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Verification of Ingredient Labels in High-Risk Oils and Fruit Juices by Using Vibrational Spectroscopy Combined with Pattern Recognition Analysis

Aykas, Didem P
http://orcid.org/0000-0002-5500-0441
http://rave.ohiolink.edu/etdc/view?acc_num=osu1555644726731438

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Food Science and Technology.
Food adulteration and counterfeiting is a major worldwide problem with a cost of as much as $15 billion annually and affecting nearly 10% of all food products on the market. Besides its economic impact, public health risks could cause far more consequences to the related food industry or food company. Food fraud has been conducted since ancient times, and it is still a worldwide public concern, and a leading cause of trade problems internationally, olive oil and wine were the first counterfeit foods followed by fruit juices, spices, tea, milk, honey, and saffron. Advances in vibrational spectroscopy instruments have made possible rapid material screening with minimal sample preparation and training. The overall objective of this study was to establish a reliable ingredient label verification program(s) for edible oils and fruit juices using portable mid-infrared and Raman spectroscopic techniques combined with pattern recognition analysis. Our first aim evaluated an untargeted approach for authentication of edible oils used in the manufacturing of potato chips by combining the fingerprinting capabilities of a portable 5-reflection attenuated total reflectance infrared (FT-ATR) spectrometer combined with supervised pattern recognition. Oils were characterized by reference methods. Based on the fatty acid composition, we identified ten different frying oils that were used by manufacturers in producing the potato chips. Our data strongly supports that the IR technology can be used by snack food industry and governmental agencies to monitor authentication of frying oils and present great potential for efficient in-situ surveillance of food ingredients. The second study aimed to develop a non-targeted approach to authenticate EVOOs using vibrational spectroscopy (FT-IR and Raman) in combination with pattern recognition analysis. The samples were classified in 4 different groups as EVOO, virgin olive oil (VOO), lower quality olive oils, and olive oils adulterated with vegetable oils and olive pomace. The spectra were collected using a portable five-reflections FT-IR and a Raman spectroscopy with 1064 nm excitation laser. The SIMCA models gave best classification performance for EVOO by using FT-IR spectra showing high discrimination from VOO, lower quality olive oils, and the adulterated olive oils. On the other hand, the SIMCA model that was generated using the Raman spectra had lower sensitivity on the samples with similar profiles (virgin and refined olive oils) but allowed detection of adulteration with vegetable oils. The fingerprinting capabilities of vibrational spectroscopy showed potential for detection of EVOO adulteration as a rapid tool. This technology can provide industry and regulatory agencies with rapid and specific analysis of EVOO through the use of portable/handheld devices to detect ingredient tempering. In the last study, the aim was to develop a targeted prediction models for determination of multiple quality traits (sucrose, glucose, fructose, and total sugars, ascorbic and citric acids, titratable acidity, and soluble solids) of fruit juices (FJs) by using a field-deployable and portable FT-IR spectroscopy with no sample preparation. The quality traits of the samples were determined using official analytical methods, and FT-IR spectra were collected using a portable FT-IR with a transmission accessory. The PLSR models were developed to predict the quality traits by combining the FT-IR spectra and the calculated reference data. Overall, the PLSR models showed good correlation (Rpred≥0.94) and low SECV between the predicted and the measured values. We also compare the declared value on the nutrition labels and the reference results, and it had been found that 15% and 40% of FJs were not in compliance with the declaration for total sugars and ascorbic acid, respectively. Portable FT-IR devices offer non-destructive, simultaneous, simple and high throughput approaches for chemical profiling and real-time prediction of sugars and acid levels of fruit juices. Their handiness and ruggedness can provide food processors a valuable out-of-the laboratory analytical tool.
Luis Rodriguez-Saona (Advisor)
Monica Giusti (Committee Member)
John Litchfield (Committee Member)
Lynn Knipe (Committee Member)
191 p.
Food Science
Vibrational Spectroscopy; ingredient labels; Pattern Recognition Analysis

Recommended Citations

Citations

  • Aykas, D. P. (2019). Verification of Ingredient Labels in High-Risk Oils and Fruit Juices by Using Vibrational Spectroscopy Combined with Pattern Recognition Analysis [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555644726731438

    APA Style (7th edition)

  • Aykas, Didem. Verification of Ingredient Labels in High-Risk Oils and Fruit Juices by Using Vibrational Spectroscopy Combined with Pattern Recognition Analysis. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1555644726731438.

    MLA Style (8th edition)

  • Aykas, Didem. "Verification of Ingredient Labels in High-Risk Oils and Fruit Juices by Using Vibrational Spectroscopy Combined with Pattern Recognition Analysis." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555644726731438

    Chicago Manual of Style (17th edition)

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