In vitro Sun Protection Factor calculation using the Mansur method and Its uncertainty for Sintrong leave without extraction stage

In vitro Sun Protection Factor calculation using the Mansur method and Its uncertainty for Sintrong leave without extraction stage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report In vitro Sun Protection Factor calculation using the Mansur method and Its uncertainty for Sintrong leave without extraction stage Muhammad Roy Asrori, Siti Khoirunnisa, Mailinda Ayu Hana Margaretha, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7243816/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sun protection factor (SPF) value is essential information for UV protection. Natural resources have been a good potential SPF for UV protection. However, the testing method for the SPF value was less efficient if an extract sample was used. In this study, the method was evaluated the SPF value using the Mansur method and its uncertainty, and introduced the non- extract sample (Sintrong leave as model). After UV-visible spectrophotometer measurement, the result showed that the most SPF Value was 28.8733 ± 0.2382 (moderate category) from the solution of 1000 ppm. This developed method was acceptable based on Linearity, LOD, LOQ, and precision. The profiles of UV graph results showed the activity as sun protection at 290 – 320 nm with their characteristic peaks. Analytical Chemistry Applied Biochemistry Sun protection factor Mansur method uncertainty Sintrong non- extraction Figures Figure 1 Figure 2 Introduction Sun energy, especially UV rays, can cause skin diseases and reduce beauty (Singer et al., 2019 ). UV rays consist of UV A, UV B, and UV C. The UV portion of the spectrum is further divided into three categories: ultraviolet C (UVC) (220–290 nm), ultraviolet B (UVB) (290–320 nm), and ultraviolet A (UVA) (320–400 nm). However, UV B and UV A can reach the Earth's surface and can impact biological systems (Black, 2014 ). The radiation can contact the human skin and penetrate the skin layer, especially UV A and UV B. Therefore, UV protection has become important in cosmetics including the sunscreen formulation (Portilho et al., 2022 ), especially when using natural resources. UV protection content is known as the sun protection factor (SPF). Keeping in mind that the sunscreen cannot be 100% of UV protection, but the protection can prevent skin disease (Serpone, 2021 ). Some SPF value testing methods were developed in vivo and in vitro. The Mansur methods has been famous for calculating SPF value in vitro. Many studies reported the results separately Mansur methods. Mansur et al. (Mansur et al., 1986 ) developed a very simple mathematical equation which substitutes the in vitro method proposed by Sayre et al (Sayre et al., 1979 ), utilizing UV spectrophotometry (Dutra et al., 2004 ). On the other hand, SPF values are difficult to measure since the SPF concept is simple because of the high variability of the unregulated interaction of light and skin (Osterwalder et al., 2024 ). Some previous studies (in review article) concluded that in vitro SPF showed varied results because of the various concentrations of samples (Chavda et al., 2023 ). However, there is less report about the uncertainty calculation of the SPF value. The uncertainty needs to be provided completely. On the other hand, many studies (in review article) reported the SPF from extracted samples (He et al., 2021 ). There is a gap that is no report about calculating the SPF value from a non-extraction sample. This study introduces new preparation of a non- extracted sample, it means without extraction stage. Therefore, this study aims to examine the SPF value using a non- extracted Sintrong leave as a sample. It will also discuss a UV graph result and its uncertainty using Mansur methods. Hopefully, this study will give more effective in the calculation of SPF value Materials and Methods The materials in this study were Ethanol 96% (p.a., Smartlab), and Sintrong leave as sample. Using UV-Vis spectrophotometer (Thermofisher