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Non-Destructive Determination of Adulteration Rate in Ground Coffee (Coffea arabica) using Chromameter Combined with Chemometrics

Year 2025, Volume: 8 Issue: 6, 766 - 773, 15.11.2025
https://doi.org/10.47115/bsagriculture.1765001

Abstract

Roasted and ground coffee, owing to its high demand and commercial value, is often adulterated with cheaper materials to offset shortages or reduce costs. However, adulteration that could harm consumers economically or health-wise cannot be detected with the naked eye. For this reason, standard chemical analytical methods are generally preferred for detecting substances added to coffee for adulteration purposes. However, these methods also have some fundamental problems, such as being time-consuming, requiring pretreatment, requiring chemicals, being subjective, and often producing conflicting results. In this study, the success of determining the proportions of Robusta coffee, roasted soybeans, and carob flour mixed into Arabica coffee using color values was investigated in a practical, non-destructive, and objective manner without the need for chemical analysis methods. For samples adulterated with soy, L*a*b* values were used (RMSEP = 4.44%, R2val = 0.98), for samples adulterated with carob, L*a*b*C*h values were used (RMSEP=3.92%, R2val=0.98), and L*a*b*C*h values were used for samples adulterated with Robusta coffee (RMSEP=8.94%, R2val=0.91), resulting in successful prediction models. When the prediction models were evaluated in terms of RPD values, it was determined that all models had RPD values greater than 3, meaning that the prediction success rate for adulteration could be considered excellent. Based on the results obtained, it was observed that the proportions of soy, carob, and Robusta coffee added to Arabica coffee could be successfully determined in a short time using a colorimeter without causing any damage.

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

Thanks

The author would like to thank Dr. Yurtsever SOYSAL for his valuable technical support during the colour measurement and analysis.

