Detection Of Bloodstains Research Articles

Enhancing forensic blood detection using hyperspectral imaging and advanced preprocessing techniques

Bloodstains are pivotal in forensic investigations as they can provide crucial DNA information about individuals involved in a crime. Traditional methods for bloodstain detection, including chemical tests and forensic lights, have limitations such as non-specificity to human blood and susceptibility to false positives. In forensic blood detection, molecular spectroscopy is crucial for identifying the unique spectral fingerprints of blood, which arise from its molecular composition. Hyperspectral imaging (HSI) leverages this principle by capturing a wide spectrum of light for each pixel in an image, allowing for the detailed analysis of various substances. HSI has emerged as a promising alternative, offering non-contact, rapid, and cost-effective detection of bloodstains by analyzing the visible and near-infrared electromagnetic spectrum. This study explores the application of HSI for blood detection, addressing challenges such as spectral mixing, time-related changes in bloodstain spectra, and data complexity. The study introduces a novel framework to optimize HSI data, enhancing the accuracy and efficiency of bloodstain classification, called the Fast Extraction (FE) framework. It includes two stages. The main method in the first one is the Enhancing Transformation Reduction (ETR) method to reduce the dimension and complexity of the HSI. The second stage contains a compatible classification model to enhance feature extraction and classification. Our approach is validated using the HyperBlood datasets and many evaluation methods, demonstrating superior performance compared to state-of-the-art deep learning models. It provides high accuracy (97%–100 %) for all HSIs, overcoming various difficulties and blood collection times.

Identification of Bloodstains by Species Using Extreme Learning Machine and Hyperspectral Imaging Technology.

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

Evaluation of Forensic Luminol in Detection of Blood Stains in Instruments Following Dental Treatment.

Background Saliva and blood, being biological materials with a high potential for infectious transmission in dental environments, pose significant risks to dental professionals, assistants, and patients alike. Therefore, practitioners must adopt stringent security measures to ensure patient care, considering all parties as potential carriers of microorganisms capable of causing infectious diseases. Currently, various methods of disinfection and sterilization are employed to maintain the aseptic chain effectively. Having reliable methods for detecting substances in liquids, particularly body fluids, is crucial and highly convenient. Luminol, a chemiluminescent agent widely used in forensic science for detecting minute traces of blood that are invisible to the naked eye, presents itself as a valuable tool. Blood, a major bodily fluid often present in instruments following dental procedures, underscores the importance of its detection. Hence, in this study, luminol was utilized to detect blood traces in dental instruments following dental treatment, both before and after sterilization or disinfection. Objective Blood and saliva splashes, together with highly contagious aerosols, are always a part of dental procedures. The objective of the current study is to detect traces of blood stains on face shields, surgical instruments, and endodontic files using luminol before and after sterilization. Materials and methods Sample size calculation was done with G*Power software (Version 3.1.9.4, Düsseldorf, Germany), and a total of 30 instruments were selected for the study. In the present study, a total of 30 items were collected and utilized, including 14 instruments used after implant placement, 12 endodontic files employed after root canal treatment, and four face shields utilized during these procedures. Meanwhile, a freshly prepared luminol solution was applied to these instruments, and they were viewed in a dark environment both before and after sterilization procedures. Luminescence generated by luminol was observed in the instruments, indicative of the presence of blood not visible to the naked eye. Statistical analysis for both groups was done with IBM SPSS Statistics for Windows, Version 16.0 (Released 2007; SPSS Inc., Chicago, IL, USA). Intragroup comparison was done using the Friedman test, and intergroup comparison was done using the Wilcoxon signed-rank test. Results Blood stains and chemiluminescence were visualized in two out of 10 endodontic files (one #15 K-file and #20 K-files) and two out of four face shields. The intragroup comparison was done using the Friedman test, and it was found to be statistically significant (p < 0.05). Intergroup comparison was done using the Wilcoxon signed-rank test and was found to be statistically insignificant (p > 0.05). Conclusion Following sterilization and disinfection, there were no visual blood stains or chemiluminescence. Therefore, luminol was found to be effective in detecting blood stains in endodontic files, surgical instruments, and face shields, as well as in validating the sterilization and disinfection processes. Hence, sterilization in dentistry stands as a critical measure to guarantee patient safety, halt the dissemination of infections, and uphold exemplary clinical care standards.

Switch-on near infrared emission in albumin behind dark fabric: toward application in forensic latent bloodstain detection.

Latent bloodstain detection remains imperative for crime scene investigators. Widely used luminol offers high sensitivity to human blood, but can produce untrustworthy results from a bleach-cleaned crime scene or in a room not dark enough. Furthermore, dark pigments impede imaging bloodstains covered by dark materials with previously reported bloodstain detection agents. A novel on/off human albumin-sensing dye (SO3C7) is reported herein with a longer emission wavelength (942 nm) than previous materials that allows imaging behind ∼5 mm of black fabric. The switch-on emission of SO3C7 is selective and sensitive to human albumin and lasts longer than luminol (24-48 hours). Emission studies, transient absorption spectra (TAS), and near-infrared (NIR) photographs herein describe the albumin sensing properties of the dye.

Stand‐off Raman spectroscopy is a promising approach for the detection and identification of bloodstains for forensic purposes

AbstractStand‐off Raman spectroscopy combined with chemometrics was demonstrated to have great potential for detecting and identifying biological stains from a distance for forensic purposes. Bloodstain spectra were collected using a handheld Raman spectrometer equipped with a stand‐off attachment and compared with spectra acquired with a benchtop Raman microscope. The two sets of spectra were visually similar, supporting the potential use of a handheld Raman spectrometer with stand‐off attachment for blood detection at the scene of crime. In addition, the stand‐off Raman spectra of bloodstains were correctly identified using a previously built SVM‐DA model for the identification of all main body fluids (Muro et al., Forensic Chemistry, 2016). Stand‐off Raman spectroscopy opens a new potential opportunity for further development of Raman spectroscopy as a universal tool for detecting and identifying body fluid traces for forensic purposes.

Discrimination of human and animal bloodstains using hyperspectral imaging

Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.

Evaluation of the detectability of different ages of bloodstains on fabrics in different washing conditions and at various wavelengths

PurposeThe detection of bloodstains at crime scenes is extremely useful in forensic investigations. This study aimed to investigate the effects of washing temperature, fabric type, fabric color, and stain age (time from staining to laundering) on the detection and identification of bloodstains on fabrics after washing. Material and methodA total of 240 fabrics (4 different colors and 5 different types) were stained with blood and washed in 4 different washing temperatures with 3 different lag times. The evaluations of fabric images were performed using the FLS system (Forenscope-Mobile Multispectral UV-VIS-IR Imaging Systems®) on a total of 1200 images using 5 different wavelengths and filter options. The bloodstained areas of the fabric pieces were then excised, and the hemoglobin presence was analyzed using the SERATEC® HemDirect hemoglobin test. ResultsThe analyses of laundered samples using the FLS system revealed that the best images were obtained from velvet, cotton fleece, denim, and polyester fabrics, in that order. Except for polyester fabrics, the SERATEC® HemDirect hemoglobin screening test, which was used to detect bloodstains on fabrics, showed positive results after washing at low temperatures (approximately 15 °C and 30 °C). At higher temperatures (60 °C and 90 °C), the SERATEC® HemDirect hemoglobin test yielded negative results. ConclusionThe fabric type and color played a crucial role in stain detection using the FLS system on the laundered fabrics. The FLS system and the SERATEC® HemDirect hemoglobin test revealed that stain age had a limited effect on the stain's detectability.

Latent bloodstain detection using a selective turn-on NIR fluorescence dye responsive to serum albumin.

Bloodstain detection can provide crucial information and evidence at a crime scene; however, the ability to selectively detect bloodstains in a non-destructive manner with high sensitivity and low background is limited. This study reports a fluorescent dye (sulfonate indolizine squaraine, SO3SQ) for bloodstain visualization under near-infrared (NIR) irradiation. While the dye itself is minimally fluorescent in aqueous solution, it exhibits a "turn-on" mechanism upon binding with human serum albumin (HSA) as the fluorescence intensity increases over 160 times with strong absorption and emission at 693 nm and 758 nm, respectively. Bloodstains can be visualized on a surface even after being diluted 1000 times, and washed latent bloodstains can be detected with high sensitivity. Further, the turn-on fluorescent emission lasts for a minimum of seven days, allowing adequate time for detection. This study also indicates that the SO3SQ can sensitively detect bloodstain after the bloodstain aged for one week. Furthermore, the detection of latent blood fingerprint patterns from colorful backgrounds is demonstrated using this non-destructive method. The selective turn-on fluorescent dye with NIR excitation and emission is highly suitable in forensic science for bloodstain visualization.

Age estimation of bloodstains based on convolutional neural network algorithm and hyperspectral imaging technology.

The most common trace of evidence at a crime scene is blood. In judicial scientific proof, it is regarded as one of the most important material types of evidence. In this study, the spectral data of the bloodstain samples with different time were collected via a hyperspectral imaging system with a spectral range of 400-1000 nm and spectral sampling interval of 5 nm. The training model of blood aging was built using the Convolutional Neural Network (CNN) algorithm and the above hyperspectral spectral data. It was also compared with the traditional partial least squares (PLS) and extreme learning machine (ELM) models. The experimental results showed that the performance of CNN model was the best, with a determination coefficient (R2) of 0.987, which is higher compared to that of the PLS model (R2 = 0.883) and ELM model (R2 = 0.936). Besides, the root mean square error of prediction (RMSEP) of the CNN model was 6.949 hours, smaller than the PLS model (RMSEP = 18.752 hours) and the ELM model (RMSEP = 13.717 hours). The mean absolute percentage error (MAPE) of the prediction set was 0.49% when using the CNN algorithm, which was also the minimum of the three algorithms, and it represented that the prediction results were the best. The experimental data showed that the method proposed in this study could accurately estimate the age of bloodstains, providing a new technology reference for bloodstain detection.

Identification of human and non-human bloodstains on rough carriers based on ATR-FTIR and chemometrics

In criminal cases, bloodstains are the most common body fluid spots. It is very important to identify them quickly. However, there are few reports on establishing a fast identification and prediction model for the direct detection of bloodstains on rough carriers. In this study, we used Fourier Transform infrared spectroscopy (FTIR) and chemometrics to establish a prediction model for rapid identification of bloodstains on rough carriers. The results of principal component analysis (PCA) showed that the difference of human and non-human bloodstains between cotton and napkin carrier groups was mainly related to protein changes. Then partial least squares discriminant analysis (PLS-DA) was used to evaluate the classification ability of training data sets and test data sets.Cotton group were 0.921 and 0.947 (sensitivity and specificity), respectively.Napkin group were 0.872 and 0.912 (sensitivity and specificity), respectively.The classification results of test data set were 0.867 and 0.944 (sensitivity and specificity) in cotton group, respectively.Napkin group were 0.767 and 0.963 (sensitivity and specificity), respectively.In addition, the classification model of cattle and sheep on rough carrier was constructed, and good classification ability was obtained.In conclusion, FTIR has the advantages of nondestructive, rapid, objective and strong identification ability.The analysis of forensic cases under actual natural conditions has great potential.

Detection of blood stains using computer vision-based algorithms and their association with postoperative outcomes in thoracoscopic lobectomies.

Our goal was to develop high throughput computer vision (CV) algorithms to detect blood stains in thoracoscopic surgery and to determine how the detected blood stains are associated with postoperative outcomes. Blood pixels in surgical videos were identified by CV algorithms trained with thousands of blood and non-blood pixels randomly selected and manually labelled. The proportion of blood pixels (PBP) was computed for key video frames to summarize the blood stain information during surgery. Statistical regression analyses were utilized to investigate the potential association between PBP and postoperative outcomes, including drainage volume, prolonged tube indwelling duration (≥5 days) and bleeding volume. A total of 275 patients undergoing thoracoscopic lobectomy were enrolled. The sum of PBP after flushing (P &lt; 0.022), age (P = 0.005), immediate postoperative air leakage (P &lt; 0.001), surgical duration (P = 0.001) and intraoperative bleeding volume (P = 0.033) were significantly associated with drainage volume in multivariable linear regression analysis. After adjustment using binary logistic regression analysis, the sum of the PBP after flushing [P = 0.017, odds ratio 1.003, 95% confidence interval (CI) 1.000-1.005] and immediate postoperative air leakage (P &lt; 0.001, odds ratio 4.616, 95% CI 1.964-10.847) were independent predictors of prolonged tube indwelling duration. In the multivariable linear regression analysis, surgical duration (P &lt; 0.001) and the sum of the PBP of the surgery (P = 0.005) were significantly correlated with intraoperative bleeding volume. This is the first study on the correlation between CV and postoperative outcomes in thoracoscopic surgery. CV algorithms can effectively detect from surgical videos information that has good prediction power for postoperative outcomes.

Bloodstain Identification Based on Visible/Near-Infrared Hyperspectral Imaging With Convolutional Neural Network

Blood samples are easily damaged in traditional bloodstain detection and identification. In complex scenes with interfering objects, bloodstain identification may be inaccurate, with low detection rates and false-positive results. In order to meet these challenges, we propose a bloodstain detection and identification method based on hyperspectral imaging and mixed convolutional neural networks, which enables fast and efficient non-destructive identification of bloodstains. In this study, we apply visible/near-infrared reflectance hyperspectral imaging in the 380-1000 nm spectral region to analyze the shape, structure, and biochemical characteristics of bloodstains. Hyperspectral images of bloodstains on different substrates and six bloodstain analogs are experimentally obtained. The acquired spectral pixels are pre-processed by Principal Component Analysis (PCA). For bloodstains and different bloodstain analogs, regions of interest are selected from each substance to obtain pixels, which are further used in convolutional neural network (CNN) modeling. After the mixed CNN modeling is completed, pixels are selected from the hyperspectral images as a test set for bloodstains and bloodstain analogs. Finally, the bloodstain recognition ability of the mixed 2D-3D CNN model is evaluated by analyzing the kappa coefficient and classification accuracy. The experimental results show that the accuracy of the constructed CNN bloodstain identification model reaches 95.4%. Compared with other methods, the bloodstain identification method proposed in this study has higher efficiency and accuracy in complex scenes. The results of this study will provide a reference for the future development of the bloodstain online detection system.

Evaluation of infrared photography for latent bloodstain visualization and the influence of time

The localization of latent blood traces at crime scenes is generally performed using fluorescent stains although infrared light has previously been recognized as an effective localization test for bloodstains as it is a non-destructive and non-contact technique. The goal of this study was to evaluate infrared photography for the detection of latent bloodstains on a large number of objects with different compositions frequently encountered at crime scenes. In this study we show that infrared light photography was able to detect bloodstains deposited on 71.7% of materials while bloodstains on 29.2% of materials could only be detected using infrared photography and not through visual photography. Bloodstain could be detected on most fabrics composed of 100% polyester, 100% cotton and 100% wool or a combination of these fibers with other types of fiber such as nylon or viscose. For other materials such as leather, tiles, wood, bricks, parquet, infrared did not improve the visibility of the bloodstains. Finally, the influence of the time of bloodstain deposition was tested over the period of 1 week and 1 month and did not reveal major differences compared to stains after 24 h drying time suggesting that time has little influence on the ability of infrared light and visual light to detect latent bloodstains.

Finding blood in the dark: A comparison of infrared imaging devices for the detection of bloodstains on dark fabrics based on their resolution

The use of infrared (IR) light to locate bloodstains on dark fabric is a search technique that is employed in forensic examinations in a number of organisations worldwide. IR is used to complement existing, established visual white light search techniques. There exist a variety of commercially available products that can be purchased for this purpose as well as the option of using IR-converted standard DSLR (Digital Single Lens Reflex) cameras. In this study, a number of IR systems with contrasting resolutions were explored and their performance was assessed on a variety of bloodstain types and fabrics in comparison with white light. The systems ranged from low-budget, low resolution options, such as portable webcams, to vision-industry standard, high resolution, purpose-built cameras for more detailed blood searching of suitable items in the laboratory. Blood spatter, transfer bloodstains, dilute bloodstains, blood mixed with other body fluids and environmental contaminants were among the samples tested on eight different dark fabric types under IR conditions to assess the impact of the resolution differences. All IR systems were able to locate bloodstains, with significantly more bloodstains being found with IR compared to white light. The higher resolution systems were able to locate significantly more bloodstains than the systems with the lower resolution. The webcams were able to locate many of the larger areas of bloodstaining but performed less well in terms of locating smaller bloodstains and dilute blood. False positives such as mud, make-up and brown sauce were detected under IR but were readily discriminated under white light and with presumptive chemical tests. The balance between the ability to locate bloodstains based on system resolution and practicality and possible efficiency gains is discussed.

Development of New Chemiluminescence Systems for Analysis

Chemiluminescence (CL) is a light-emitting phenomenon as a result of chemical reactions. Luminol and lucigenin are typical CL luminophores, and hydrogen peroxide is typical coreactant. It is an important analytical technique because of its inherent features, such as high sensitivity, wide dynamic range, fast detection, simple equipment, and low instrumentation costs because no external light source or optics is needed. It has been extensively used for immunoassays, DNA detection, enzymatic biosensors, forensic bloodstain detection, pharmaceutical analysis, metal ion determination, imaging and so on. To improve performance of CL analysis and broaden the applications of CL, it is of great importance to develop new CL systems.Herein, we present some new CL systems such as artemisinin/luminol, thiourea dioxide/luminol, N-hydroxyphthalimide/luminol, hydroxylamine-O-sulfonic acid/luminol, allantoin/ lucigenin and creatinine/H2O2 systems. We will also demonstrate their applications in forensic bloodstain detection, enzyme detection, metal ions detection, and so on [1-3].AcknowledgmentWe are grateful for financial support from the National Key Research and Development Program of China (No. 2016YFA0201300), National Natural Science Foundation of China (Nos. 21874126,21675148), the Chinese Academy of Sciences (CAS)-the Academy of Sciences for the Developing World (TWAS), President’s Fellowship Program, and the CAS President's International Fellowship Initiative (PIFI) project.References(1) Wenyue Gao, Chao Wang, Kateryna Muzyka, Shimeles Addisu Kitte, Jianping Li, Wei Zhang and Guobao Xu*. Artemisinin-luminol chemiluminescence for forensic bloodstain detection using smart phone as detector. Anal. Chem., 2017, 89 (11), 6160–6165.(2) Wenyue Gao, Wenjing Qi, Jianping Lai, Liming Qi, Saadat Majeed and Guobao Xu*. Thiourea dioxide as unique eco-friendly coreactant of luminol chemiluminescence for sensitive detection of luminol, thiourea dioxide, and cobalt ions. Chem. Commun., 2015, 51(9), 1620–1623.(3) Muhammad Saqib, Liming Qi, Pan Hui, Anaclet Nsabimana, Mohamed Ibrahim Halawa, Wei Zhang,* Guobao Xu*. Development of luminol-N-hydroxyphthalimide chemiluminescence system for highly selective and sensitive detection of superoxide dismutase, uric acid and Co2+. Biosens. Bioelectron., 2018, 99, 519-524.

应用于血迹检测的高光谱成像技术研究综述

应用于血迹检测的高光谱成像技术研究综述

Degrees of contrast: Detection of latent bloodstains on fabric using an alternate light source (ALS) and the effects of washing.

Blood is often a piece of evidence of violent crimes and will often be on the perpetrator's clothing. If the perpetrator is wearing dark clothing, it can be easily identified by chemical means such as Bluestar®, but this can destroy the pattern, which may be evidentiary itself. This study explores the use of alternate light source (ALS) to photograph bloodstains on dark and/or patterned fabrics to provide an alternate, noninvasive tool before the use of chemical detection techniques. Sixty-nine (69) unwashed fabrics, of various dark colored and dark patterns, were photographed in monochrome under ambient light and subsequently with and without a filter under ultraviolet (UV), violet, blue, green, and infrared light. This study used ImageJ to measure the contrast between the bloodstain and the fabric and thus the effectiveness of each wavelength. Each fabric was washed, photographed, and analyzed five times or until the bloodstain was no longer visible under ALS. Results indicated photography with ALS was a viable method for blood detection on fabrics and should be used prior to chemical means. Further, infrared, followed by violet light with no filter, was the most effective light source for viewing bloodstains on dark fabrics without the use of chemicals. However, these wavelengths were not effective on military uniforms. This study also described one effect fabric manufacturer chemical treatments have on bloodstains and the effect of washing fabrics with bloodstains.

Development of New Chemiluminescence Systems for Analysis

Chemiluminescence (CL) is a light-emitting phenomenon as a result of chemical reactions. Luminol and lucigenin are typical CL luminophores, and hydrogen peroxide is typical coreactant. It is an important analytical technique because of its inherent features, such as high sensitivity, wide dynamic range, fast detection, simple equipment, and low instrumentation costs because no external light source or optics is needed. It has been extensively used for immunoassays, DNA detection, enzymatic biosensors, forensic bloodstain detection, pharmaceutical analysis, metal ion determination, imaging and so on. To improve performance of CL analysis and broaden the applications of CL, it is of great importance to develop new CL systems.Herein, we present some new CL systems such as artemisinin/luminol, thiourea dioxide/luminol, N-hydroxyphthalimide/luminol, hydroxylamine-O-sulfonic acid/luminol, allantoin/ lucigenin and creatinine/H2O2 systems. We will also demonstrate their applications in forensic bloodstain detection, enzyme detection, metal ions detection, and so on [1-7].AcknowledgmentWe are grateful for financial support from the National Key Research and Development Program of China (No. 2016YFA0201300), National Natural Science Foundation of China (Nos. 21874126,21675148), the Chinese Academy of Sciences (CAS)-the Academy of Sciences for the Developing World (TWAS), President’s Fellowship Program, and the CAS President's International Fellowship Initiative (PIFI) project.

Detection of latent bloodstains at fire scenes using reflected infrared photography

Bloodstain evidence is an element of crime scene investigation often found at scenes involving violence. Setting fire to the scene is a method sometimes used by offenders of crime in an attempt to conceal evidence. Fire often produces thick soot as a by-product of the combustion and has the potential to cover bloodstain patterns rendering them latent. There is limited published material offering a method of detecting bloodstains hidden beneath dense soot deposits caused by fire. This project employed a modified digital single-lens reflex (SLR) camera to investigate the application of reflected infrared photography to detect latent bloodstain evidence beneath varying deposited overlaying soot densities. The potential of this technique was examined by photographing blood samples beneath soot from a scaled fire simulation. A qualitative evaluation was completed by comparing images taken of a series of samples using both reflected infrared and standard visible light photography and corroborated with quantitative image analysis to support the findings. Results indicate that infrared photography can reveal latent bloodstains beneath a dense layer of soot in excess of ρ2.3 (550nm) density with substantial clarity. The success of this technique is dependent on specific optical and specimen parameters. These parameters include (i) the reflective properties of the background surface, (ii) the spectral absorption properties of blood and (iii) the ability of infrared wavelengths to transmit through the soot layer. Reflected infrared photography may provide crime scene examiners with a specialised field recording method that is easily executed and non-destructive to assist in visualising and locating latent bloodstain patterns beneath dense layers of soot.

A comparison study of the detection of bloodstains on painted and cleaned surfaces with luminol

There seems to be a limited amount of research about the detection of concealed bloodstains on painted surfaces. The bloodstains on walls and floors are often removed by cleaning, in some cases the surfaces are painted by the perpetrator after committing a violent crime in order to hide the crime that has occurred. The study hereafter extends and deepens on previous researches by investigating the detectability of horse bloodstains on painted ceramic tiles as a function of the number of layers of paint. In this study luminol was used as a reagent to detect the bloodstains. The study focuses on two types of paints: water based and solvent based paint. This study also investigates the effectiveness in reducing the detectability of bloodstains on ceramic tiles using four different cleaning methods pure water, soap with water, wet wipes, and bleach. In the experiment the bloodstains were cleaned at various intervals of time after the deposition (two minutes, fifteen minutes and one hour). The study concluded that the bloodstains concealed by layers of solvent based paint are less likely to be detected by luminol compared to water based paint. The study also concluded that the tiles cleaned with bleach are recognisable from the other ones cleaned using other methods. In each study the duration of the reaction was timed, highlighting the differences in the cleaning methods.