Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection

Tesis por compendio[ES] El cacao es un producto de alto valor, no únicamente por sus características sensoriales, sino porque también presenta un alto contenido en antioxidantes y alcaloides estimulantes con efectos saludables. Debido a la alta demanda, la industria del cacao en polvo tiene el desafío de asegurar la calidad de grandes volúmenes de producción de una manera rápida y precisa, evitando la presencia de contaminantes o adulterantes en la materia prima, ofreciendo productos donde se preserven las propiedades saludables. La espectroscopia del infrarrojo cercano (NIR) es una tecnología rápida y no destructiva útil en el análisis de productos alimentarios. La presente tesis doctoral se centra en evaluar el potencial uso del NIR como una herramienta de control de calidad con el fin de poder resolver problemas que se presentan en la industria del cacao en polvo. Los problemas a resolver incluyen la detección de materiales no deseados o adulterantes en el cacao en polvo, y la monitorización rápida y precisa del contenido de flavanoles y metilxantinas del cacao en polvo durante el proceso de alcalinización. El primer capítulo evalúa la viabilidad del NIR, en combinación con análisis quimiométricos, en la detección de la presencia de materiales no deseados o adulterantes como son cascarilla de cacao o harina de algarroba. Para ello, diferentes muestras de cacao en polvo natural y con diferentes niveles de alcalinización (suave, medio y fuerte) fueron mezcladas con distintas proporciones de cascarilla de cacao (con cacao natural) o harina de algarroba (con cacao natural y alcalinizado). Los resultados obtenidos indican que el NIR, combinado con modelos estadísticos tales como el análisis discriminante por mínimos cuadrados parciales (PLS-DA) y la regresión parcial de mínimos cuadrados (PLS), es un método rápido y eficaz para identificar cualitativa y cuantitativamente materiales no deseados o adulterantes como la cascarilla y la algarroba en cacao en polvo, independientemente del grado de alcalinización o el nivel de tostado de la harina de algarroba. En el segundo capítulo, el análisis composicional del cacao en polvo se orientó al control de los cambios producidos en el contenido de flavanoles y metilxantinas debidos al proceso de alcalinización al que se somete el caco en polvo. Se determinó el contenido de catequina, epicatequina, cafeína y teobromina mediante cromatografía líquida de alta resolución (HPLC), correlacionándose los contenidos obtenidos para cada uno de estos compuestos con las determinaciones NIR. Se obtuvieron buenos modelos para la predicción de los compuestos mediante regresión PLS con valores superiores a 3 para la relación entre el rendimiento y la desviación (RDP), lo cual demuestra que los modelos obtenidos pueden ser utilizados para la rápida y fiable predicción del contenido de flavanoles y metilxantinas en cacaos naturales y con diferentes niveles de alcalinización.[CA] El cacau és un producte d'alt valor, no sols per les seues característiques sensorials, sinó perquè també presenta un elevat contingut en antioxidants i alcaloids estimulants amb efectes saludables. A conseqüència a l'alta demanda, l'industria del cacau en pols té el desafiament d'assegurar la qualitat de grans volums de producció d'una manera ràpida i precisa, evitant la presència de contaminants o adulterants en la matèria cosina, oferint productes a on se preserven les propietats saludables. L'espectroscòpia de l'infrarroig proper (NIR) és una tecnologia ràpida i no destructiva útil en l'anàlisi de productes alimentaris. La present tesis doctoral se centra en avaluar el potencial ús del NIR com una eina de control de qualitat amb l'objectiu de poder resoldre problemes que es presenten en l'industria del cacau en pols. Els problemes a resoldre inclouen la detecció de materials no desitjats o adulterants en el cacau en pols, i la monitorització ràpida i precisa del contingut de flavanols i metilxantines del cacau en pols durant el procés d'alcalinització. El primer capítol avalua la viabilitat del NIR, en combinació amb anàlisis quimiométrics, en la detecció de la presència de materials no desitjats o adulterants com són pellofa de cacau o farina de garrofa. Per a això, diferents mostres de cacau en pols natural i