A New Strategy for the Morphological and Colorimetric Recognition of Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color Images of Blood Smears

The detection of red blood cells based on morphology and colorimetric appearance is very important in improving hematology diagnostics. There are automatons capable of detecting certain forms, but these have limitations with regard to the formal identification of red blood cells because they consider certain cells to be red blood cells when they are not and vice versa. Other automata have limitations in their operation because they do not cover a sufficient area of the blood smear. In spite of their performance, biologists have very often resorted to the manual analysis of blood smears under an optical microscope for a morphological and colorimetric study. In this paper, we present a new strategy for semi-automatic identification of red blood cells based on their isolation, their automatic color segmentation using Otsu's algorithm and their morphology. The algorithms of our method have been implemented in the programming environment of the scientific software MATLAB resulting in an artificial intelligence application. The application, once launched, allows the biologist to select a region of interest containing the erythrocyte to be characterized, then a set of attributes are computed extracted from this target red blood cell. These attributes include compactness, perimeter, area, morphology, white and red proportions of the erythrocyte, etc. The types of anemia treated in this work concern the iron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms. The results obtained are excellent because they highlight different forms of anemia contracted in a patient.Comment: ISIS

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products

Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods

[EN] Cocoa (Theobroma cacao L.) and its derivatives are appreciated for their aroma, color, and healthy properties, and are commodities of high economic value worldwide. Wide ranges of conventional methods have been used for years to guarantee cocoa quality. Recently, however, demand for global cocoa and the requirements of sensory, functional, and safety cocoa attributes have changed. On the one hand, society and health authorities are increasingly demanding new more accurate quality control tests, including not only the analysis of physicochemical and sensory parameters, but also determinations of functional compounds and contaminants (some of which come in trace quantities). On the other hand, increased production forces industries to seek quality control techniques based on fast, nondestructive online methods. Finally, an increase in global cocoa demand and a consequent rise in prices can lead to future cases of fraud. For this reason, new analytes, technologies, and ways to analyze data are being researched, developed, and implemented into research or quality laboratories to control cocoa quality and authenticity. The main advances made in destructive techniques focus on developing new and more sensitive methods such as chromatographic analysis to detect metabolites and contaminants in trace quantities. These methods are used to assess cocoa quality; study new functional properties; control cocoa authenticity; or detect frequent emerging frauds. Regarding nondestructive methods, spectroscopy is the most explored technique, which is conducted within the near infrared range, and also within the medium infrared range to a lesser extent. It is applied mainly in the postharvest stage of cocoa beans to analyze different biochemical parameters or to assess the authenticity of cocoa and its derivatives.The authors wish to acknowledge the financial assistance provided by the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). Maribel Quelal Vásconez thanks the Ministry Higher Education, Science, Technology, and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant.Quelal-Vásconez, MA.; Lerma-García, MJ.; Pérez-Esteve, É.; Talens Oliag, P.; Barat Baviera, JM. (2020). Roadmap of cocoa quality and authenticity control in the industry: a review of conventional and alternative methods. Comprehensive Reviews in Food Science and Food Safety. 19(2):448-478. https://doi.org/10.1111/1541-4337.12522S448478192Abdullahi, G., Muhamad, R., Dzolkhifli, O., & Sinniah, U. R. (2018). Analysis of quality retentions in cocoa beans exposed to solar heat treatment in cardboard solar heater box. Cogent Food & Agriculture, 4(1), 1483061. doi:10.1080/23311932.2018.1483061Abt, E., Fong Sam, J., Gray, P., & Robin, L. P. (2018). Cadmium and lead in cocoa powder and chocolate products in the US Market. Food Additives & Contaminants: Part B, 11(2), 92-102. doi:10.1080/19393210.2017.1420700Acierno, V., Alewijn, M., Zomer, P., & van Ruth, S. M. (2018). Making cocoa origin traceable: Fingerprints of chocolates using Flow Infusion - Electro Spray Ionization - Mass Spectrometry. Food Control, 85, 245-252. doi:10.1016/j.foodcont.2017.10.002Aculey, P. C., Snitkjaer, P., Owusu, M., Bassompiere, M., Takrama, J., Nørgaard, L., … Nielsen, D. S. (2010). Ghanaian Cocoa Bean Fermentation Characterized by Spectroscopic and Chromatographic Methods and Chemometrics. Journal of Food Science, 75(6), S300-S307. doi:10.1111/j.1750-3841.2010.01710.xAfoakwa, E. O., Paterson, A., Fowler, M., & Ryan, A. (2009). Matrix effects on flavour volatiles release in dark chocolates varying in particle size distribution and fat content using GC–mass spectrometry and GC–olfactometry. Food Chemistry, 113(1), 208-215. doi:10.1016/j.foodchem.2008.07.088Afoakwa, E. O., Quao, J., Takrama, J., Budu, A. S., & Saalia, F. K. (2011). Chemical composition and physical quality characteristics of Ghanaian cocoa beans as affected by pulp pre-conditioning and fermentation. Journal of Food Science and Technology, 50(6), 1097-1105. doi:10.1007/s13197-011-0446-5Alander, J. T., Bochko, V., Martinkauppi, B., Saranwong, S., & Mantere, T. (2013). A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety. International Journal of Spectroscopy, 2013, 1-36. doi:10.1155/2013/341402Álvarez, C., Pérez, E., Cros, E., Lares, M., Assemat, S., Boulanger, R., & Davrieux, F. (2012). The Use of near Infrared Spectroscopy to Determine the Fat, Caffeine, Theobromine and (−)-Epicatechin Contents in Unfermented and Sun-Dried Beans of Criollo Cocoa. Journal of Near Infrared Spectroscopy, 20(2), 307-315. doi:10.1255/jnirs.990Agricultural and Processed Food Products Export Development Authority (APEDA). (2015).Export statement. Retrieved fromhttp://agriexchange.apeda.gov.in/indexp/exportstatement.aspxAprotosoaie, A. C., Luca, S. V., & Miron, A. (2015). Flavor Chemistry of Cocoa and Cocoa Products-An Overview. Comprehensive Reviews in Food Science and Food Safety, 15(1), 73-91. doi:10.1111/1541-4337.12180Arévalo-Gardini, E., Arévalo-Hernández, C. O., Baligar, V. C., & He, Z. L. (2017). Heavy metal accumulation in leaves and beans of cacao (Theobroma cacao L.) in major cacao growing regions in Peru. Science of The Total Environment, 605-606, 792-800. doi:10.1016/j.scitotenv.2017.06.122Assa, A., Noor, A., Yunus, M. R., Misnawi, & Djide, M. N. (2018). Heavy metal concentrations in cocoa beans (Theobroma cacaoL.) originating from EastLuwu, South Sulawesi, Indonesia. Journal of Physics: Conference Series, 979, 012011. doi:10.1088/1742-6596/979/1/012011Barbin, D. F., Maciel, L. F., Bazoni, C. H. V., Ribeiro, M. da S., Carvalho, R. D. S., Bispo, E. da S., … Hirooka, E. Y. (2018). Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses. Journal of Food Science and Technology, 55(7), 2457-2466. doi:10.1007/s13197-018-3163-5Belo, R. F. C., Figueiredo, J. P., Nunes, C. M., Pissinatti, R., Souza, S. V. C. de, & Junqueira, R. G. (2017). Accelerated solvent extraction method for the quantification of polycyclic aromatic hydrocarbons in cocoa beans by gas chromatography–mass spectrometry. Journal of Chromatography B, 1053, 87-100. doi:10.1016/j.jchromb.2017.03.017Belščak, A., Komes, D., Horžić, D., Ganić, K. K., & Karlović, D. (2009). Comparative study of commercially available cocoa products in terms of their bioactive composition. Food Research International, 42(5-6), 707-716. doi:10.1016/j.foodres.2009.02.018Berrueta, L. A., Alonso-Salces, R. M., & Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158(1-2), 196-214. doi:10.1016/j.chroma.2007.05.024Beulens, A. J. M., Broens, D.-F., Folstar, P., & Hofstede, G. J. (2005). Food safety and transparency in food chains and networks Relationships and challenges. Food Control, 16(6), 481-486. doi:10.1016/j.foodcont.2003.10.010Bolliger, S., Zeng, Y., & Windhab, E. J. (1999). In-line measurement of tempered cocoa butter and chocolate by means of near-infrared spectroscopy. Journal of the American Oil Chemists’ Society, 76(6), 659-667. doi:10.1007/s11746-999-0157-5Bonvehí, J. S. (2005). Investigation of aromatic compounds in roasted cocoa powder. European Food Research and Technology, 221(1-2), 19-29. doi:10.1007/s00217-005-1147-yBratinova S. Karasek L. Buttinger G. &Wenzl T.(2015).Report on the 16th Interlaboratory comparison organnnsed by the European Union Reference Laboratory for Polycyclic Aromatic Hydrocarbons EUR 27558 15. EU.https://doi.org/10.2787/279750.Brera, C., Grossi, S., & Miraglia, M. (2005). Interlaboratory Study for Ochratoxin A Determination in Cocoa Powder Samples. Journal of Liquid Chromatography & Related Technologies, 28(1), 35-61. doi:10.1081/jlc-200038574Bro, R. (1997). PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems, 38(2), 149-171. doi:10.1016/s0169-7439(97)00032-4Cádiz-Gurrea, M. L., Lozano-Sanchez, J., Contreras-Gámez, M., Legeai-Mallet, L., Fernández-Arroyo, S., & Segura-Carretero, A. (2014). Isolation, comprehensive characterization and antioxidant activities of Theobroma cacao extract. Journal of Functional Foods, 10, 485-498. doi:10.1016/j.jff.2014.07.016Cambrai, A., Marcic, C., Morville, S., Sae Houer, P., Bindler, F., & Marchioni, E. (2010). Differentiation of Chocolates According to the Cocoa’s Geographical Origin Using Chemometrics. Journal of Agricultural and Food Chemistry, 58(3), 1478-1483. doi:10.1021/jf903471eCAOBISCO‐ECA‐FCC. (2015).Cocoa beans: Chocolate and cocoa industry quality requirements. Retrieved fromhttp://www.cocoaquality.eu/Caporaso, N., Whitworth, M. B., Fowler, M. S., & Fisk, I. D. (2018). Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans. Food Chemistry, 258, 343-351. doi:10.1016/j.foodchem.2018.03.039CBI. (2016).CBI trade statistics: Cocoa in Europe. Retrieved fromhttps://www.cbi.eu/sites/default/files/market_information/researches/trade-statistics-europe-cocoa-2016.pdfChavez, E., He, Z. L., Stoffella, P. J., Mylavarapu, R. S., Li, Y. C., Moyano, B., & Baligar, V. C. (2015). Concentration of cadmium in cacao beans and its relationship with soil cadmium in southern Ecuador. Science of The Total Environment, 533, 205-214. doi:10.1016/j.scitotenv.2015.06.106Chavez, E., He, Z. L., Stoffella, P. J., Mylavarapu, R. S., Li, Y. C., & Baligar, V. C. (2016). Chemical speciation of cadmium: An approach to evaluate plant-available cadmium in Ecuadorian soils under cacao production. Chemosphere, 150, 57-62. doi:10.1016/j.chemosphere.2016.02.013Chetschik, I., Kneubühl, M., Chatelain, K., Schlüter, A., Bernath, K., & Hühn, T. (2017). Investigations on the Aroma of Cocoa Pulp (Theobroma cacao L.) and Its Influence on the Odor of Fermented Cocoa Beans. Journal of Agricultural and Food Chemistry, 66(10), 