Aidan P. Holman BSc, Davis N. Pickett HSD, Abigail E. Orr, Aaron M. Tarone PhD, Dmitry Kurouski PhD
{"title":"一种无损技术,用于鉴定鞘氨醇 macellaria 三龄幼虫的性别。","authors":"Aidan P. Holman BSc, Davis N. Pickett HSD, Abigail E. Orr, Aaron M. Tarone PhD, Dmitry Kurouski PhD","doi":"10.1111/1556-4029.15619","DOIUrl":null,"url":null,"abstract":"<p>Forensic entomology plays an important role in medicolegal investigations by using insects, primarily flies, to estimate the time of colonization. This estimation relies on the development of the flies found at the (death) scene and can be affected (and sometimes corrected) by external factors, such as temperature and humidity, and internal factors, such as species and sex. This study leverages infrared (IR) spectroscopy combined with machine learning models—Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting trees Discriminant Analysis (XGBDA)—to differentiate between male and female <i>Cochliomyia macellaria</i> larvae, commonly found on human remains. Significant vibrational differences were detected in the infrared spectra of third instar <i>C. macellaria</i> larvae, with distinct peaks showing variations in relative absorbance between sexes, suggesting differences in biochemical compositions such as cuticular proteins and lipids. The application of PLS-DA and XGBDA yielded high classification accuracies of about 94% and 96%, respectively, with female spectra consistently having higher sensitivity than males. This non-destructive approach offers the potential to refine supplemental post-mortem interval estimations significantly, enhancing the accuracy of forensic analyses.</p>","PeriodicalId":15743,"journal":{"name":"Journal of forensic sciences","volume":"69 6","pages":"2075-2081"},"PeriodicalIF":1.5000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A nondestructive technique for the sex identification of third instar Cochliomyia macellaria larvae\",\"authors\":\"Aidan P. Holman BSc, Davis N. Pickett HSD, Abigail E. Orr, Aaron M. Tarone PhD, Dmitry Kurouski PhD\",\"doi\":\"10.1111/1556-4029.15619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Forensic entomology plays an important role in medicolegal investigations by using insects, primarily flies, to estimate the time of colonization. This estimation relies on the development of the flies found at the (death) scene and can be affected (and sometimes corrected) by external factors, such as temperature and humidity, and internal factors, such as species and sex. This study leverages infrared (IR) spectroscopy combined with machine learning models—Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting trees Discriminant Analysis (XGBDA)—to differentiate between male and female <i>Cochliomyia macellaria</i> larvae, commonly found on human remains. Significant vibrational differences were detected in the infrared spectra of third instar <i>C. macellaria</i> larvae, with distinct peaks showing variations in relative absorbance between sexes, suggesting differences in biochemical compositions such as cuticular proteins and lipids. The application of PLS-DA and XGBDA yielded high classification accuracies of about 94% and 96%, respectively, with female spectra consistently having higher sensitivity than males. This non-destructive approach offers the potential to refine supplemental post-mortem interval estimations significantly, enhancing the accuracy of forensic analyses.</p>\",\"PeriodicalId\":15743,\"journal\":{\"name\":\"Journal of forensic sciences\",\"volume\":\"69 6\",\"pages\":\"2075-2081\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of forensic sciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.15619\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of forensic sciences","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.15619","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
A nondestructive technique for the sex identification of third instar Cochliomyia macellaria larvae
Forensic entomology plays an important role in medicolegal investigations by using insects, primarily flies, to estimate the time of colonization. This estimation relies on the development of the flies found at the (death) scene and can be affected (and sometimes corrected) by external factors, such as temperature and humidity, and internal factors, such as species and sex. This study leverages infrared (IR) spectroscopy combined with machine learning models—Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting trees Discriminant Analysis (XGBDA)—to differentiate between male and female Cochliomyia macellaria larvae, commonly found on human remains. Significant vibrational differences were detected in the infrared spectra of third instar C. macellaria larvae, with distinct peaks showing variations in relative absorbance between sexes, suggesting differences in biochemical compositions such as cuticular proteins and lipids. The application of PLS-DA and XGBDA yielded high classification accuracies of about 94% and 96%, respectively, with female spectra consistently having higher sensitivity than males. This non-destructive approach offers the potential to refine supplemental post-mortem interval estimations significantly, enhancing the accuracy of forensic analyses.
期刊介绍:
The Journal of Forensic Sciences (JFS) is the official publication of the American Academy of Forensic Sciences (AAFS). It is devoted to the publication of original investigations, observations, scholarly inquiries and reviews in various branches of the forensic sciences. These include anthropology, criminalistics, digital and multimedia sciences, engineering and applied sciences, pathology/biology, psychiatry and behavioral science, jurisprudence, odontology, questioned documents, and toxicology. Similar submissions dealing with forensic aspects of other sciences and the social sciences are also accepted, as are submissions dealing with scientifically sound emerging science disciplines. The content and/or views expressed in the JFS are not necessarily those of the AAFS, the JFS Editorial Board, the organizations with which authors are affiliated, or the publisher of JFS. All manuscript submissions are double-blind peer-reviewed.