Scientific, Evolution 201), the sample was placed in a cuvette and reaction tubes. Sintrong leave preparation with a new non-extraction method Clean sample was weighed based on the calculation below: The values of EE x I are constants (Dutra et al., 2004). The SPF value was then categorized as follow: non-sunscreen (SPF < 2), minimum potency (SPF 2-11), moderate potency (SPF 12-30), and high protection (SPF ≥ 30) from FDA (Food and Drug Administration) (Rinatha et al., 2023). Furthermore, the data was analyzed to calculate linearity, limit of detection (LOD), limit of quantity (LOQ), precision, and uncertainty. For uncertainty, the possible uncertainty came from: weighing, solution, and spectrophotometer measurement. Results and Discussion Calculation Results of Sun Protection Factor using The Mansur method In this study, the uncertainty calculation is presented as listed in Table 1. Furthermore, the uncertainty value can be added to the SPF value as listed in Table 2. The calculation of SPF value can be seen in supplementary S1. Table 2. SPF value of a non- extract Sintrong leave using the Mansur method Variation SPF value ± uncertainty category 100 2.2121 ± 0.046 Minimum potency 200 4.6558 ± 0.0464 minimum potency 300 7.5442 ± 0.051 minimum potency 400 10.7872 ± 0.0464 minimum potency 500 14.0736 ± 0.0464 moderate potency 1000 28.8733 ± 0.2382 moderate potency Based on the results in Table 2, the SPF value can be categorized as moderate potency for a non- extract Sintrong leave. On the other hand, the more increase the concentration, the more increase the SPF value. The previous study reported that the SPF value of the extracted Sintrong leave was 3.9 at concentration of 80 ppm (Rusli et al., 2024). Therefore, this study (non- extracted sample) reflected proximity of value to the previous study (extracted sample). Furthermore, the linearity value of SPF testing has been analyzed as in Figure 1. Based on Figure 1, the linearity value is 0.9993. The linearity approaches value 1 as active category. Therefore, the method in this study is highly reliable and produces consistent results across the measured range. Subsequently, the LOD obtained in this study is 30.95 ppm. The LOD represents the study's analytical method can reliably detect the sample at 30.95 ppm. The LOQ obtained in this study is 93.78 ppm. The LOQ result represents the lowest concentration of a substance that can be reliably quantified using the method in this study. Furthermore, calculation result of precision based on the sample (1000 ppm) is about 0.011. the precision is acceptable because of less than 2% limit (Ermer & Ploss, 2005). Profiling UV results UV spectrophotometer has been used to resulting the absorbance value as required. The UV measurement results can be also seen in Figure 2. Based on the Figure 1, the UV results showed the existence of the sun protection activity. There is a shoulder at 300 nm and maximum absorption (λmax) at 330 nm. The figure1 show the broad absorption bands attributed to π→π* and/or η→π* transitions. This was estimated as aromatic rings or carbonyl groups in the molecular structures of the sample (Saito et al., 2019). However, the sample of 1000 ppm showed a different graph. The Fig. 1.f. showed many shoulders and presence of absorption at 340 nm. The case may be caused of the color intensity of the sample (1000 ppm) as green solution regarding complementary color (yellow – green) at 340 – 450 nm (Pratiwi & Nandiyanto, 2022). Furthermore, the wavelength above 340 was not significantly absorption. The case reflected that it is not very harmful relating to sunburn and skin cancer (Pelizzo et al., 2012). Therefore, SPF activity of the sample of 100 – 500 ppm was more precise than the sample of 1000 ppm. Conclusion Based on the discussed study, sunscreen can be calculated using Mansur method without a significant difference value. The highest SPF value is 28.8733 ± 0.2382 (medium) at 1000 ppm. The developed method is acceptable based on linearity, LOD, LOQ, and precision. On the graph of Absorbance vs wavelength, the sample (Sintrong leave) showed the essential peaks relating to SPF activity. The future challenge is how to present the calculation data using ISO 23675:2024 and ISO 23698:2024. Declarations Acknowledgment Thanks to the Laboratory of Minerals and Advanced Materials, Universitas Negeri Malang (UM, Indonesia) for supporting this study. References Black, A. T. (2014). Ultraviolet A. In P. B. T.-E. of T. (Third E. Wexler (Ed.), Encyclopedia of Toxicology (Third Edition) (pp. 893–895). Academic Press. https://doi.org/10.1016/B978-0-12-386454-3.01181-7 Chavda, V. P., Acharya, D., Hala, V., Daware, S., & Vora, L. K. (2023). Sunscreens: A comprehensive review with the application of nanotechnology. Journal of Drug Delivery Science and Technology , 86 , 104720. https://doi.org/10.1016/j.jddst.2023.104720 Dutra, E. A., Oliveira, D. A. G. da C. e, Kedor-Hackmann, E. R. M., & Santoro, M. I. R. M. (2004). Determination of sun protection factor (SPF) of sunscreens by ultraviolet spectrophotometry. Revista Brasileira de Ciências Farmacêuticas , 40 (3), 382–385. https://doi.org/10.1590/S1516-93322004000300014 Ermer, J., & Ploss, H.-J. (2005). Validation in pharmaceutical analysis: Part II: central importance of precision to establish acceptance criteria and for verifying and improving the quality of analytical data. Journal of Pharmaceutical and Biomedical Analysis , 37 (5), 859–870. https://doi.org/10.1016/j.jpba.2004.06.018 He, H., Li, A., Li, S., Tang, J., Li, L., & Xiong, L. (2021). Natural components in sunscreens: Topical formulations with sun protection factor (SPF). Biomedicine & Pharmacotherapy , 134 , 111161. https://doi.org/10.1016/j.biopha.2020.111161 Mansur, J. de S., Breder, M. N. R., Mansur, M. C. d’Ascençäo, & Azulay, R. D. (1986). Determinação Do Fator De Proteção Solar Por Espectrofotometria. Anais Brasileiros de Dermatologia , 61 (3), 121–124. Osterwalder, U., Hubaud, J.-C., Perroux-David, E., Moraine, T., & van den Bosch, J. (2024). Sun-protection factor of zinc-oxide sunscreens: SPFin vitro too low compared to SPFin vivo—a brief review. Photochemical & Photobiological Sciences , 23 (10), 1999–2009. https://doi.org/10.1007/s43630-024-00644-0 Pelizzo, M., Zattra, E., Nicolosi, P., Peserico, A., Garoli, D., & Alaibac, M. (2012). In Vitro Evaluation of Sunscreens: An Update for the Clinicians. International Scholarly Research Notices , 2012 (1), 352135. https://doi.org/10.5402/2012/352135 Portilho, L., Aiello, L. M., Vasques, L. I., Bagatin, E., & Leonardi, G. R. (2022). Effectiveness of sunscreens and factors influencing sun protection: a review. Brazilian Journal of Pharmaceutical Sciences , 58 , e20693. https://doi.org/10.1590/s2175-97902022e20693 Pratiwi, R. A., & Nandiyanto, A. B. D. (2022). How to Read and Interpret UV-VIS Spectrophotometric Results in Determining the Structure of Chemical Compounds. Indonesian Journal of Educational Research and Technology , 2 (1), 1–20. https://doi.org/10.17509/ijert.v2i1.35171 Rinatha, E., Ulfa, A. M., & Tutik, T. (2023). Stability testing abd determination of sun protection (SPF) value in gel formulation combining Moringa oleifera l. leaf extract with Citrus aurantifolia peel. Jurnal Fitofarmaka Indonesia , 10 (3), 53–60. https://doi.org/10.33096/jffi.v10i3.1000 Rusli, R., Nuri, I., Ramadani, M. A., Siregar, V. O., Priastomo, M., & Faisal, M. (2024). Aktivitas Antioksidan dan Tabir Surya Ekstrak Etanol Tanaman Crassocephalum crepidioides (Benth.). Jurnal Sains Dan Kesehatan , 4 (3), 320–325. https://jsk.ff.unmul.ac.id/index.php/JSK/article/view/575 Saito, G. P., Bizari, M., Cebim, M. A., Correa, M. A., Jafelicci Junior, M., & Davolos, M. R. (2019). Study of the colloidal stability and optical properties of sunscreen creams. Eclética Química , 44 (2 SE-Original articles), 26–36. https://doi.org/10.26850/1678-4618eqj.v44.2.2019.p26-36 Sayre, R. M., Agin, P. P., LeVee, G. J., & Marlowe, E. (1979). A comparison of in vivo and in vitro testing of sunscreening formulas. Photochemistry and Photobiology , 29 (3), 559–566. https://doi.org/10.1111/j.1751-1097.1979.tb07090.x Serpone, N. (2021). Sunscreens