Bloodstain in forensics: From visual inspections to AI-assisted pattern analysis and age estimation Article Swipe
Chitnarong Sirisathitkul
,
Yaowarat Sirisathitkul
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32703/2415-7422--2025-15-1-26-46
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32703/2415-7422--2025-15-1-26-46
Bloodstains have long served as critical evidence in forensic investigations, providing insights into the timing and nature of violent crimes. This article traces the historical evolution of bloodstain analysis, from early visual inspection to the adoption of modern methods and technologies. Blood pattern analysis has now advanced into a systematic science and incorporated artificial intelligence technology, offering quantitative insights into the mechanisms of blood spatter. For age estimation of bloodstains, DNA analysis extracts temporal changes in genetic materials from degraded bloodstains. High-performance liquid chromatography further complemented bloodstain investigations by quantifying biochemical markers indicative of time since deposition. Spectroscopic methods, including Raman and infrared spectroscopy, have identified specific molecular vibrations associated with the temporal degradation of blood components, while optical techniques based on photon reflection, absorption, and fluorescence provide alternative pathways for estimating bloodstain age. Smartphone-based colorimetry has emerged as a cost-effective and portable solution, tracking the visible progression of blood color from bright red to dark brown over time. Moreover, hyperspectral imaging integrates imaging and spectroscopy, allowing spatially resolved age estimation by analyzing spectral data at the pixel level. This article highlights the historical progression and technological advancements that have shaped bloodstain analysis in forensic discipline. By integrating modern instrumentation with artificial intelligence technologies, the field continues to move closer to reliable on-site analysis. However, challenges such as environmental variability, substrate effects, and standardization remain. Continued research and validation are imperative to refine these methods and establish standardized protocols for forensic applications. This historical and technical overview underscores the transformative impact of interdisciplinary innovation on the evolution of bloodstain analysis, bridging the gap between laboratory research and practical forensic settings.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32703/2415-7422--2025-15-1-26-46
- https://www.hst-journal.com/index.php/hst/article/download/688/505
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4411797030Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32703/2415-7422--2025-15-1-26-46Digital Object Identifier
- Title
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Bloodstain in forensics: From visual inspections to AI-assisted pattern analysis and age estimationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-30Full publication date if available
- Authors
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Chitnarong Sirisathitkul, Yaowarat SirisathitkulList of authors in order
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https://doi.org/10.32703/2415-7422--2025-15-1-26-46Publisher landing page
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https://www.hst-journal.com/index.php/hst/article/download/688/505Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.hst-journal.com/index.php/hst/article/download/688/505Direct OA link when available
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.evolution | 25, 256 |
| abstract_inverted_index.including | 99 |
| abstract_inverted_index.materials | 77 |
| abstract_inverted_index.molecular | 107 |
| abstract_inverted_index.practical | 267 |
| abstract_inverted_index.protocols | 238 |
| abstract_inverted_index.providing | 10 |
| abstract_inverted_index.settings. | 269 |
| abstract_inverted_index.solution, | 143 |
| abstract_inverted_index.spatially | 167 |
| abstract_inverted_index.substrate | 220 |
| abstract_inverted_index.technical | 245 |
| abstract_inverted_index.artificial | 53, 201 |
| abstract_inverted_index.associated | 109 |
| abstract_inverted_index.bloodstain | 27, 86, 132, 191, 258 |
| abstract_inverted_index.challenges | 215 |
| abstract_inverted_index.estimating | 131 |
| abstract_inverted_index.estimation | 67, 170 |
| abstract_inverted_index.highlights | 181 |
| abstract_inverted_index.historical | 24, 183, 243 |
| abstract_inverted_index.identified | 105 |
| abstract_inverted_index.imperative | 230 |
| abstract_inverted_index.indicative | 92 |
| abstract_inverted_index.innovation | 253 |
| abstract_inverted_index.inspection | 32 |
| abstract_inverted_index.integrates | 162 |
| abstract_inverted_index.laboratory | 264 |
| abstract_inverted_index.mechanisms | 61 |
| abstract_inverted_index.systematic | 49 |
| abstract_inverted_index.techniques | 119 |
| abstract_inverted_index.validation | 228 |
| abstract_inverted_index.vibrations | 108 |
| abstract_inverted_index.Bloodstains | 0 |
| abstract_inverted_index.absorption, | 124 |
| abstract_inverted_index.alternative | 128 |
| abstract_inverted_index.biochemical | 90 |
| abstract_inverted_index.colorimetry | 135 |
| abstract_inverted_index.components, | 116 |
| abstract_inverted_index.degradation | 113 |
| abstract_inverted_index.deposition. | 96 |
| abstract_inverted_index.discipline. | 195 |
| abstract_inverted_index.integrating | 197 |
| abstract_inverted_index.progression | 147, 184 |
| abstract_inverted_index.quantifying | 89 |
| abstract_inverted_index.reflection, | 123 |
| abstract_inverted_index.technology, | 55 |
| abstract_inverted_index.underscores | 247 |
| abstract_inverted_index.advancements | 187 |
| abstract_inverted_index.bloodstains, | 69 |
| abstract_inverted_index.bloodstains. | 80 |
| abstract_inverted_index.complemented | 85 |
| abstract_inverted_index.fluorescence | 126 |
| abstract_inverted_index.incorporated | 52 |
| abstract_inverted_index.intelligence | 54, 202 |
| abstract_inverted_index.quantitative | 57 |
| abstract_inverted_index.standardized | 237 |
| abstract_inverted_index.variability, | 219 |
| abstract_inverted_index.Spectroscopic | 97 |
| abstract_inverted_index.applications. | 241 |
| abstract_inverted_index.environmental | 218 |
| abstract_inverted_index.hyperspectral | 160 |
| abstract_inverted_index.spectroscopy, | 103, 165 |
| abstract_inverted_index.technological | 186 |
| abstract_inverted_index.technologies, | 203 |
| abstract_inverted_index.technologies. | 40 |
| abstract_inverted_index.chromatography | 83 |
| abstract_inverted_index.cost-effective | 140 |
| abstract_inverted_index.investigations | 87 |
| abstract_inverted_index.transformative | 249 |
| abstract_inverted_index.instrumentation | 199 |
| abstract_inverted_index.investigations, | 9 |
| abstract_inverted_index.standardization | 223 |
| abstract_inverted_index.High-performance | 81 |
| abstract_inverted_index.Smartphone-based | 134 |
| abstract_inverted_index.interdisciplinary | 252 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.27535066 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |