Article

Robust Chemical Testing for Food Safety: Assessing and Identifying Dye Adulterants in Blueberry Juice: A Comprehensive Case Study with Statistical Measures and Correlation Analysis

Saurabh Bhandare ORCID , Sarika Shivaji  Malode ORCID
    Received
    22 Jan 2024
    Published
    09 Aug 2024

    Abstract

    In the food sector, the problem of food fraud—which encompasses the more focused category of adulteration driven by economic gain—is becoming more widely acknowledged and is raising concerns. Both the government and the industry are accountable for avoiding food adulteration, regardless of where the food danger originated. Food adulteration can result from incidents concerning food safety, food fraud, and food defence, which poses serious risks to the general public's health. In contrast to inadvertent injury in food safety events and intentional harm in food defence incidents, food fraud entails intentional activities for financial gain. Although financially motivated adulteration may have financial motivations, the unusual nature of contaminants frequently makes the related public health hazards more dangerous than traditional food safety threats. Establishing and executing a database that aggregates reports of food component fraud available for publicly accessible sources is an essential task. For governments, agencies, and individual enterprises assessing the possible dangers associated with certain items created in specific regions and those disseminated and sold elsewhere, this database provides essential information and useful data. The paper also describes current analytical tools for identifying food fraud and highlights new developments and trends in the food production industry and the food production bad sector.

    Keywords

    Adulteration, Artificial dyes, Average consumption, Blueberry juice, Chemical laboratory investigations, Correlation with other factors, Frequency distribution, Percentage, Statistical measures, Toxicity

    References
    1. [1] Haji A., Desalegn K., Hassen H., 2023. Selected food items adulteration, their impacts on public health, and detection methods: A review. Food Science and Nutrition. 11(12), 7534–7545. DOI: https://doi.org/10.1002/fsn3.3732
    2. [2] Moore J.C., Spink J., Lipp M., 2012. Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. J Food Sci. 77(4):R118–R126. DOI: https://doi.org/10.1111/j.1750-3841.2012.02657.x
    3. [3] Abbey J.R., Fields B., O’Mullane M., et al., 2014. Food additives: colorants. Elsevier eBooks. 459–465. DOI: https://doi.org/10.1016/b978-0-12-378612-8.00225-0
    4. [4] Akramzadeh N., Hosseini H., Pilevar ., et al.,2018. Physicochemical properties of novel non-meat sausages containing natural colorants and preservatives. Journal of Food Processing and Preservation.42(9), e13660. DOI: https://doi.org/10.1111/jfpp.13660
    5. [5] International Association of Color Manufacturers. PapRika – International Association of Color Manufacturers. International Association of Color Manufacturers. Published November 17, 2023 [cited 2023 Nov. 19]. Available from: https://iacmcolor.org/color-profile/paprika-paprika-oleoresin/#:~:text=Paprika%20oleoresin%20is%20the%20combination,are%20known%20to%20be%20present.
    6. [6] Spink J., Moyer D.C., 2011. Defining the public health threat of food fraud. Journal of Food Science. 76(9). DOI: https://doi.org/10.1111/j.1750-3841.2011.02417.x
    7. [7] Spink J., Moyer D.C., Park H., et al., 2015. Introducing Food Fraud including translation and interpretation to Russian, Korean, and Chinese languages. Food Chem. 189, 102–107. DOI: https://doi.org/10.1016/j.foodchem.2014.09.106
    8. [8] Spink J., Moyer D.C., 2011. Defining the public health threat of food fraud. J Food Sci. 76(9), R157–R163. DOI: https://doi.org/10.1111/j.1750-3841.2011.02417.x
    9. [9] Tibola C.S., da Silva S.A., Dossa A.A., et al.,2018. Economically Motivated Food Fraud and Adulteration in Brazil: Incidents and Alternatives to Minimize Occurrence. J Food Sci.83(8), 2028-2038. DOI: https://doi.org/10.1111/1750-3841.14279
    10. [10] Kendall H, Naughton P, Kuznesof S, et al.(2018) Food fraud and the perceived integrity of European food imports into China. PLoS One. 2018;13(5):e0195817. DOI: https://doi.org/10.1371/journal.pone.0195817
    11. [11] Galvin-King P., Haughey S.A., Elliott C.T.,2018. Herb and spice fraud; the drivers, challenges and detection. Food Control. 88, 85–97. DOI: https://doi.org/10.1016/j.foodcont.2017.12.031
    12. [12] Wang O., De Steur H., Gellynck X., et al.,2015. Motives for consumer choice of traditional food and European food in mainland China. Appetite. 87, 143–151. DOI: https://doi.org/10.1016/j.appet.2014.12.211
    13. [13] Dowd K., Burke K.J., 2013. The influence of ethical values and food choice motivations on intentions to purchase sustainably sourced foods. Appetite. 69, 137–144. DOI: https://doi.org/10.1016/j.appet.2013.05.024
    14. [14] He Y., Bai X., Xiao Q., et al., 2021. Detection of adulteration in food based on nondestructive analysis techniques: a review. Crit Rev Food Sci Nutr. 61(14), 2351–2371. DOI: https://doi.org/10.1080/10408398.2020.1777526
    15. [15] Deng Y., Wang X., Yang M., et al., 2020. Research advances in imaging technology for food safety and quality control. 38(7), 741–749. DOI: https://doi.org/10.3724/SP.J.1123.2020.03015
    16. [16] Huang H., Liu L., Ngadi M.O., 2014. Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors (Basel). 14(4), 7248–7276. DOI: https://doi.org/10.3390/s140407248
    17. [17] Ma J., Sun D.W., Pu H., et al., 2019. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol. 10, 197–220. DOI: https://doi.org/10.1146/annurev-food-032818-121155
    18. [18] Bonah E., Huang X., Aheto J.H., et al., 2019. Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization. Foodborne Pathog Dis. 16(10), 712–722. DOI: https://doi.org/10.1089/fpd.2018.2617
    19. [19] Haghbin N., Bakhshipour A., Zareiforoush H.,et al., 2023. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. Plant Methods. 19(1), 53. Published 2023 Jun 2. DOI: https://doi.org/10.1186/s13007-023-01032-y
    20. [20] Olsen N.V., Sijtsema S.J., Hall G., 2010. Predicting consumers’ intention to consume readyto-eat meals. The role of moral attitude. Appetite. 55(3), 534–539. DOI: https://doi.org/10.1016/j.appet.2010.08.016
    21. [21] Arvola A., Vassallo M., Dean M., et al., 2008. Predicting intentions to purchase organic food: the role of affective and moral attitudes in the Theory of Planned Behaviour. Appetite. 50(2–3), 443–454. DOI: https://doi.org/10.1016/j.appet.2007.09.010
    22. [22] Nanou E., Pliatsika N., Couris S., 2023. Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy. Molecules. 28(24), 7960. Published 2023 Dec 5. DOI: https://doi.org/10.3390/molecules28247960
    23. [23] Moan I.S., Rise J., 2011. Predicting intentions not to “drink and drive” using an extended version of the theory of planned behaviour. Accid Anal Prev. 43(4), 1378–1384. DOI: https://doi.org/10.1016/j.aap.2011.02.012
    24. [24] Conner M., Lawton R., Parker D., et al., 2007. Application of the theory of planned behaviour to the prediction of objectively assessed breaking of posted speed limits. Br J Psychol. 98(Pt 3), 429–453. DOI: https://doi.org/10.1348/000712606X133597
    25. [25] Pigłowski M., Niewczas-Dobrowolska M.Hazards, 2024. Reported on food of plant origin in the Rapid Alert System for Foodand Feed (RASFF) from 1997 to 2021 and their occurrence, prevention and reduction. Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 41(1), 91–104. DOI: https://doi.org/10.1080/19440049.2023.2299679
    26. [26] Pakdel M., Olsen A., Bar E.M.S., 2023. A review of food contaminants and their pathways within food processing facilities using open food processing equipment. Journal of Food Protection. 86(12), 100184. DOI: https://doi.org/10.1016/j.jfp.2023.100184
    27. [27] Møretrø T., Moen B., Heir E., et al., 2016. Contamination of salmon fillets and processing plants with spoilage bacteria. International Journal of Food Microbiology. 237, 98–108. DOI: https://doi.org/10.1016/j.ijfoodmicro.2016.08.016
    28. [28] How to prevent food poisoning. CDC. GOV. Accessed December 29, 2023. https://www.cdc.gov/foodsafety/prevention.html
    29. [29] Johnson, R., 2014. Food Fraud and “Economically Motivated Adulteration” of Food and 878 Food Ingredients. Available from: https://fas.org/sgp/crs/misc/R43358.pdf Accessed 25–December-2022
    30. [30] Toci A.T., Farah A., Pezza H.R., et al., 2016. Coffee Adulteration: More than Two Decades of Research. Crit Rev Anal Chem. 46(2), 83–92. DOI: https://doi.org/10.1080/10408347.2014.966185
    31. [31] Barbin D.F., De Souza Madureira Felício A.L., Sun D., et al., 2014. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Research International. 61, 23–32. DOI: https://doi.org/10.1016/j.foodres.2014.01.005
    32. [32] Burns D.T., Walker M.J., 2020. Critical Review of Analytical and Bioanalytical Verification of the Authenticity of Coffee. J AOAC Int. 103(2), 283–294. DOI: https://doi.org/10.5740/jaoacint.18-0392
    33. [33] Huck C., Guggenbichler W., Bonn G.K., 2005. Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Analytica Chimica Acta. 538(1–2), 195–203. DOI: https://doi.org/10.1016/j.aca.2005.01.064
    34. [34] Carvalho C., Santos GVCCD., 2015. Global communities, biotechnology and sustainable design – natural / bio dyes in textiles. Procedia Manufacturing. 3, 6557–6564. DOI: https://doi.org/10.1016/j.promfg.2015.07.956
    35. [35] Ziarani G.M., Moradi R., Lashgari N., et al., 2018. Introduction and importance of synthetic organic dyes. Elsevier eBooks. 1–7. DOI: https://doi.org/10.1016/b978-0-12-815647-6.00001-7
    36. [36] Baker F.J., Silverton R.E., 1976. Biological staining. Elsevier eBooks. 384–392.DOI: https://doi.org/10.1016/b978-0-407-00154-1.50021-x
    37. [37] Köroğlu E., Yörüklü H.C., DemiR A., et al., 2019. Scale-Up and commercialization issues of the MFCs. Elsevier eBooks. 565–583. DOI: https://doi.org/10.1016/b978-0-444-64052-9.00023-6
    38. [38] Chomphen L., Yamanont P., Morales N.P., 2024. Flavonoid Metabolites in Serum and Urine after the Ingestion of Selected Tropical Fruits. Nutrients. 16(1), 161.DOI: https://doi.org/10.3390/nu16010161
    39. [39] Daniel WW. Biostatistics: A Foundation for Analysis in the Health Science. 7th ed. John Wiley and Sons, Inc. New York.; 2005.
    Back to top