) within a specified area were performed, as demonstrated in Fig. 3. Meanwhile, this process is compared to the results obtained from this method with a microplate reader (Fig. 4), thereby validating the effectiveness of the smartphone colorimetry technique for quantitative analysis.

Fig. 3
figure 3

Software interface for colorimetry. Program Launcher (Left Panel): menu with options to run different tools. Image Window (Center Panel): displays an image with colorimeter plate of different solutions or samples, in addition of area of interest, shown as light green circles, to measure the color. RGB Values Window (Right Panel): the RGB values of the selected region in the image.

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Fig.  4
figure 4

Flow diagram of the LIGHt smartphone colorimetry procedure.

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To standardize the photography process, an RGB value extraction device was designed (Fig. 1). This device consists of a light source, a microplate, and a camera, and it is highly versatile. The camera can be replaced by a smartphone camera, and the microplate can be replaced by a quartz or glass cuvette. Additionally, the height and angle of the camera can also be freely adjusted as needed.

In the actual experiment, to exclude the potential impact of different microplate readers on the detection of RGB values, multiple sets of standard solutions with the same gradient concentrations were prepared to ensure each microplate has at least one set of standard solutions. Meanwhile, the standard solutions were analyzed in the exact same lighting condition as the samples being tested. To further avoid randomness, each solution is added to three wells on the same microplate to achieve triplicate measurements. The light source used in the experiment is an iPad tablet (Model A2152, Apple Inc., California, USA) with a pure white image displayed and the brightness adjusted to the highest level. The microplate is placed on the screen and photographed straight down from above using a smartphone.

Software for LIGHt smartphone colorimetric analysis

We developed an in-house RGB value extraction program to extract the RGB data. This program is written in Python (version 3.9, Python Software Foundation) and runs on a computer with a Windows 11 operating system (Microsoft, Redmond, Washington, USA). The core functionality of this program uses the Open-Source Computer Vision Library (OpenCV, version 4.7.0.68) for the image processing functions. Additionally, the Tkinter library included in the original Python distribution that provides graphical user interface (GUI) were used to implement the functionality of interactively selecting the sample area that needs to be extracted.

The software is user-friendly: the user can select a specific circular area by pressing and holding the left mouse button. After confirmation, the average RGB values of that area can be extracted. The precision of the color intensity is retained to four decimal places. Subsequently, the required light values are matched with the concentrations of the gradient standard solutions to plot the standard curve. Finally, the concentration of the sample can then be calculated from the RGB data of the sample. This value is either recorded or directly paste into a spreadsheet program for further calculations. The Excel 2021 (Version 2108, Microsoft) spreadsheet program were used for further data processing.

Parameters of LIGHt smartphone colorimetric analysis

The RGB data were extracted by taking photos with a vivo X80 smartphone (vivo Mobile Communication Co., Ltd., Dongguan, Guangdong, China) running an OriginOS (Version 4, vivo Mobile Communication Co.) system. The default camera App in the smartphone system is used for the entire experiment using default settings of shooting pictures. Specifically, the focal length was 35 mm, the aperture was f/1.75, the ISO speed was ISO-216, and no flash is used. The generated images are in Joint Photographic Experts Group (JPEG) format with a resolution of 4096 × 3072. To compare the accuracy of the smartphone colorimetry method with traditional instrumental analysis methods, the absorbance data for all the microplates to be measured are extracted at 620 nm by the Infinite M1000 PRO microplate reader (Tecan Group Ltd., Männedorf, Switzerland) at room temperature, and then the RGB data were extracted using the smartphone camera.

Using the absorbance and RGB data measured by the series of standard solutions, the standard curves are constructed. In preliminary studies, we found that the intensity of the red color is most suitable for the colorimetric detection of the anthrone-sulfuric acid method. This is also consistent with the fact that the maximum absorbance is at 620 nm of the anthrone complex that falls within the approximate wavelengths for red color, which is approximately 620–750 nm. Therefore, a standard curve is plotted with the starch concentration on the x-axis and the corresponding intensity of red light from the RGB data. The intensity of the red color is referred to as the R value in the subsequent texts.

Results

Preliminary assessment of the influence of smartphone camera variability and lighting conditions on smartphone colorimetry

The LIGHt smartphone colorimetry aims to develop robust, device-independent colorimetric measurements using smartphones. Therefore, it is important to evaluate and calibrate smartphone-based colorimetric detection platforms to reduce external interference. Additionally, consistency and accuracy of measurements across different devices and environments is necessary to be evaluated. To investigate the impact of light control and the selection of different smartphone cameras, different smartphones and different lighting conditions were applied. We used three smartphones from different brands (HONOR 30, vivo X80, iQOO Z9 Turbo) to take same snapshots of the same drop of a colorimeter plate with a standard solution under the same scene, using the same focal length and aperture, in both well- and poorly-lit rooms. The resolution of photos obtained by the HONOR 30, vivo X80, iQOO Z9 Turbo smartphones were 1318 × 1378, 1385 × 1511, 1385 × 1511, and 1518 × 1734, respectively. All experiments were performed without flash. The RGB data from the photos to calculate the starch content and compared it with the standard results detected by the plate reader were extracted. From Table 1, it can be observed that the RGB detection results in dim light environments are generally lower than those in well-lit environments and are closer to the detection results of the plate reader. The relative error between the detection results of the three smartphones in well-lit and dim light ranges from 2 to 5%, with an average relative error of 4%. In dim light environments, the average starch content is 18 g/100 g dry weight, with a relative standard deviation (RSD) of 1%. Although different types of phone sensor, system, software, lighting conditions, including well-lit or dim light were used, the results of different smartphone cameras were in good agreement. This is partly because the calibration curve was tested in real-time, ensuring that the final concentration relies on a series of calibrated standards. The average starch content detected in dim light has a relative error of 5.1% compared to the detection results of the plate reader. Based on this evaluation, all the subsequent evaluations use vivo X80 smartphone as the single smartphone device only.

Table 1 Colorimetric results of three brands of smartphones under different lighting conditions*.
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Comparison of LIGHt smartphone colorimetry and conventional spectrophotometry

Table 2 lists the standard curves and corresponding R2 values obtained for starch determination by microplate reader and smartphone colorimetry. The standard curves illustrate the relationship between starch concentration and measured values by the two methods. The R2 values for both methods are 0.9972 and 0.9941, respectively, indicating a strong correlation between actual starch concentration and measured values. This demonstrates reliable detection performance and also indicates the feasibility of using the R value to detect starch concentration.

Table 2 Standard curve of starch concentration.
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Figure 5 presents the starch content measured in tobacco powder samples using absorbance and R values. The data from the microplate reader column shows a clear difference in starch content among different tobacco samples, ranging from 10 to 80 g/100 g dry weight. Such differences may be caused by various factors, such as differences in tobacco variety, cultivation environment, and fertilization conditions. Different tobacco varieties can lead to differences in the efficiency of starch synthesis and accumulation. Cultivation environment, such as soil quality, sunlight conditions, and irrigation, can also affect tobacco growth, and fertilization conditions can influence the growth rate of tobacco and the progress of photosynthesis. Comparing the detection results of starch content in 27 tobacco samples by microplate reader method and smartphone colorimetry, the average relative error of the smartphone colorimetry is 6%, with a maximum relative error of 20% and a minimum relative error of 0.5%. This indicates that the method has a high detection accuracy and potential as a rapid on-site method for determining starch content in tobacco leaves. Further improvement of the detection accuracy and standardizing the detection process is expected to enhance the performance of smartphone colorimetry.

Fig.  5
figure 5

Comparison of starch content by plate reader and smartphone colorimetry.

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Method validation of LIGHt smartphone colorimetry

Table 3 presents the results of the inter- and intra-day reproducibility of the smartphone colorimetry method. The tobacco leaf samples No. 27 were selected for the experiment. First, triplicate intra-day experiments were conducted to assess the stability of the method, ensuring the reliability and consistency of the data. In the inter-day evaluation, replicate experiments were conducted in three groups each day for three consecutive days to assess the stability of the method. The RSD for the microplate reader and smartphone colorimetry were respectively 4% and 11%, indicating that the smartphone colorimetry method has good reproducibility, and were close to the microplate reader analysis. The results indicate that this method is capable of detecting starch at a relatively low cost and with excellent portability. The R value is suitable for use as the RGB data based on the anthrone-sulfuric acid colorimetric method.