2467-2472. doi:10.1021/acs.jafc.6b05008Codex Alimentarius. (2014).Codex Alimentarius Cocoa‐ Cocoa liquor.CODEX STAN 228–2001. (2001).General methods of analysis for contaminants CODEX STAN 228–2001.Cordella, M., Torri, C., Adamiano, A., Fabbri, D., Barontini, F., & Cozzani, V. (2012). Bio-oils from biomass slow pyrolysis: A chemical and toxicological screening. Journal of Hazardous Materials, 231-232, 26-35. doi:10.1016/j.jhazmat.2012.06.030Cortés, V., Blasco, J., Aleixos, N., Cubero, S., & Talens, P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology, 85, 138-148. doi:10.1016/j.tifs.2019.01.015Counet, C., Ouwerx, C., Rosoux, D., & Collin, S. (2004). Relationship between Procyanidin and Flavor Contents of Cocoa Liquors from Different Origins. Journal of Agricultural and Food Chemistry, 52(20), 6243-6249. doi:10.1021/jf040105bCrafack, M., Keul, H., Eskildsen, C. E., Petersen, M. A., Saerens, S., Blennow, A., … Nielsen, D. S. (2014). Impact of starter cultures and fermentation techniques on the volatile aroma and sensory profile of chocolate. Food Research International, 63, 306-316. doi:10.1016/j.foodres.2014.04.032Crouzillat D. Bellanger L. Rigoreau M. Bucheli P. &Pétiard V.(2000).Genetic structure characterisation and selection of Nacional cocoa compared to other genetic groups. In International Workshop on New Technologies and Cocoa Breeding.Cubero-Leon, E., Bouten, K., Senyuva, H., Stroka, J., Adam, M., … Bakalova, D. (2017). Determination of Ochratoxin A in Black and White Pepper, Nutmeg, Spice Mix, Cocoa, and Drinking Chocolate by High-Performance Liquid Chromatography Coupled with Fluorescence Detection: Collaborative Study. Journal of AOAC INTERNATIONAL, 100(5), 1458-1468. doi:10.5740/jaoacint.16-0430D’Souza, R. N., Grimbs, S., Behrends, B., Bernaert, H., Ullrich, M. S., & Kuhnert, N. (2017). Origin-based polyphenolic fingerprinting of Theobroma cacao in unfermented and fermented beans. Food Research International, 99, 550-559. doi:10.1016/j.foodres.2017.06.007Di Mattia, C., Martuscelli, M., Sacchetti, G., Beheydt, B., Mastrocola, D., & Pittia, P. (2014). Effect of different conching processes on procyanidin content and antioxidant properties of chocolate. Food Research International, 63, 367-372. doi:10.1016/j.foodres.2014.04.009Dickens, B., & Dickens, S. H. (1999). Estimation of concentration and bonding environment of water dissolved in common solvents using near infrared absorptivity. Journal of Research of the National Institute of Standards and Technology, 104(2), 173. doi:10.6028/jres.104.012Tran, P. D., Van de Walle, D., De Clercq, N., De Winne, A., Kadow, D., Lieberei, R., … Van Durme, J. (2015). Assessing cocoa aroma quality by multiple analytical approaches. Food Research International, 77, 657-669. doi:10.1016/j.foodres.2015.09.019DuBois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A., & Smith, F. (1956). Colorimetric Method for Determination of Sugars and Related Substances. Analytical Chemistry, 28(3), 350-356. doi:10.1021/ac60111a017Elwers, S., Zambrano, A., Rohsius, C., & Lieberei, R. (2009). Differences between the content of phenolic compounds in Criollo, Forastero and Trinitario cocoa seed (Theobroma cacao L.). European Food Research and Technology, 229(6), 937-948. doi:10.1007/s00217-009-1132-yEuropean Commission (EU). (2011).Commission regulation (EU) No 835/2011 of 19 August 2011 amending Regulation (EC) No 1881/2006 as regards maximum levels for polycyclic aromatic hydrocarbons in foodstuffs. Official Journal of the European Union 215 4–8.Fayeulle, N., Vallverdu-Queralt, A., Meudec, E., Hue, C., Boulanger, R., Cheynier, V., & Sommerer, N. (2018). Characterization of new flavan-3-ol derivatives in fermented cocoa beans. Food Chemistry, 259, 207-212. doi:10.1016/j.foodchem.2018.03.133FCC. (2018 June 20).Services ‐ Rules | The Federation of Cocoa Commerce. Retrieved fromhttp://www.cocoafederation.com/services/rulesForsyth, W. G. C., & Quesnel, V. C. (1957). Cacao polyphenolic substances. 4. The anthocyanin pigments*. Biochemical Journal, 65(1), 177-179. doi:10.1042/bj0650177Franco, R., Oñatibia-Astibia, A., & Martínez-Pinilla, E. (2013). Health Benefits of Methylxanthines in Cacao and Chocolate. Nutrients, 5(10), 4159-4173. doi:10.3390/nu5104159García-Alamilla, P., Salgado-Cervantes, M. A., Barel, M., Berthomieu, G., Rodríguez-Jimenes, G. C., & García-Alvarado, M. A. (2007). Moisture, acidity and temperature evolution during cacao drying. Journal of Food Engineering, 79(4), 1159-1165. doi:10.1016/j.jfoodeng.2006.04.005Gianfredi, V., Salvatori, T., Nucci, D., Villarini, M., & Moretti, M. (2018). Can chocolate consumption reduce cardio-cerebrovascular risk? A systematic review and meta-analysis. Nutrition, 46, 103-114. doi:10.1016/j.nut.2017.09.006Goodacre, R., & Anklam, E. (2001). Fourier transform infrared spectroscopy and chemometrics as a tool for the rapid detection of other vegetable fats mixed in cocoa butter. Journal of the American Oil Chemists’ Society, 78(10), 993-1000. doi:10.1007/s11746-001-0377-xHashimoto, J. C., Lima, J. C., Celeghini, R. M. S., Nogueira, A. B., Efraim, P., Poppi, R. J., & Pallone, J. A. L. (2018). Quality Control of Commercial Cocoa Beans (Theobroma cacao L.) by Near-infrared Spectroscopy. Food Analytical Methods, 11(5), 1510-1517. doi:10.1007/s12161-017-1137-2Hinneh, M., Semanhyia, E., Van de Walle, D., De Winne, A., Tzompa-Sosa, D. A., Scalone, G. L. L., … Dewettinck, K. (2018). Assessing the influence of pod storage on sugar and free amino acid profiles and the implications on some Maillard reaction related flavor volatiles in Forastero cocoa beans. Food Research International, 111, 607-620. doi:10.1016/j.foodres.2018.05.064Huang, X., Teye, E., Sam-Amoah, L. K., Han, F., Yao, L., & Tchabo, W. (2014). Rapid measurement of total polyphenols content in cocoa beans by data fusion of NIR spectroscopy and electronic tongue. Anal. Methods, 6(14), 5008-5015. doi:10.1039/c4ay00223gHue, C., Gunata, Z., Bergounhou, A., Assemat, S., Boulanger, R., Sauvage, F. X., & Davrieux, F. (2014). Near infrared spectroscopy as a new tool to determine cocoa fermentation levels through ammonia nitrogen quantification. Food Chemistry, 148, 240-245. doi:10.1016/j.foodchem.2013.10.005Hue, C., Gunata, Z., Breysse, A., Davrieux, F., Boulanger, R., & Sauvage, F. X. (2016). Impact of fermentation on nitrogenous compounds of cocoa beans (Theobroma cacao L.) from various origins. Food Chemistry, 192, 958-964. doi:10.1016/j.foodchem.2015.07.115Humston, E. M., Knowles, J. D., McShea, A., & Synovec, R. E. (2010). Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics. Journal of Chromatography A, 1217(12), 1963-1970. doi:10.1016/j.chroma.2010.01.069ICCO. (2012).Physical and chemical information on cocoa beans butter mass and powder. Retrieved fromhttps://www.icco.org/faq/61-physical-and-chemical-information-on-cocoa/106-physical-and-chemical-information-on-cocoa-beans-butter-mass-and-powder.htmlICCO. (2018).How is the quality of cocoa checked—by hand by machine?Retrieved fromhttps://www.icco.org/faq/59-fermentation-a-drying/108-how-is-the-quality-of-cocoa-checked-by-hand-by-machine.htmlICCO. (2019).Leading countries of cocoa bean processing worldwide 2018/2019 | Statista. Retrieved fromhttps://www.statista.com/statistics/238242/leading-countries-of-global-cocoa-bean-processing/Ioannone, F., Di Mattia, C. D., De Gregorio, M., Sergi, M., Serafini, M., & Sacchetti, G. (2015). Flavanols, proanthocyanidins and antioxidant activity changes during cocoa (Theobroma cacao L.) roasting as affected by temperature and time of processing. Food Chemistry, 174, 256-262. doi:10.1016/j.foodchem.2014.11.019Ishaq, S., & Jafri, L. (2017). Biomedical Importance of Cocoa (Theobroma cacao): Significance and Potential for the Maintenance of Human Health. Matrix Science Pharma, 1(1), 1-5. doi:10.26480/msp.01.2017.01.05Jackson E. Farrington D. S. &Henderson K.