amb diferents nivells d'alcalinització (suau, mig i fort) foren barrejades en distintes proporcions de pellofa de cacau (en cacau natural) o farina de garrofa (en cacau natural i alcalinisat). Els resultats obtinguts per a NIR, combinats amb models estadístics com l'anàlisi discriminant per mínims quadrats parcials (PLS-DA) i la regressió parcial de mínims quadrats (PLS), és un mètode ràpid i eficaç per identificar materials no desitjats o adulterants com la pellofa de cacau o la farina de garrofa, amb independència del grau d'alcalinització del cacau o de torrat de la farina de garrofa. En el segon capítol, l'anàlisi composicional del cacau en pols s'orientà al control dels canvis produïts en el contingut de flavanols i metilxantines a causa del procés d'alcalinització al que se sotmet el cacau en pols. Es va determinar el contingut de catequina, epicatequina, cafeïna i teobromina mitjançant cromatografia líquida d'alta resolució (HPLC), i es van correlacionar els continguts obtinguts per a cadascun d'estos composts amb les determinacions NIR. Es van obtindré bons models per a la predicció dels composts mitjançant regressió PLS amb valors superiors a 3 per a la relació entre el rendiment i la desviació (RDP), la qual cosa demostra que els models obtinguts poden ser emprats per a la ràpida i fiable predicció del contingut de flavanols i metilxantines en cacaus naturals o amb diferents nivells d'alcalinització.[EN] Cocoa is a product of high value, not only because of its sensory characteristics, but also because it has a high content of antioxidants and stimulating alkaloids with health effects. Due to the high demand, the cocoa powder industry has the challenge of ensuring the quality of large volumes of production in a fast and accurate way, avoiding the presence of contaminants or adulterants in the raw material, offering products where the healthy properties are preserved. The near infrared spectroscopy (NIR) is a rapid and non-destructive technology useful in the analysis of food products. The present doctoral thesis focuses on evaluating the potential use of NIR as a quality control tool in order to solve problems that arise in the cocoa industry powdered. The problems to solve include the detection of unwanted materials or adulterants in the cocoa powder, and the rapid and accurate monitorization of the flavanols and methylxanthines content in the cocoa powder during the alkalization process. The first chapter evaluates the viability of the NIR, in combination with chemometric analysis, in the detection of presence of unwanted materials or adulterants such as cocoa shell or carob flour. For this, different samples of natural cocoa powder and with different levels of alkalization (light, medium and strong) were mixed with different proportions of cocoa shell (with natural cocoa) or carob flour (with natural and alkalized cocoa). The results obtained indicate that the NIR combined with statistical models such as the partial least squares discriminant analysis (PLS-DA) and the partial least squares regression (PLS), is a fast and efficient method to identify qualitative and quantitative unwanted materials or adulterants such as shell and carob in cocoa powder, regardless of the degree of alkalization or level of roasting of carob flour. In the second chapter, the compositional analysis of cocoa powder was oriented to the control of the changes produced in the content of flavanols and methylxanthines due to the process of alkalization to which the cocoa powder is subjected. The content of catechin, epicatechin, caffeine and theobromine were determined by high performance liquid chromatography (HPLC), correlating the contents obtained for each of these compounds with the NIR determinations. Good models were obtained for the prediction of compounds by regression PLS with values above 3 for the ratio of performance to deviation (RDP), which shows that the obtained models can be used for the quick and reliable prediction of flavanol content and methylxanthines in natural cocoas and with different alkalization levels.This Doctoral Thesis has been carried out thanks to a doctoral studies scholarship granted by the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of EcuadorQuelal Vásconez, MA. (2019). Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/135258TESISCompendi