Table 3 Precision of starch content determined by microplate reader and smartphone colorimetry.
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The detection limit was tested according to the standard deviation of the blank27,28,29, which involves measuring 10 blank samples (without starch) in succession. The noise level is then estimated by calculating the standard deviation of the blank samples. The detection limit (LOD) is set as three times the standard deviation. The standard deviation of the starch content in the 10 blank samples was determined to be 0.51 µg/mL, resulting in a detection limit of 1.53 µg/mL for this method. A recent method reported by Du et al. that employs alkali potassium persulfate digestion has reached a detection limit range from 0.44 × 10− 2 to 9.09 × 10− 2 mg/100 mg30. In comparing these two, the alkali potassium persulfate digestion method offers a narrower detection limit range. The smartphone colorimetry method, while potentially more accessible due to the widespread availability of smartphones, its LOD of 1.53 µg/mL is higher than the upper detection limit of the alkali method, suggesting it may be less sensitive for detecting lower concentrations of carbohydrates.

The recovery test is an important evaluation in the analysis procedure, which can verify the accuracy and reliability of the smartphone colorimetry method. The recovery rates for samples of three different tobacco leaves were tested, and the results are shown in Table 4. The recovery rates ranged from 89.54 to 111.84%, with an average value of 95.72%, indicating that the results obtained by the smartphone colorimetry method are relatively accurate.

Table 4 Recovery test results of smartphone colorimetry*.
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Evaluation of green performance of LIGHt smartphone colorimetry

The LIGHt smartphone colorimetry is in good agreement of the recent emerging tendency of the green chemistry, which is a set of principles designed to reduce or eliminate the environmental impact of chemical processes. The LIGHt starch detection method was evaluated by the blue applicability grade index (BAGI)31, a novel metric for evaluating the practicability of a method in analytical chemistry. The BAGI were ranging from 25 to 100, with the higher the score, the more practical the method, and vice versa. By a series of efforts put in using less chemicals, choosing safer chemicals, and designing efficient and high-throughput analysis approach, the BAGI index of this method resulted in a final score 80 (Fig. 6), indicating superior performance in terms of practicality and applicability toward the concept of green chemistry.

Fig.  6
figure 6

BAGI index pictograms for the LIGHt smartphone colorimetric starch detection. A direct snapshot from the online tool (https://bagi-index.anvil.app/) were presented for calculation detail.

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Discussion

This study introduces a LIGHt smartphone colorimetry method that utilizes the smartphone camera and an RGB data extraction program written in Python to extract the R values from stained solutions. The results obtained by the smartphone method were tested for reproducibility, detection limit, and recovery rate. The performance of LIGHt smartphone colorimetry was compared with traditional spectrophotometry. Overall, there is a significant consistency in the detection results of starch content in tobacco leaf samples between the two methods. The average relative error of the smartphone colorimetry method is 6.00%, the RSD for the three-day repeatability (n = 3) is 11%, the detection limit is 1.53 µg/mL, and the average recovery rate is 95.72%. The detection results of Table 1 indicate that the impact of different smartphone photography on the RGB detection method can be essentially neglected, and the average relative error in both well-lit and dim light environments is 3.83%, indicating that this method has a wide applicability. While there is still slightly lower detection precision compared to traditional spectroscopic instruments like microplate readers, the method has proven its feasibility for rapid detection of starch content in tobacco powder and its potential for on-site testing in the tobacco industry due to its good reproducibility and stability. The method does not require complex laboratory equipment or professional personnel to operate, enabling rapid on-site testing. Furthermore, the LIGHt smartphone colorimetry for starch detection resulted a BAGI score of 80, reflecting its alignment with green chemistry principles and its high practicality. The smartphone colorimetry method, based on the principle of colorimetry, is not only applicable to the anthrone-sulfuric acid method for detecting starch content in tobacco leaves as in this study, but it can also be used in similar detections such as the Bradford method for protein content detection. Therefore, the RGB value extraction device developed in this study has strong versatility and is expected to be extended to the detection of nutrients such as starch and protein in other agricultural products. This workflow can be easily transferred into a portable, low-cost, Android-based handheld device for convenient colorimetric measurements.