References

  • Adamchuck V, Tremblay N. 2017. New developments in proximal soil sensing. In: Proceedings of the 7th Asian-Australasian Conf Precision Agric, Hamilton, New Zealand, pp: 1-4.
  • Ameca‐Veneroso C, Sánchez‐Arellano L, Ramón‐Canul LG, Herrera‐Corredor JA, Cuervo‐Osorio VD, Quetz‐Aguirre EM, Rodríguez‐Miranda J, Cabal‐Prieto A, Ramírez‐Rivera ED. 2021. A modified version of the sensory pivot technique as a possible tool for the analysis of food adulteration: a case of coffee. J Sens Stud, 36: e12705.
  • Aouadi B, Vitális F, Bodor Z, Zinia Zaukuu J, Kertész I, Kovács Z. 2022. NIRS and aquaphotomics trace Robusta-to-Arabica ratio in liquid coffee blends. Molecules, 27: 388.
  • Aroufai İA. 2020. Determination of antioxidant properties and their bioaccessibilities of coffee beans grown in different countries. MSc thesis, Bursa Uludağ Univ, Inst Nat Appl Sci, Bursa, pp: 55 (in Turkish).
  • Baytemir M. 2013. Tarımsal gıda işletmelerinin modernizasyonu. Presentation document. URL: https://www.tarimorman.gov.tr/ABDGM/Belgeler/%C4%B0DAR%C4%B0%20%C4%B0%C5%9ELER/temmuz/1.pdf (accessed date: 18 August, 2025).
  • Bonfil DJ. 2017. Monitoring wheat fields by RapidScan: accuracy and limitations. Adv Anim Biosci, 8: 333-337.
  • Çataltaş Ö, Tütüncü K. 2021. A review of data analysis techniques used in near infrared spectroscopy. Eur J Sci Technol, 25: 475-484.
  • Ebrahimi-Najafabadi H, Leardi R, Oliveri P, Casolino MC, Jalali-Heravi M, Lanteri S. 2012. Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques. Talanta, 99: 175-179
  • Ertugay MF, Başlar M. 2011. Gıdaların kalite özelliklerinin belirlenmesinde yakın kızıl ötesi (NIR) spektroskopisi. Gıda, 36(1): 49-54 (in Turkish).
  • Esbensen KH. 2009. Multivariate data analysis: in practice. 5th ed. Camo Software AS, Oslo, Norway, pp: 618.
  • FAOSTAT. 2022. Coffee production numbers. Food and Agriculture Organization of the United Nations (FAO). URL: https://www.fao.org/faostat/en (accessed date: 10 June, 2022).
  • Farah A. 2012. Coffee constituents. In: Chu YF, editor. Coffee: emerging health effects and disease prevention. Wiley-Blackwell, Hoboken, NJ, USA, 1st ed., pp: 21-58.
  • Ferreira T, Farah A, Oliveira TC, Lima IS, Vitório F, Oliveira EM. 2016. Using real-time PCR as a tool for monitoring the authenticity of commercial coffees. Food Chem, 199: 433-438.
  • Flores-Valdez M, Meza-Márquez OG, Osorio-Revilla G, Gallardo-Velázquez T. 2020. Identification and quantification of adulterants in coffee (Coffea arabica L.) using FT-MIR spectroscopy coupled with chemometrics. Foods, 9: 851.
  • Forchetti DAP, Poppi RJ. 2020. Detection and quantification of adulterants in roasted and ground coffee by NIR hyperspectral imaging and multivariate curve resolution. Food Anal Methods, 13: 44-49.
  • Garcia LM, Pauli ED, Cristiano V, da Camara CA, Scarminio IS, Nixdorf SL. 2009. Chemometric evaluation of adulteration profile in coffee due to corn and husk by determining carbohydrates using HPAEC-PAD. J Chromatogr Sci, 47 (9): 825-32.
  • Gholizadeh A, Kopačková V. 2019. Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques. Int J Environ Sci Technol, 16: 2511-2524.
  • ICO. 2020. Coffee production values in selected importing countries. International Coffee Organization, London, UK. URL: http://www.ico.org (accessed date: 10 June, 2022)
  • Keskin M, Arslan AA, Soysal Y, Şekerli YE, Çeliktaş N. 2021. Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: a pilot study. J Food Process Preserv, 00: e15846.
  • Keskin M, Setlek P, Demir S. 2017. Use of color measurement systems in food science and agriculture. In: Proceedings of the Int Adv Res Eng Cong (IAREC 2017), 16–18 November 2017, Osmaniye, Türkiye, pp: 2350-2359.
  • Keskin M. 2023. Assessing the level of adulteration in firik bulgur mixed with regular bulgur by using a color meter: a preliminary study. J Raw Mater Process Foods, 4(1): 10-25.
  • Kljusurić JG, Mihalev K, Becić IM, Polović I, Georgieva M, Djaković S, Kurtanjek Z. 2016. Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits. Food Technol Biotechnol, 54(2): 236-242.
  • Konica Minolta. 2007. Precise color communication: color control from perception to instrumentation. Konica Minolta Photo Sensing Inc., Japan. URL: https://www.konicaminolta.com/instruments/knowledge/color/pdf/color_communication.pdf (accessed date: May 20, 2025).
  • Narita Y, Inouye K. 2015. Chlorogenic acids from coffee. In: Preedy VR, editor. Coffee in health and disease prevention. Academic Press, London, UK, 1st ed., pp: 189-199.
  • Pathare PB, Opara UL, Al-Said FA. 2013. Colour measurement and analysis in fresh and processed foods: a review. Food Bioprocess Technol, 6(1): 36-60.
  • Pauli ED, Barbieri F, Garcia PS, Madeira TB, Acquaro VR, Scarminio IS, Câmara CA, Nixdorf SL. 2014. Detection of ground roasted coffee adulteration with roasted soybean and wheat. Food Res Int, 61: 112-119.
  • Santos JR, Sarraguça MC, Rangel AO, Lopes JA. 2012. Evaluation of green coffee beans quality using near infrared spectroscopy: a quantitative approach. Food Chem, 135(3): 1828-1835.
  • Schievano E, Finotello C, De Angelis E, Mammi S, Navarini L. 2014. Rapid authentication of coffee blends and quantification of 16-O-methylcafestol in roasted coffee beans by nuclear magnetic resonance. J Agric Food Chem, 62: 12309-12314.
  • Sezer B, Apaydın H, Bilge G, Boyaci IH. 2018. Coffee arabica adulteration: detection of wheat, corn and chickpea. Food Chem, 264: 142-148.
  • Soysal Y, Oztekin S, Isıkber AA, Duman AD, Dayısoylu KS. 2005. Assessing the colour quality attributes of Turkish red chilli peppers (Capsicum annuum L.) and colour stability during storage. In: Proceedings of the 9th International Congress on Mechanization and Energy in Agriculture, September, İzmir, Türkiye, pp: 27-29.
  • Wermelinger T, D’Ambrosio L, Klopprogge B, Yeretzian C. 2011. Quantification of the robusta fraction in a coffee blend via Raman spectroscopy: proof of principle. J Agric Food Chem, 59: 9074-9079.

Non-Destructive Determination of Adulteration Rate in Ground Coffee (Coffea arabica) using Chromameter Combined with Chemometrics

Year 2025, Volume: 8 Issue: 6, 766 - 773, 15.11.2025
https://doi.org/10.47115/bsagriculture.1765001

Abstract

Roasted and ground coffee, owing to its high demand and commercial value, is often adulterated with cheaper materials to offset shortages or reduce costs. However, adulteration that could harm consumers economically or health-wise cannot be detected with the naked eye. For this reason, standard chemical analytical methods are generally preferred for detecting substances added to coffee for adulteration purposes. However, these methods also have some fundamental problems, such as being time-consuming, requiring pretreatment, requiring chemicals, being subjective, and often producing conflicting results. In this study, the success of determining the proportions of Robusta coffee, roasted soybeans, and carob flour mixed into Arabica coffee using color values was investigated in a practical, non-destructive, and objective manner without the need for chemical analysis methods. For samples adulterated with soy, L*a*b* values were used (RMSEP = 4.44%, R2val = 0.98), for samples adulterated with carob, L*a*b*C*h values were used (RMSEP=3.92%, R2val=0.98), and L*a*b*C*h values were used for samples adulterated with Robusta coffee (RMSEP=8.94%, R2val=0.91), resulting in successful prediction models. When the prediction models were evaluated in terms of RPD values, it was determined that all models had RPD values greater than 3, meaning that the prediction success rate for adulteration could be considered excellent. Based on the results obtained, it was observed that the proportions of soy, carob, and Robusta coffee added to Arabica coffee could be successfully determined in a short time using a colorimeter without causing any damage.

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

Thanks

The author would like to thank Dr. Yurtsever SOYSAL for his valuable technical support during the colour measurement and analysis.

References

  • Adamchuck V, Tremblay N. 2017. New developments in proximal soil sensing. In: Proceedings of the 7th Asian-Australasian Conf Precision Agric, Hamilton, New Zealand, pp: 1-4.
  • Ameca‐Veneroso C, Sánchez‐Arellano L, Ramón‐Canul LG, Herrera‐Corredor JA, Cuervo‐Osorio VD, Quetz‐Aguirre EM, Rodríguez‐Miranda J, Cabal‐Prieto A, Ramírez‐Rivera ED. 2021. A modified version of the sensory pivot technique as a possible tool for the analysis of food adulteration: a case of coffee. J Sens Stud, 36: e12705.
  • Aouadi B, Vitális F, Bodor Z, Zinia Zaukuu J, Kertész I, Kovács Z. 2022. NIRS and aquaphotomics trace Robusta-to-Arabica ratio in liquid coffee blends. Molecules, 27: 388.
  • Aroufai İA. 2020. Determination of antioxidant properties and their bioaccessibilities of coffee beans grown in different countries. MSc thesis, Bursa Uludağ Univ, Inst Nat Appl Sci, Bursa, pp: 55 (in Turkish).
  • Baytemir M. 2013. Tarımsal gıda işletmelerinin modernizasyonu. Presentation document. URL: https://www.tarimorman.gov.tr/ABDGM/Belgeler/%C4%B0DAR%C4%B0%20%C4%B0%C5%9ELER/temmuz/1.pdf (accessed date: 18 August, 2025).
  • Bonfil DJ. 2017. Monitoring wheat fields by RapidScan: accuracy and limitations. Adv Anim Biosci, 8: 333-337.
  • Çataltaş Ö, Tütüncü K. 2021. A review of data analysis techniques used in near infrared spectroscopy. Eur J Sci Technol, 25: 475-484.
  • Ebrahimi-Najafabadi H, Leardi R, Oliveri P, Casolino MC, Jalali-Heravi M, Lanteri S. 2012. Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques. Talanta, 99: 175-179
  • Ertugay MF, Başlar M. 2011. Gıdaların kalite özelliklerinin belirlenmesinde yakın kızıl ötesi (NIR) spektroskopisi. Gıda, 36(1): 49-54 (in Turkish).
  • Esbensen KH. 2009. Multivariate data analysis: in practice. 5th ed. Camo Software AS, Oslo, Norway, pp: 618.
  • FAOSTAT. 2022. Coffee production numbers. Food and Agriculture Organization of the United Nations (FAO). URL: https://www.fao.org/faostat/en (accessed date: 10 June, 2022).
  • Farah A. 2012. Coffee constituents. In: Chu YF, editor. Coffee: emerging health effects and disease prevention. Wiley-Blackwell, Hoboken, NJ, USA, 1st ed., pp: 21-58.
  • Ferreira T, Farah A, Oliveira TC, Lima IS, Vitório F, Oliveira EM. 2016. Using real-time PCR as a tool for monitoring the authenticity of commercial coffees. Food Chem, 199: 433-438.
  • Flores-Valdez M, Meza-Márquez OG, Osorio-Revilla G, Gallardo-Velázquez T. 2020. Identification and quantification of adulterants in coffee (Coffea arabica L.) using FT-MIR spectroscopy coupled with chemometrics. Foods, 9: 851.
  • Forchetti DAP, Poppi RJ. 2020. Detection and quantification of adulterants in roasted and ground coffee by NIR hyperspectral imaging and multivariate curve resolution. Food Anal Methods, 13: 44-49.
  • Garcia LM, Pauli ED, Cristiano V, da Camara CA, Scarminio IS, Nixdorf SL. 2009. Chemometric evaluation of adulteration profile in coffee due to corn and husk by determining carbohydrates using HPAEC-PAD. J Chromatogr Sci, 47 (9): 825-32.
  • Gholizadeh A, Kopačková V. 2019. Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques. Int J Environ Sci Technol, 16: 2511-2524.
  • ICO. 2020. Coffee production values in selected importing countries. International Coffee Organization, London, UK. URL: http://www.ico.org (accessed date: 10 June, 2022)
  • Keskin M, Arslan AA, Soysal Y, Şekerli YE, Çeliktaş N. 2021. Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: a pilot study. J Food Process Preserv, 00: e15846.
  • Keskin M, Setlek P, Demir S. 2017. Use of color measurement systems in food science and agriculture. In: Proceedings of the Int Adv Res Eng Cong (IAREC 2017), 16–18 November 2017, Osmaniye, Türkiye, pp: 2350-2359.
  • Keskin M. 2023. Assessing the level of adulteration in firik bulgur mixed with regular bulgur by using a color meter: a preliminary study. J Raw Mater Process Foods, 4(1): 10-25.
  • Kljusurić JG, Mihalev K, Becić IM, Polović I, Georgieva M, Djaković S, Kurtanjek Z. 2016. Near-infrared spectroscopic analysis of total phenolic content and antioxidant activity of berry fruits. Food Technol Biotechnol, 54(2): 236-242.
  • Konica Minolta. 2007. Precise color communication: color control from perception to instrumentation. Konica Minolta Photo Sensing Inc., Japan. URL: https://www.konicaminolta.com/instruments/knowledge/color/pdf/color_communication.pdf (accessed date: May 20, 2025).
  • Narita Y, Inouye K. 2015. Chlorogenic acids from coffee. In: Preedy VR, editor. Coffee in health and disease prevention. Academic Press, London, UK, 1st ed., pp: 189-199.
  • Pathare PB, Opara UL, Al-Said FA. 2013. Colour measurement and analysis in fresh and processed foods: a review. Food Bioprocess Technol, 6(1): 36-60.
  • Pauli ED, Barbieri F, Garcia PS, Madeira TB, Acquaro VR, Scarminio IS, Câmara CA, Nixdorf SL. 2014. Detection of ground roasted coffee adulteration with roasted soybean and wheat. Food Res Int, 61: 112-119.
  • Santos JR, Sarraguça MC, Rangel AO, Lopes JA. 2012. Evaluation of green coffee beans quality using near infrared spectroscopy: a quantitative approach. Food Chem, 135(3): 1828-1835.
  • Schievano E, Finotello C, De Angelis E, Mammi S, Navarini L. 2014. Rapid authentication of coffee blends and quantification of 16-O-methylcafestol in roasted coffee beans by nuclear magnetic resonance. J Agric Food Chem, 62: 12309-12314.
  • Sezer B, Apaydın H, Bilge G, Boyaci IH. 2018. Coffee arabica adulteration: detection of wheat, corn and chickpea. Food Chem, 264: 142-148.
  • Soysal Y, Oztekin S, Isıkber AA, Duman AD, Dayısoylu KS. 2005. Assessing the colour quality attributes of Turkish red chilli peppers (Capsicum annuum L.) and colour stability during storage. In: Proceedings of the 9th International Congress on Mechanization and Energy in Agriculture, September, İzmir, Türkiye, pp: 27-29.
  • Wermelinger T, D’Ambrosio L, Klopprogge B, Yeretzian C. 2011. Quantification of the robusta fraction in a coffee blend via Raman spectroscopy: proof of principle. J Agric Food Chem, 59: 9074-9079.
There are 31 citations in total.

Details

Primary Language English
Subjects Precision Agriculture Technologies
Journal Section Research Articles
Authors

Yunus Emre Şekerli 0000-0002-7954-8268

Ahmet Zafer Aslan 0009-0001-4907-4348

Early Pub Date November 14, 2025
Publication Date November 15, 2025
Submission Date August 15, 2025
Acceptance Date September 18, 2025
Published in Issue Year 2025 Volume: 8 Issue: 6

Cite

APA Şekerli, Y. E., & Aslan, A. Z. (2025). Non-Destructive Determination of Adulteration Rate in Ground Coffee (Coffea arabica) using Chromameter Combined with Chemometrics. Black Sea Journal of Agriculture, 8(6), 766-773. https://doi.org/10.47115/bsagriculture.1765001

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