and their usefulness: have we made any progress in the last two decades? Photochemical & Photobiological Sciences , 20 (2), 189–244. https://doi.org/10.1007/s43630-021-00013-1 Singer, S., Karrer, S., & Berneburg, M. (2019). Modern sun protection. Current Opinion in Pharmacology , 46 , 24–28. https://doi.org/10.1016/j.coph.2018.12.006 Sumitra, J., & Pasaribu, E. N. R. (2022). Anti-inflammatory excitivity test of sintrong leaf ethanol extract (Crassocephalum crepidiodes) on male white mice. Jurnal Farmasimed (JFM) , 5 (1), 52–56. https://doi.org/10.35451/jfm.v5i1.1279 Table Table 1 is available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7243816","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":492632923,"identity":"120facf8-ed85-40c5-bd03-0d8d52730f58","order_by":0,"name":"Muhammad Roy Asrori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYFACHjDJ2AAXYGY+AGUwGBCrhS2BVC0MPHCVWLXozsg9+Olmjp1sg/ThYxI//tyTM2/n+SZ1g8FOnoGdeQM2LWY38pKlc7clGzfwpaVJ9rYVG8sc5t0mncOQbNjAzFaAXUuOAVALc2IDD4+ZBG9DQuIMZrAW5gQGZh6sDgNqMf6du60erEXyzx+QFp5nQC31+LSYAW05DNYizcMG1sIG1HIYt5Yzb8ysc7cdN27jYUu2lm1LMJZgZjO2zjE4btiGyy/Hc4xv526rlu3nYT54882fBDkJ/sMPb+dUVMvz8x/GGmJwwMbAwCKB4BqARQgC5g9EKBoFo2AUjIIRCACC5FF6muT87gAAAABJRU5ErkJggg==","orcid":"","institution":"Universitas Negeri Malang","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Roy","lastName":"Asrori","suffix":""},{"id":492632924,"identity":"27f77176-535c-4fd0-983c-98d5a434002b","order_by":1,"name":"Siti Khoirunnisa","email":"","orcid":"","institution":"Universitas Muhammadiyah Yogyakarta","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"","lastName":"Khoirunnisa","suffix":""},{"id":492636434,"identity":"9a55d998-c275-421d-93d5-a6d2156ac582","order_by":2,"name":"Mailinda Ayu Hana Margaretha","email":"","orcid":"","institution":"Universitas Negeri Malang","correspondingAuthor":false,"prefix":"","firstName":"Mailinda","middleName":"Ayu Hana","lastName":"Margaretha","suffix":""},{"id":492636435,"identity":"03610bfa-493a-41c3-82bb-e15c2abc02d3","order_by":3,"name":"Deni Ainur Rokhim","email":"","orcid":"","institution":"Universitas Negeri Malang","correspondingAuthor":false,"prefix":"","firstName":"Deni","middleName":"Ainur","lastName":"Rokhim","suffix":""},{"id":492636436,"identity":"ae76fc73-8296-400b-aab0-d02386e30af2","order_by":4,"name":"Suharti","email":"","orcid":"","institution":"Universitas Negeri Malang","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Suharti","suffix":""},{"id":492636437,"identity":"d41306ed-a9d4-47ee-ade4-52061b0ceb96","order_by":5,"name":"Surjani Wonorahardjo","email":"","orcid":"","institution":"Universitas Negeri Malang","correspondingAuthor":false,"prefix":"","firstName":"Surjani","middleName":"","lastName":"Wonorahardjo","suffix":""}],"badges":[],"createdAt":"2025-07-29 13:35:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7243816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7243816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87892532,"identity":"f8df0b11-94c9-48bc-a006-4ac4f73bd77e","added_by":"auto","created_at":"2025-07-30 06:55:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10585,"visible":true,"origin":"","legend":"\u003cp\u003elinearity value of SPF value vs concentration\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7243816/v1/e935cd65565bf93e65e662a7.png"},{"id":87891785,"identity":"bf8d292b-fbfa-41e2-be33-467b5ec1d5ed","added_by":"auto","created_at":"2025-07-30 06:47:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216744,"visible":true,"origin":"","legend":"\u003cp\u003eAbsorbance change (10 repetition) of the sample in concentration of: (A) 100 ppm, (B) 200 ppm, (C) 300 ppm, (D) 400 ppm, (E) 500 ppm, and (F) 1000 ppm. All the sample were re-measured until 10 folds.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7243816/v1/f9fb01eb67cbf28170f4af66.png"},{"id":87893004,"identity":"d97d2a65-9415-404c-a7e7-fc888f86cae9","added_by":"auto","created_at":"2025-07-30 07:03:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":622105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7243816/v1/bec2a478-b988-4481-8f33-6cc7c91c3c19.pdf"},{"id":87891779,"identity":"8c1d247f-0b93-46da-9269-0b1b94ddee47","added_by":"auto","created_at":"2025-07-30 06:47:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20421,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7243816/v1/d7854ca969bfd52771d9e9d1.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIn vitro Sun Protection Factor calculation using the Mansur method and Its uncertainty for Sintrong leave without extraction stage\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSun energy, especially UV rays, can cause skin diseases and reduce beauty (Singer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). UV rays consist of UV A, UV B, and UV C. The UV portion of the spectrum is further divided into three categories: ultraviolet C (UVC) (220\u0026ndash;290 nm), ultraviolet B (UVB) (290\u0026ndash;320 nm), and ultraviolet A (UVA) (320\u0026ndash;400 nm). However, UV B and UV A can reach the Earth's surface and can impact biological systems (Black, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The radiation can contact the human skin and penetrate the skin layer, especially UV A and UV B. Therefore, UV protection has become important in cosmetics including the sunscreen formulation (Portilho et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially when using natural resources. UV protection content is known as the sun protection factor (SPF). Keeping in mind that the sunscreen cannot be 100% of UV protection, but the protection can prevent skin disease (Serpone, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome SPF value testing methods were developed in vivo and in vitro. The Mansur methods has been famous for calculating SPF value in vitro. Many studies reported the results separately Mansur methods. Mansur \u003cem\u003eet al.\u003c/em\u003e (Mansur et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) developed a very simple mathematical equation which substitutes the \u003cem\u003ein vitro\u003c/em\u003e method proposed by Sayre \u003cem\u003eet al\u003c/em\u003e (Sayre et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), utilizing UV spectrophotometry (Dutra et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). On the other hand, SPF values are difficult to measure since the SPF concept is simple because of the high variability of the unregulated interaction of light and skin (Osterwalder et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome previous studies (in review article) concluded that in vitro SPF showed varied results because of the various concentrations of samples (Chavda et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, there is less report about the uncertainty calculation of the SPF value. The uncertainty needs to be provided completely. On the other hand, many studies (in review article) reported the SPF from extracted samples (He et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is a gap that is no report about calculating the SPF value from a non-extraction sample. This study introduces new preparation of a non- extracted sample, it means without extraction stage. Therefore, this study aims to examine the SPF value using a non- extracted Sintrong leave as a sample. It will also discuss a UV graph result and its uncertainty using Mansur methods. Hopefully, this study will give more effective in the calculation of SPF value\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe materials in this study were Ethanol 96% (p.a., Smartlab), and Sintrong leave as sample. Using UV-Vis spectrophotometer (Thermofisher Scientific, Evolution 201), the sample was placed in a cuvette and reaction tubes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSintrong leave preparation with a new non-extraction method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClean sample was weighed based on the calculation below:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe values of EE x I are constants (Dutra et al., 2004). The SPF value was then categorized as follow:\u0026nbsp;non-sunscreen (SPF \u0026lt; 2), minimum potency (SPF 2-11), moderate potency (SPF 12-30), and high protection (SPF \u0026ge; 30) \u0026nbsp;from FDA (Food and Drug Administration)\u0026nbsp;(Rinatha et al., 2023).\u003c/p\u003e\n\u003cp\u003eFurthermore, the data was analyzed to calculate linearity, limit of detection (LOD), limit of quantity (LOQ), precision, and uncertainty. For uncertainty, the possible uncertainty came from: weighing, solution, and spectrophotometer measurement.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eCalculation Results of Sun Protection Factor using The Mansur method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the uncertainty calculation is presented as listed in Table 1.\u003c/p\u003e\n\u003cp\u003eFurthermore, the uncertainty value can be added to the SPF value as listed in Table 2. The calculation of SPF value can be seen in supplementary S1.\u003c/p\u003e\n\u003cp\u003eTable 2. SPF value of a non- extract Sintrong leave using the Mansur method\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPF value\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn; uncertainty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2.2121 \u0026plusmn; 0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMinimum potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e4.6558 \u0026plusmn; 0.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eminimum potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e7.5442 \u0026plusmn; 0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eminimum potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e10.7872 \u0026plusmn; 0.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eminimum potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e14.0736 \u0026plusmn; 0.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003emoderate potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e28.8733 \u0026plusmn; 0.2382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003emoderate potency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBased on the results in Table 2, the SPF value can be categorized as moderate potency for a non- extract Sintrong leave. On the other hand, the more increase the concentration, the more increase the SPF value. The previous study reported that the SPF value of the extracted Sintrong leave was 3.9 at concentration of 80 ppm (Rusli et al., 2024). Therefore, this study (non- extracted sample) reflected proximity of value to the previous study (extracted sample).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the linearity value of SPF testing has been analyzed as in Figure 1. Based on Figure 1, the linearity value is 0.9993. The linearity approaches value 1 as active category. Therefore, the method in this study is highly reliable and produces consistent results across the measured range. Subsequently, the LOD obtained in this study is 30.95 ppm. The LOD represents the study\u0026apos;s analytical method can reliably detect the sample at 30.95 ppm. The LOQ obtained in this study is 93.78 ppm. The LOQ result represents the lowest concentration of a substance that can be reliably quantified using the method in this study.\u003c/p\u003e\n\u003cp\u003eFurthermore, calculation result of precision based on the sample (1000 ppm) is about 0.011. the precision is acceptable because of less than 2% limit (Ermer \u0026amp; Ploss, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfiling UV results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUV spectrophotometer has been used to resulting the absorbance value as required. The UV measurement results can be also seen in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the Figure 1, the UV results showed the existence of the sun protection activity. There is a shoulder at 300 nm and maximum absorption (\u0026lambda;max) at 330 nm. The figure1 show the broad absorption bands attributed to \u0026pi;\u0026rarr;\u0026pi;* and/or \u0026eta;\u0026rarr;\u0026pi;* transitions. This was estimated as aromatic rings or carbonyl groups in the molecular structures of the sample (Saito et al., 2019). However, the sample of 1000 ppm showed a different graph. The Fig. 1.f. showed many shoulders and presence of absorption at 340 nm. The case may be caused of the color intensity of the sample (1000 ppm) as green solution regarding complementary color (yellow \u0026ndash; green) at 340 \u0026ndash; 450 nm (Pratiwi \u0026amp; Nandiyanto, 2022). Furthermore, the wavelength above 340 was not significantly absorption. The case reflected that it is not very harmful relating to sunburn and skin cancer (Pelizzo et al., 2012). Therefore, SPF activity of the sample of 100 \u0026ndash; 500 ppm was more precise than the sample of 1000 ppm.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the discussed study, sunscreen can be calculated using Mansur method without a significant difference value. The highest SPF value is 28.8733\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2382 (medium) at 1000 ppm. The developed method is acceptable based on linearity, LOD, LOQ, and precision. On the graph of Absorbance vs wavelength, the sample (Sintrong leave) showed the essential peaks relating to SPF activity. The future challenge is how to present the calculation data using ISO 23675:2024 and ISO 23698:2024.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThanks to the Laboratory of Minerals and Advanced Materials, Universitas Negeri Malang (UM, Indonesia) for supporting this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBlack, A. T. (2014). Ultraviolet A. In P. B. T.-E. of T. (Third E. Wexler (Ed.), \u003cem\u003eEncyclopedia of Toxicology (Third Edition)\u003c/em\u003e (pp. 893\u0026ndash;895). 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Validation in pharmaceutical analysis: Part II: central importance of precision to establish acceptance criteria and for verifying and improving the quality of analytical data. \u003cem\u003eJournal of Pharmaceutical and Biomedical Analysis\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(5), 859\u0026ndash;870. https://doi.org/10.1016/j.jpba.2004.06.018\u003c/li\u003e\n \u003cli\u003eHe, H., Li, A., Li, S., Tang, J., Li, L., \u0026amp; Xiong, L. (2021). Natural components in sunscreens: Topical formulations with sun protection factor (SPF). \u003cem\u003eBiomedicine \u0026amp; Pharmacotherapy\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e, 111161. https://doi.org/10.1016/j.biopha.2020.111161\u003c/li\u003e\n \u003cli\u003eMansur, J. de S., Breder, M. N. R., Mansur, M. C. d\u0026rsquo;Ascen\u0026ccedil;\u0026auml;o, \u0026amp; Azulay, R. D. (1986). Determina\u0026ccedil;\u0026atilde;o Do Fator De Prote\u0026ccedil;\u0026atilde;o Solar Por Espectrofotometria. \u003cem\u003eAnais Brasileiros de Dermatologia\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(3), 121\u0026ndash;124.\u003c/li\u003e\n \u003cli\u003eOsterwalder, U., Hubaud, J.-C., Perroux-David, E., Moraine, T., \u0026amp; van den Bosch, J. (2024). Sun-protection factor of zinc-oxide sunscreens: SPFin vitro too low compared to SPFin vivo\u0026mdash;a brief review. \u003cem\u003ePhotochemical \u0026amp; Photobiological Sciences\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(10), 1999\u0026ndash;2009. https://doi.org/10.1007/s43630-024-00644-0\u003c/li\u003e\n \u003cli\u003ePelizzo, M., Zattra, E., Nicolosi, P., Peserico, A., Garoli, D., \u0026amp; Alaibac, M. (2012). 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How to Read and Interpret UV-VIS Spectrophotometric Results in Determining the Structure of Chemical Compounds. \u003cem\u003eIndonesian Journal of Educational Research and Technology\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 1\u0026ndash;20. https://doi.org/10.17509/ijert.v2i1.35171\u003c/li\u003e\n \u003cli\u003eRinatha, E., Ulfa, A. M., \u0026amp; Tutik, T. (2023). Stability testing abd determination of sun protection (SPF) value in gel formulation combining Moringa oleifera l. leaf extract with Citrus aurantifolia peel. \u003cem\u003eJurnal Fitofarmaka Indonesia\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 53\u0026ndash;60. https://doi.org/10.33096/jffi.v10i3.1000\u003c/li\u003e\n \u003cli\u003eRusli, R., Nuri, I., Ramadani, M. A., Siregar, V. O., Priastomo, M., \u0026amp; Faisal, M. (2024). Aktivitas Antioksidan dan Tabir Surya Ekstrak Etanol Tanaman Crassocephalum crepidioides (Benth.). \u003cem\u003eJurnal Sains Dan Kesehatan\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 320\u0026ndash;325. https://jsk.ff.unmul.ac.id/index.php/JSK/article/view/575\u003c/li\u003e\n \u003cli\u003eSaito, G. P., Bizari, M., Cebim, M. A., Correa, M. A., Jafelicci Junior, M., \u0026amp; Davolos, M. R. (2019). Study of the colloidal stability and optical properties of sunscreen creams. \u003cem\u003eEcl\u0026eacute;tica Qu\u0026iacute;mica\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(2 SE-Original articles), 26\u0026ndash;36. https://doi.org/10.26850/1678-4618eqj.v44.2.2019.p26-36\u003c/li\u003e\n \u003cli\u003eSayre, R. M., Agin, P. P., LeVee, G. J., \u0026amp; Marlowe, E. (1979). A comparison of in vivo and in vitro testing of sunscreening formulas. \u003cem\u003ePhotochemistry and Photobiology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 559\u0026ndash;566. https://doi.org/10.1111/j.1751-1097.1979.tb07090.x\u003c/li\u003e\n \u003cli\u003eSerpone, N. (2021). Sunscreens and their usefulness: have we made any progress in the last two decades? \u003cem\u003ePhotochemical \u0026amp; Photobiological Sciences\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(2), 189\u0026ndash;244. https://doi.org/10.1007/s43630-021-00013-1\u003c/li\u003e\n \u003cli\u003eSinger, S., Karrer, S., \u0026amp; Berneburg, M. (2019). Modern sun protection. \u003cem\u003eCurrent Opinion in Pharmacology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 24\u0026ndash;28. https://doi.org/10.1016/j.coph.2018.12.006\u003c/li\u003e\n \u003cli\u003eSumitra, J., \u0026amp; Pasaribu, E. N. R. (2022). Anti-inflammatory excitivity test of sintrong leaf ethanol extract (Crassocephalum crepidiodes) on male white mice. \u003cem\u003eJurnal Farmasimed (JFM)\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 52\u0026ndash;56. https://doi.org/10.35451/jfm.v5i1.1279\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"State University of Malang","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sun protection factor, Mansur method, uncertainty, Sintrong, non- extraction","lastPublishedDoi":"10.21203/rs.3.rs-7243816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7243816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSun protection factor (SPF) value is essential information for UV protection. Natural resources have been a good potential SPF for UV protection. However, the testing method for the SPF value was less efficient if an extract sample was used. In this study, the method was evaluated the SPF value using the Mansur method and its uncertainty, and introduced the non- extract sample (Sintrong leave as model). After UV-visible spectrophotometer measurement, the result showed that the most SPF Value was 28.8733 ± 0.2382 (moderate category) from the solution of 1000 ppm. This developed method was acceptable based on Linearity, LOD, LOQ, and precision. The profiles of UV graph results showed the activity as sun protection at 290 – 320 nm with their characteristic peaks.\u003c/p\u003e","manuscriptTitle":"In vitro Sun Protection Factor calculation using the Mansur method and Its uncertainty for Sintrong leave without extraction stage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 06:47:04","doi":"10.21203/rs.3.rs-7243816/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8eecc2b2-3455-4708-9b2a-ac6321c453d6","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52309979,"name":"Analytical Chemistry"},{"id":52309980,"name":"Applied Biochemistry"}],"tags":[],"updatedAt":"2025-07-30T06:47:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 06:47:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7243816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7243816","identity":"rs-7243816","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}