(1986).The analysis of agricultural materials: A manual of the analytical methods used by the Agricultural Development and Advisory Service. The Analysis of Agricultural Materials: A Manual of the Analytical Methods Used by the Agricultural Development and Advisory Service (No. 427 (Ed. 3)).Jahurul, M. H. A., Soon, Y., Shaarani Sharifudin, M., Hasmadi, M., Mansoor, A. H., Zaidul, I. S. M., … Jinap, S. (2018). Bambangan (Mangifera pajang ) kernel fat: a potential new source of cocoa butter alternative. International Journal of Food Science & Technology, 53(7), 1689-1697. doi:10.1111/ijfs.13753Jinap, S., Thien, J., & Yap, T. N. (1994). Effect of drying on acidity and volatile fatty acids content of cocoa beans. Journal of the Science of Food and Agriculture, 65(1), 67-75. doi:10.1002/jsfa.2740650111Kongor, J. E., Hinneh, M., de Walle, D. V., Afoakwa, E. O., Boeckx, P., & Dewettinck, K. (2016). Factors influencing quality variation in cocoa (Theobroma cacao) bean flavour profile — A review. Food Research International, 82, 44-52. doi:10.1016/j.foodres.2016.01.012Krähmer, A., Engel, A., Kadow, D., Ali, N., Umaharan, P., Kroh, L. W., & Schulz, H. (2015). Fast and neat – Determination of biochemical quality parameters in cocoa using near infrared spectroscopy. Food Chemistry, 181, 152-159. doi:10.1016/j.foodchem.2015.02.084Krähmer, A., Gudi, G., Weiher, N., Gierus, M., Schütze, W., & Schulz, H. (2013). Characterization and quantification of secondary metabolite profiles in leaves of red and white clover species by NIR and ATR-IR spectroscopy. Vibrational Spectroscopy, 68, 96-103. doi:10.1016/j.vibspec.2013.05.012Kruszewski, B., Obiedziński, M. W., & Kowalska, J. (2018). Nickel, cadmium and lead levels in raw cocoa and processed chocolate mass materials from three different manufacturers. Journal of Food Composition and Analysis, 66, 127-135. doi:10.1016/j.jfca.2017.12.012Kubíc̆ková, A., Kubíc̆ek, V., & Coufal, P. (2011). UV-VIS detection of amino acids in liquid chromatography: Online post-column solid-state derivatization with Cu(II) ions. Journal of Separation Science, 34(22), 3131-3135. doi:10.1002/jssc.201100561Kucha, C., Liu, L., & Ngadi, M. (2018). Non-Destructive Spectroscopic Techniques and Multivariate Analysis for Assessment of Fat Quality in Pork and Pork Products: A Review. Sensors, 18(2), 377. doi:10.3390/s18020377Kumari, N., Grimbs, A., D’Souza, R. N., Verma, S. K., Corno, M., Kuhnert, N., & Ullrich, M. S. (2018). Origin and varietal based proteomic and peptidomic fingerprinting of Theobroma cacao in non-fermented and fermented cocoa beans. Food Research International, 111, 137-147. doi:10.1016/j.foodres.2018.05.010Kutsanedzie, F. Y. H., Chen, Q., Hassan, M. M., Yang, M., Sun, H., & Rahman, M. H. (2018). Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chemistry, 240, 231-238. doi:10.1016/j.foodchem.2017.07.117Andres-Lacueva, C., Monagas, M., Khan, N., Izquierdo-Pulido, M., Urpi-Sarda, M., Permanyer, J., & Lamuela-Raventós, R. M. (2008). Flavanol and Flavonol Contents of Cocoa Powder Products: Influence of the Manufacturing Process. Journal of Agricultural and Food Chemistry, 56(9), 3111-3117. doi:10.1021/jf0728754Langer, S., Marshall, L. J., Day, A. J., & Morgan, M. R. A. (2011). Flavanols and Methylxanthines in Commercially Available Dark Chocolate: A Study of the Correlation with Nonfat Cocoa Solids. Journal of Agricultural and Food Chemistry, 59(15), 8435-8441. doi:10.1021/jf201398tLevasseur-Garcia, C. (2018). Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley). Toxins, 10(1),

New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications