Measuring haemolysis in cattle serum by direct UV–VIS and RGB digital image-based methods

Simundic, A. M., Baird, G., Cadamuro, J., Costelloe, S. J. & Lippi, G. Managing hemolyzed samples in clinical laboratories. Crit. Rev. Clin. Lab. Sci. 57, 1–21 (2020).
Google Scholar
Lippi, G., Cadamuro, J., Von Meyer, A. & Simundic, A. M. Practical recommendations for managing hemolyzed samples in clinical chemistry testing. Clin. Chem. Lab. Med. 56, 718–727 (2018).
Google Scholar
Braun, J. P., Bourgès-Abella, N., Geffré, A., Concordet, D. & Trumel, C. The preanalytic phase in veterinary clinical pathology. Vet. Clin. Pathol. 44, 8–25 (2015).
Google Scholar
Šimundić, A. M., Nikolac, N. & Guder, W. G. Preanalytical variation and preexamination processes. In Tietz Textbook of Clinical Chemistry and Molecular Diagnostics (eds Rifai, N. et al.) 81–94 (Elsevier, 2018).
Di Martino, G. et al. The degree of acceptability of swine blood values at increasing levels of hemolysis evaluated through visual inspection versus automated quantification. J. Vet. Diagn. Investig. 27, 306–312 (2015).
Google Scholar
Jacobs, R. M., Lumsden, J. H. & Grift, E. Effects of bilirubinemia, hemolysis, and lipemia on clinical chemistry analytes in bovine, canine, equine, and feline sera. Can. Vet. J. 33, 605–608 (1992).
Google Scholar
Killilea, D. W. et al. Identification of a hemolysis threshold that increases plasma and serum zinc concentration. J. Nutr. 147, 1218–1225 (2017).
Google Scholar
Koseoglu, M., Hur, A., Atay, A. & Çuhadar, S. Effects of hemolysis interferences on routine biochemistry parameters. Biochem. Medica 21, 79–85 (2011).
Google Scholar
Leard, B. L., Alsaker, R. D., Porter, W. P. & Sobel, L. P. The effect of haemolysis on certain canine serum chemistry parameters. Lab. Anim. 24, 32–35 (1990).
Google Scholar
O’Neill, S. L. & Feldman, B. F. Hemolysis as a factor in clinical chemistry and hematology of the dog. Vet. Clin. Pathol. 18, 58–68 (1989).
Google Scholar
Larrán, B. et al. Influence of haemolysis on the mineral profile of cattle serum. Anim. 11, 3336 (2021).
Google Scholar
Sodi, R., Darn, S. M., Davison, A. S., Stott, A. & Shenkin, A. Mechanism of interference by haemolysis in the cardiac troponin T immunoassay. Ann. Clin. Biochem. 43, 49–56 (2006).
Google Scholar
Nougier, C., Jousselme, E., Sobas, F., Pousseur, V. & Négrier, C. Effects of hemolysis, bilirubin, and lipemia interference on coagulation tests detected by two analytical systems. Int. J. Lab. Hematol. 42, 88–94 (2020).
Google Scholar
Hawkins, R. Discrepancy between visual and spectrophotometric assessment of sample haemolysis. Ann. Clin. Biochem. 42, 521–522 (2002).
Google Scholar
Simundic, A. M. et al. Comparison of visual vs. automated detection of lipemic, icteric and hemolyzed specimens: Can we rely on a human eye?. Clin. Chem. Lab. Med. 47, 1361–1365 (2009).
Google Scholar
Luksic, A. H. et al. Visual assessment of hemolysis affects patient safety. Clin. Chem. Lab. Med. 56, 574–581 (2018).
Google Scholar
Lippi, G. & Cadamuro, J. Visual assessment of sample quality: Quo usque tandem?. Clin. Chem. Lab. Med. 56, 513–515 (2018).
Google Scholar
Gómez Rioja, R. et al. Hemólisis en las muestras para diagnóstico. Rev. del Lab. Clin. 2, 185–195 (2009).
Lippi, G. Systematic assessment of the Hemolysis index: Pros and cons. Adv. Clin. Chem. 71, 157–170 (2015).
Google Scholar
Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A02010L0063-20190626 (2010).
Real Decreto 53/2013, de 1 de febrero, por el que se establecen las normas básicas aplicables para la protección de los animales utilizados en experimentación y otros fines científicos, incluyendo la docencia. https://www.boe.es/buscar/act.php?id=BOE-A-2013-1337 (2013).
Jerry Kaneko, J., Harvey, J. J. & Bruss, M. L. Clinical Biochemistry of Domestic Animals (Academic Press, 2008).
Herrero-Latorre, C., Barciela-García, J., García-Martín, S. & Peña-Crecente, R. M. Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques. Food Chem. X 3, 100046 (2019).
Google Scholar
Geladi, P. & Kowalski, B. R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 185, 1–17 (1986).
Google Scholar
Zupan, J. & Gasteiger, J. Neural Networks for Chemistry and Drug Design: An Introduction (Wiley-VCH, 1999).
Vabalas, A. Machine learning algorithm validation with a limited sample size. PLoS ONE 14, e0224365 (2019).
Google Scholar
International Committee for Standardization in Haematology. Br. J. Haematol. 13, 71–75 (1967).
Google Scholar
van Kampen, E. J. & Zijlstra, W. G. Standardization of hemoglobinometry II. The hemiglobincyanide method. Clin. Chim. Acta 6, 538–544 (1961).
Google Scholar
Srivastava, T., Negandhi, H., Neogi, S. B., Sharma, J. & Saxena, R. Methods for hemoglobin estimation: A review of “what works”. J. Hematol. Transfus. 2, 2005–2006 (2014).
Smith, M. B. et al. Hemolysis, icterus and lipemia/turbidity indices as indicators of interference in clinical laboratory analysis. Clin. Lab. Stand. Inst. 32, 1–35 (2012).
Malinauskas, R. A. Plasma hemoglobin measurement techniques for the in vitro evaluation of blood damage caused by medical devices. Artif. Organs 21, 1255–1267 (1997).
Google Scholar
Roggan, A., Friebel, M., Dörschel, K., Hahn, A. & Müller, G. Optical properties of circulating human blood in the wavelength range 400–2500 nm. J. Biomed. Opt. 4, 36–46 (1999).
Google Scholar
Taparia, N., Platten, K. C., Anderson, K. B. & Sniadecki, N. J. A microfluidic approach for hemoglobin detection in whole blood. AIP Adv. 7, 105102 (2017).
Google Scholar
Merchant, M., Hammack, T., Sanders, P. & Dronette, J. Rapid and inexpensive method for the spectroscopic determination of innate immune activity of crocodilians. Spectrosc. Lett. 39, 337–343 (2006).
Google Scholar
Vieira, T. O. & Diré, G. F. Spectrophotometric and hemolytic analysis of an extract of Costus spicatus. EJBMSR 3, 29–58 (2015).
Shah, J. S., Soon, P. S. & Marsh, D. J. Comparison of methodologies to detect low levels of hemolysis in serum for accurate assessment of serum microRNAs. PLoS ONE 11, e0153200 (2016).
Google Scholar
Liu, P., Zhu, Z., Zeng, C. & Nie, G. Specific absorption spectra of hemoglobin at different PO2 levels: Potential noninvasive method to detect PO2 in tissues. J. Biomed. Opt. 17, 125002 (2012).
Google Scholar
Zijlstra, W. G. & Buursma, A. Spectrophotometry of hemoglobin: Absorption spectra of bovine oxyhemoglobin, deoxyhemoglobin, carboxyhemoglobin, and methemoglobin. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 118, 743–749 (1997).
Google Scholar
Vallejos, S. et al. Solid sensory polymer substrates for the quantification of iron in blood, wine and water by a scalable RGB technique. J. Mater. Chem. A 1, 15435–15441 (2013).
Google Scholar
Andrew, W., Hannuna, S., Campbell, N. & Burghardt, T. Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. In Proceedings of the IEEE International Conference on Image Processing (ICIP) vols 2016-August 484–488 (IEEE Computer Society, 2016).
Bhole, A., Falzon, O., Biehl, M. & Azzopardi, G. A computer vision pipeline that uses thermal and RGB images for the recognition of Holstein cattle. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11679 108–119 (Springer Verlag, 2019).
Jorquera-Chavez, M. et al. Modelling and validation of computer vision techniques to assess heart rate, eye temperature, ear-base temperature and respiration rate in cattle. Animals 9, 1089 (2019).
Google Scholar
Philipsen, M. P., Dueholm, J. V., Jørgensen, A., Escalera, S. & Moeslund, T. B. Organ segmentation in poultry viscera using RGB-D. Sensors (Switzerland) 18, 117 (2018).
Google Scholar
Lee, K., Baek, S., Kim, D. & Seo, J. A freshness indicator for monitoring chicken-breast spoilage using a Tyvek® sheet and RGB color analysis. Food Packag. Shelf Life 19, 40–46 (2019).
Google Scholar
Tyburski, E. A. et al. Disposable platform provides visual and color-based point-of-care anemia self-testing. J. Clin. Invest. 124, 4387–4394 (2014).
Google Scholar
Edwards, P. et al. Smartphone based optical spectrometer for diffusive reflectance spectroscopic measurement of hemoglobin. Sci. Rep. 7(1), 12224 https://doi.org/10.1038/s41598-017-12482-5 (2017).
Google Scholar
Collings, S. et al. Non-invasive detection of anaemia using digital photographs of the conjunctiva. PLoS ONE 11, e0153286 (2016).
Google Scholar
Hasan, M. K., Sakib, N., Love, R. R. & Ahamed, S. I. RGB pixel analysis of fingertip video image captured from sickle cell patient with low and high level of hemoglobin in 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017 Vol. 2018 499–505 (Institute of Electrical and Electronics Engineers Inc., 2017).
Noriega, L. M., Rojas, P. W. & Silva, A. S. Hemoglobin screening using cloud-based mobile photography applications. Ing. y Univ. 23(2), 1–22 https://doi.org/10.11144/Javeriana.iyu23-2.hsuc (2019).
Google Scholar
Jayakody, J. A. D. C. A., Edirisinghe, E. A. G. A. & Lokuliyana, S. HemoSmart: A non‐invasive device and mobile app for anemia detection. In Cognitive Engineering for Next Generation Computing 93–119 (Wiley, 2021). doi:https://doi.org/10.1002/9781119711308.ch3
Rajmanova, P., Vala, D. & Slanina, Z. Detection of haemolytic transfusion bagsin IFAC Proceedings Volumes (IFAC-PapersOnline) vol. 12 400–404 (IFAC Secretariat, 2013).
Archibong, E., Konnaiyan, K. R., Kaplan, H. & Pyayt, A. A mobile phone-based approach to detection of hemolysis. Biosens. Bioelectron. 88, 204–209 (2017).
Google Scholar
Lopes, K. M. et al. Portable device for measuring blood test hemolyzed samples based on computer vision and neural network. J. Med. Devices Trans. ASME 13, 1–25 (2019).
Kim, T. et al. Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology. J. Biomed. Opt. 21, 107001 (2016).
Google Scholar