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),

Determination of Trigonelline and Chlorogenic Acid (CGA) Concentration in Intact Coffee Beans by NIR Spectroscopy

Trigonelline and chlorogenic acid (CGA) are important quality indicators of coffee. Commonly, trigonelline and CGA concentration are determined using chemical method. This method is time consuming and destructive so it is not suitable for coffee industries which need a fast measurement. The objective of this study was to assess NIR spectroscopy for predicting trigonelline and CGA concentration in intact coffee beans. Coffee beans samples of 96 g (n=100) were placed in a petri dish. The reflectance of samples was measured by FT-NIR spectrometer in the wavelengths of 1000-2500 nm. Subsequently, the trigonelline and CGA content of samples were determined using Liquid Chromatography Mass Spectrometry (LCMS). Spectra data processing such as first and second derivative, multiple scatter correction (MSC), Standard Normal Variate (SNV) and the combination of them were carried out to reduce scattering, to eliminate overlapped absorption bands, and to optimize the best data input in calibration process. After that, these spectra (n=67) were calibrated to chemical data using Partial Least Square (PLS) to find the best calibration models. Then the calibration models were applied to predict trigonelline and chlorogenic acid (CGA) in another set of samples (n=33).  The results showed that NIR spectra data processing of second derivative combined with 4 factors of PLS was the best model for predicting CGA concentration of coffee (r=0.94, CV=2,75%, RPD=2.27). For trigonelline, however, the best model was combination of second derivative and MSC of spectra data processing and 4 factors of PLS (r=0.98, CV=1.63%, RPD=2.98). These results indicated that NIR spectroscopy can be used as a fast and nondestructive method for determining trigonelline and CGA in intact coffee beans accurately. Keywords:   chlorogenic acid, intact coffee bean, NIRS, PLS, trigonellin

Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans

The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000-2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R2 of 0.50 (RMSEP=0.27, RPD=1.40), 0.70 (RMSEP=34.1 mg ferulic acid g-1, RPD=1.77) and 0.74 (60.0 mmol Trolog kg-1, RPD=1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds. HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control

Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy

[EN] Cocoa powder is a highly valuable global product that can be adulterated with low-cost raw materials like carob flour as small amounts of this flour would not change the color, aroma and taste characteristics of the final product. Rapid methods, like NIR technology combined with multivariate analysis, are interesting for such detection. In this work, unaltered cocoa powders with different alkalization levels, carob flours with three different roasting degrees, and adulterated samples, prepared by blending cocoa powders with carob flour at several proportions, were analyzed. The diffuse reflectance spectra of the samples of 1100¿2500¿nm were acquired in a Foss NIR spectrophotometer. A qualitative and a quantitative analysis were done. For the qualitative analysis, a principal component analysis (PCA) and a partial least squares discriminant analysis (PLS-DA) were performed. Good results (100% classification accuracy) were obtained, which indicates the possibility of distinguishing pure cocoa powders from adulterated samples. For the quantitative analysis, a partial least squares (PLS) regression analysis was performed. The most robust PLS prediction model was obtained with one factor (LV), a coefficient of determination for prediction (RP2) of 0.974 and a root mean square error of prediction (RMSEP) of 3.2% for the external set. These data allowed us to conclude that NIR technology combined with multivariate analysis enables the identification and determination of the amount of natural cocoa powder present in a mixture adulterated with carob flour.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 of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant. The Olam Food Ingredients Company is acknowledged for proving part of the cocoa samples used hereinQuelal-Vásconez, MA.; Pérez-Esteve, É.; Arnau-Bonachera, A.; Barat Baviera, JM.; Talens Oliag, P. (2018). Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy. Food Control. 92:183-189. https://doi.org/10.1016/j.foodcont.2018.05.001S1831899

Electronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beans

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation. © Intelligent Network and Systems Society.The authors thank the Directorate of Research and Community Service, Ministry of Research, Technology and Higher Education, the Republic of Indonesia for providing research grants of PTUPT 2019 (Contract No. 2688/UN1.DITLIT/DITLIT/LT/2019). The authors also like to acknowledge the financial support given by Associate Laboratory LSRE-LCM-UID/EQU/50020/2019, strategic funding UID/BIO/04469/2019-CEB, BioTecNorte operation (NORTE-01-0145-FEDER-000004) and strategic project PEst-OE/AGR/UI0690/2014 – CIMO, all funded by national funds through FCT/MCTES (PIDDAC).info:eu-repo/semantics/publishedVersio

Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis

[EN] Cocoa shell must be removed from the cocoa bean before or after the roasting process. In the case of a low efficient peeling process or the intentional addition of cocoa shell to cocoa products (i.e. cocoa powders) to increase the economic benefit, quality of the final product could be unpleasantly affected. In this scenario, the Codex Alimentarius on cocoa and chocolate has established that cocoa cake must not contain more than 5% of cocoa shell and germ (based on fat-free dry matter). Traditional analysis of cocoa shell is very laborious. Thus, the aim of this work is to develop a methodology based on near infrared (NIR) spectroscopy and multivariate analysis for the fast detection of cocoa shell in cocoa powders. For this aim, binary mixtures of cocoa powder and cocoa shell containing increasing proportions of cocoa shell (up to ca. 40% w/w based on fat-free dried matter) have been prepared. After acquiring NIR spectra (1100-2500 nm) of pure samples (cocoa powder and cocoa shell) and mixtures, qualitative and quantitative analysis were done. The qualitative analysis was performed by using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), finding that the model was able to correctly classify all samples containing less than 5% of cocoa shell. The quantitative analysis was performed by using a partial least squares (PLS) regression. The best PLS model was the one constructed using extended multiple signal correction plus orthogonal signal correction pre-treatment using the 6 main wavelengths selected according to the Variable Importance in Projection (VIP) scores. Determination coefficient of prediction and root mean square error of prediction values of 0.967 and 2.43, respectively, confirmed the goodness of the model. According to these results it is possible to conclude that NIR technology in combination with multivariate analysis is a good and fast tool to determine if a cocoa powder contains a cocoa shell content out of Codex Alimentarius specifications.The authors wish to acknowledge the financial assistance provided the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). M. A. Quelal thanks the Ministry of 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.; Arnau-Bonachera, A.; Barat Baviera, JM.; Talens Oliag, P. (2019). Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis. Food Control. 99:68-72. https://doi.org/10.1016/j.foodcont.2018.12.028S68729

Changes in methylxanthines and flavanols during cocoa powder processing and their quantification by near-infrared spectroscopy

[EN] Variation in methylxanthines (theobromine and caffeine) and flavanols (catechin and epicatechin) was studied in a large set of cocoa powders (covering different origins, processing parameters and alkalisation levels). The content of these compounds was established by high-performance liquid chromatography (HPLC), whose results showed that the alkalisation process lowered the content of all analytes, whose loss was more evident in flavanols. Therefore, the determination of these analytes in a huge set of samples allowed not only better knowledge of the concentration variability in natural commercial cocoas from different origins, but also the understanding of the effect that industrial alkalisation has on these contents. The feasibility of reflectance near-infrared spectroscopy (NIRS) combined with partial least square (PLS) to non-destructively predict these contents, was also evaluated. All the analytes were generally well predicted, with predictions for methylxanthines (R-P(Z) 0.819-0.813 and RMSEP 0.068-0.022%, and bias 0.005 and 0.007 for theobromine and caffeine, respectively) and for flavanols (R-P(Z) 0.830-0.824; RMSEP 8.160-7.430% and bias - 1.440 and -1.034 for catechin and epicatechin, respectively). Thus NIRS could be an alternative fast reliable method for the routine assessment of these analytes in the cocoa industry.The authors would like to acknowledge the financial support of the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). M. A. Quelal thanks the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant. Olam Food Ingredients Company is aknowledged for proving part of the cocoa samples used in the study.Quelal-Vásconez, MA.; Lerma-García, MJ.; Pérez-Esteve, É.; Arnau-Bonachera, A.; Barat Baviera, JM.; Talens Oliag, P. (2020). Changes in methylxanthines and flavanols during cocoa powder processing and their quantification by near-infrared spectroscopy. LWT - Food Science & Technology (Online). 117:1-8. https://doi.org/10.1016/j.lwt.2019.108598S18117Afoakwa, E. O., Paterson, A., Fowler, M., & Ryan, A. (2008). Flavor Formation and Character in Cocoa and Chocolate: A Critical Review. Critical Reviews in Food Science and Nutrition, 48(9), 840-857. doi:10.1080/10408390701719272Álvarez, 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.990Bázár, G., Romvári, R., Szabó, A., Somogyi, T., Éles, V., & Tsenkova, R. (2016). NIR detection of honey adulteration reveals differences in water spectral pattern. Food Chemistry, 194, 873-880. doi:10.1016/j.foodchem.2015.08.092Bro, R., & Smilde, A. K. (2014). Principal component analysis. Anal. Methods, 6(9), 2812-2831. doi:10.1039/c3ay41907jBrunetto, M. del R., Gutiérrez, L., Delgado, Y., Gallignani, M., Zambrano, A., Gómez, Á., … Romero, C. (2007). Determination of theobromine, theophylline and caffeine in cocoa samples by a high-performance liquid chromatographic method with on-line sample cleanup in a switching-column system. Food Chemistry, 100(2), 459-467. doi:10.1016/j.foodchem.2005.10.007Cádiz-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.2014.07.016Elwers, 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-yFayeulle, 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.2018.03.133Franco, R., Oñatibia-Astibia, A., & Martínez-Pinilla, E. (2013). Health Benefits of Methylxanthines in Cacao and Chocolate. Nutrients, 5(10), 4159-4173. doi:10.3390/nu5104159Gottumukkala, R. V. S. S., Nadimpalli, N., Sukala, K., & Subbaraju, G. V. (2014). Determination of Catechin and Epicatechin Content in Chocolates by High-Performance Liquid Chromatography. International Scholarly Research Notices, 2014, 1-5. doi:10.1155/2014/628196Hue, 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.2013.10.005Humston, 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.2010.01.069Kongor, 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.2015.02.084Andres-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/jf201398tLi, Y., Feng, Y., Zhu, S., Luo, C., Ma, J., & Zhong, F. (2012). The effect of alkalization on the bioactive and flavor related components in commercial cocoa powder. Journal of Food Composition and Analysis, 25(1), 17-23. doi:10.1016/j.jfca.2011.04.010Machonis, P., Jones, M., Schaneberg, B., Kwik-Uribe, C., & Dowell, D. (2014). Method for the Determination of Catechin and Epicatechin Enantiomers in Cocoa-Based Ingredients and Products by High-Performance Liquid Chromatography: First Action 2013.04. Journal of AOAC International, 97(2), 506-509. doi:10.5740/jaoacint.13-351Miller, K. B., Hurst, W. J., Payne, M. J., Stuart, D. A., Apgar, J., Sweigart, D. S., & Ou, B. (2008). Impact of Alkalization on the Antioxidant and Flavanol Content of Commercial Cocoa Powders. Journal of Agricultural and Food Chemistry, 56(18), 8527-8533. doi:10.1021/jf801670pOñatibia-Astibia, A., Franco, R., & Martínez-Pinilla, E. (2017). Health benefits of methylxanthines in neurodegenerative diseases. Molecular Nutrition & Food Research, 61(6), 1600670. doi:10.1002/mnfr.201600670Payne, M. J., Hurst, W. J., Miller, K. B., Rank, C., & Stuart, D. A. (2010). Impact of Fermentation, Drying, Roasting, and Dutch Processing on Epicatechin and Catechin Content of Cacao Beans and Cocoa Ingredients. Journal of Agricultural and Food Chemistry, 58(19), 10518-10527. doi:10.1021/jf102391qPérez-Esteve, É., Lerma-García, M. J., Fuentes, A., Palomares, C., & Barat, J. M. (2016). Control of undeclared flavoring of cocoa powders by the determination of vanillin and ethyl vanillin by HPLC. Food Control, 67, 171-176. doi:10.1016/j.foodcont.2016.02.048Quelal-Vásconez, M. A., Lerma-García, M. J., Pérez-Esteve, É., Arnau-Bonachera, A., Barat, J. M., & Talens, P. (2019). Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis. Food Control, 99, 68-72. doi:10.1016/j.foodcont.2018.12.028Quelal-Vásconez, M. A., Pérez-Esteve, É., Arnau-Bonachera, A., Barat, J. M., & Talens, P. (2018). Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy. Food Control, 92, 183-189. doi:10.1016/j.foodcont.2018.05.001Risner, C. H. (2008). Simultaneous Determination of Theobromine, (+)-Catechin, Caffeine, and (-)-Epicatechin in Standard Reference Material Baking Chocolate 2384, Cocoa, Cocoa Beans, and Cocoa Butter. Journal of Chromatographic Science, 46(10), 892-899. doi:10.1093/chromsci/46.10.892Saeys, W., Mouazen, A. M., & Ramon, H. (2005). Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy. Biosystems Engineering, 91(4), 393-402. doi:10.1016/j.biosystemseng.2005.05.001Srdjenovic, B., Djordjevic-Milic, V., Grujic, N., Injac, R., & Lepojevic, Z. (2008). Simultaneous HPLC Determination of Caffeine, Theobromine, and Theophylline in Food, Drinks, and Herbal Products. Journal of Chromatographic Science, 46(2), 144-149. doi:10.1093/chromsci/46.2.144Sunoj, S., Igathinathane, C., & Visvanathan, R. (2016). Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy. Computers and Electronics in Agriculture, 124, 234-242. doi:10.1016/j.compag.2016.04.012Teye, E., Huang, X., Dai, H., & Chen, Q. (2013). Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 114, 183-189. doi:10.1016/j.saa.2013.05.063Toledo-Martín, E., García-García, M., Font, R., Moreno-Rojas, J., Salinas-Navarro, M., Gómez, P., & del Río-Celestino, M. (2018). Quantification of Total Phenolic and Carotenoid Content in Blackberries (Rubus Fructicosus L.) Using Near Infrared Spectroscopy (NIRS) and Multivariate Analysis. Molecules, 23(12), 3191. doi:10.3390/molecules23123191Toro-Uribe, S., Montero, L., López-Giraldo, L., Ibáñez, E., & Herrero, M. (2018). Characterization of secondary metabolites from green cocoa beans using focusing-modulated comprehensive two-dimensional liquid chromatography coupled to tandem mass spectrometry. Analytica Chimica Acta, 1036, 204-213. doi:10.1016/j.aca.2018.06.068Van Durme, J., Ingels, I., & De Winne, A. (2016). Inline roasting hyphenated with gas chromatography–mass spectrometry as an innovative approach for assessment of cocoa fermentation quality and aroma formation potential. Food Chemistry, 205, 66-72. doi:10.1016/j.foodchem.2016.03.004Vergara-Barberán, M., Lerma-García, M. J., Herrero-Martínez, J. M., & Simó-Alfonso, E. F. (2015). Cultivar discrimination of Spanish olives by using direct FTIR data combined with linear discriminant analysis. European Journal of Lipid Science and Technology, 117(9), 1473-1479. doi:10.1002/ejlt.201400425Veselá, A., Barros, A. S., Synytsya, A., Delgadillo, I., Čopíková, J., & Coimbra, M. A. (2007). Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder. Analytica Chimica Acta, 601(1), 77-86. doi:10.1016/j.aca.2007.08.039Wajrock, S., Antille, N., Rytz, A., Pineau, N., & Hager, C. (2008). Partitioning methods outperform hierarchical methods for clustering consumers in preference mapping. Food Quality and Preference, 19(7), 662-669. doi:10.1016/j.foodqual.2008.06.002Wold, S., Antti, H., Lindgren, F., & Öhman, J. (1998). Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 175-185. doi:10.1016/s0169-7439(98)00109-

Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging

© 2020 This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI). 170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions. HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations