Academic literature on the topic 'Food adulteration'
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Journal articles on the topic "Food adulteration"
HABZA-KOWALSKA, EWA, MAŁGORZATA GRELA, MAGDALENA GRYZIŃSKA, and PIOTR LISTOS. "Molecular techniques for detecting food adulteration." Medycyna Weterynaryjna 75, no. 05 (2020): 6260–2020. http://dx.doi.org/10.21521/mw.6261.
Full textEditorial, JNMA. "Food Adulteration." Journal of Nepal Medical Association 3, no. 4 (January 1, 2003): 258–59. http://dx.doi.org/10.31729/jnma.1068.
Full textSook Ling, Tan, Syazwan Hanani Meriam Suhaimy, and Nur Azimah Abd Samad. "Evaluation of fresh palm oil adulteration with recycled cooking oil using GC-MS and ATR-FTIR spectroscopy: A review." Czech Journal of Food Sciences 40, No. 1 (February 24, 2022): 1–14. http://dx.doi.org/10.17221/116/2021-cjfs.
Full textIssa-Issa, Hanán, Francisca Hernández, David López-Lluch, Reyhan Selin Uysal, and Ángel A. Carbonell-Barrachina. "Fondillón Wine Adulteration by Addition of Other Monastrell Wines." Beverages 9, no. 1 (March 20, 2023): 28. http://dx.doi.org/10.3390/beverages9010028.
Full textMburu, Monica, Clement Komu, Olivier Paquet-Durand, Bernd Hitzmann, and Viktoria Zettel. "Chia Oil Adulteration Detection Based on Spectroscopic Measurements." Foods 10, no. 8 (August 4, 2021): 1798. http://dx.doi.org/10.3390/foods10081798.
Full textGonzález-Domínguez, Sayago, Morales, and Fernández-Recamales. "Assessment of Virgin Olive Oil Adulteration by a Rapid Luminescent Method." Foods 8, no. 8 (July 25, 2019): 287. http://dx.doi.org/10.3390/foods8080287.
Full textSharma, Ameeta. "Food Adulteration: A Review." International Journal for Research in Applied Science and Engineering Technology V, no. III (March 30, 2017): 686–89. http://dx.doi.org/10.22214/ijraset.2017.3129.
Full textAmoah, Millicent, Regina Enyonam Adonu, Hannah Opoku, and Mercy Gyamea Atiemoh. "Consumer Awareness on Food Adulteration Practices on the Market and its Challenges." EAS Journal of Humanities and Cultural Studies 5, no. 04 (August 12, 2023): 197–204. http://dx.doi.org/10.36349/easjhcs.2023.v05i04.007.
Full textRahman, Md Arifur, Md Zakir Sultan, Mohammad Sharifur Rahman, and Mohammad A. Rashid. "Food Adulteration: a serious public health concern in Bangladesh." Bangladesh Pharmaceutical Journal 18, no. 1 (June 1, 2015): 1–7. http://dx.doi.org/10.3329/bpj.v18i1.23503.
Full textHoque, Majedul. "UNVEILING THE SILENT THREAT: FOOD ADULTERATION IN BANGLADESH." International Journal of Biological Innovations 05, no. 02 (2023): 21–26. http://dx.doi.org/10.46505/ijbi.2023.5203.
Full textDissertations / Theses on the topic "Food adulteration"
Gu, Youyang. "Food adulteration detection using neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106015.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 99-100).
In food safety and regulation, there is a need for an automated system to be able to make predictions on which adulterants (unauthorized substances in food) are likely to appear in which food products. For example, we would like to know that it is plausible for Sudan I, an illegal red dye, to adulter "strawberry ice cream", but not "bread". In this work, we show a novel application of deep neural networks in solving this task. We leverage data sources of commercial food products, hierarchical properties of substances, and documented cases of adulterations to characterize ingredients and adulterants. Taking inspiration from natural language processing, we show the use of recurrent neural networks to generate vector representations of ingredients from Wikipedia text and make predictions. Finally, we use these representations to develop a sequential method that has the capability to improve prediction accuracy as new observations are introduced. The results outline a promising direction in the use of machine learning techniques to aid in the detection of adulterants in food.
by Youyang Gu.
M. Eng.
Scordino, Monica. "Food control: quality assurance and protection against adulteration techniques." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1154.
Full textHossain, Rakia. "Safe Food in Bangladesh: Perception and Influences on Safe Food Purchasing." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/395524.
Full textThesis (Masters)
Master of Medical Research (MMedRes)
School of Medical Science
Griffith Health
Full Text
Narayanan, Deepak. "Building and processing a dataset containing articles related to food adulteration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100641.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 69).
In this thesis, I explored the problem of building a dataset containing news articles related to adulteration, and curating this dataset in an automated fashion. In particular, we looked at food-adulterant co-existence detection, query reforumulation, and entity extraction and text deduplication. All proposed algorithms were implemented in Python, and performance was evaluated on multiple datasets. Methods described in this thesis can be generalized to other applications as well.
by Deepak Narayanan.
M. Eng.
September, Danwille Jacqwin Franco. "Detection and quantification of spice adulteration by near infrared hyperspectral imaging." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6624.
Full textENGLISH ABSTRACT: Near infrared hyperspectral imaging (NIR HSI) in conjunction with multivariate image analysis was evaluated for the detection of millet and buckwheat flour in ground black pepper. Additionally, midinfrared (MIR) spectroscopy was used for the quantification of millet and buckwheat flour in ground black pepper. These techniques were applied as they allow non-destructive, invasive and rapid analysis. Black pepper and adulterant (either millet or buckwheat flour) mixtures were made in 5% (w/w) increments spanning the range 0-100% (w/w). The mixtures were transferred to eppendorf tube holders and imaged with a sisuChema short wave infrared (SWIR) pushbroom imaging system across the spectral range of 1000–2498 nm. Principal component analysis (PCA) was applied to pseudo-absorbance images for the removal of unwanted data (e.g. background, shading effects and bad pixels). PCA was subsequently applied to the ‘cleaned’ data. An adulterant concentration related gradient was observed in principal component one (PC1) and a difference between black pepper adulterated with buckwheat and millet was noted in PC4. Four absorption peaks (1461, 2241, 2303 and 2347 nm) were identified in the loading line plot of PC1 that are associated with protein and oil. The loading line plot of PC4 revealed absorption peaks at 1955, 1999, 2136 and 2303 nm, that are related to protein and oil. Partial least squares discriminant analysis (PLS-DA) was applied to NIR HSI images for discrimination between black pepper adulterated with varying amounts of adulterant (millet or buckwheat). The model created with millet adulterated black pepper samples had a classification accuracy of 77%; a classification accuracy of 70% was obtained for the buckwheat adulterated black pepper samples. An average spectrum was calculated for each sample in the NIR HSI images and the resultant spectra were used for the quantification of adulterant (millet or buckwheat) in ground black pepper. All samples were also analysed using an attenuated total reflectance (ATR) Fourier transform (FT) – infrared (IR) instrument and MIR spectra were collected between 576 and 3999 cm-1. PLS regression was employed. NIR based predictions (r2 = 0.99, RMSEP = 3.02% (w/w), PLS factor = 4) were more accurate than MIR based predictions (r2 = 0.56, RMSEP = 19.94% (w/w), PLS factors = 7). Preprocessed NIR spectra revealed adulterant specific absorption bands (1743, 2112 and 2167 nm) whereas preprocessed MIR spectra revealed a buckwheat specific signal at 1574 cm-1. NIR HSI has great promise for both the qualitative and quantitative analysis of powdered food products. Our study signals the beginning of incorporating hyperspectral imaging in the analysis of powdered food substances and results can be improved with advances in instrumental development and better sample preparation.
AFRIKAANSE OPSOMMING: Die gebruik van naby infrarooi hiperspektrale beelding (NIR HB) tesame met veelvoudige beeldanalise is ondersoek vir die opsporing van stysel-verwante produkte (giers en bokwiet) in gemaalde swart pepper. Middel-infrarooi (MIR) spektroskopie is addisioneel gebruik vir die kwantifisering van hierdie stysel-verwante produkte in swart pepper. Albei hierdie tegnieke is toegepas aangesien dit deurdringend van aard is en dit bied nie-destruktiewe sowel as spoedige analise. Swart pepper en vervalsingsmiddel (giers of bokwiet) mengsels is uitgevoer in 5% (m/m) inkremente tussen 0 en 100% (m/m). Eppendorfbuishouers is met die mengsels gevul en hiperspektrale beelde is verkry deur die gebruik van ‘n sisuChema SWIR (kortgolf infrarooi) kamera met ‘n spektrale reikwydte van 1000–2498 nm. Hoofkomponent-analise (HK) is toegepas op pseudo-absorbansie beelde vir die verwydering van ongewenste data (bv. agtergrond, skadu en dooie piksels). Hoofkomponent-analise is vervolgens toegepas op die ‘skoon’ data. Hoofkomponent (HK) een (HK1) het die aanwesigheid van ‘n vervalsingsmiddel konsentrasie verwante gradient getoon terwyl HK4 ‘n verskil getoon het tussen swart pepper vervals met giers en bokwiet. Vier absorpsiepieke (1461, 2241, 2303 en 2347 nm) was geïdentifiseer binne die HK lading stip van HK1 wat met proteïen en olie geassosieer kon word. Die HK lading stip van HK4 het absorpsipieke by 1955, 1999, 2136 en 2303 nm aangedui wat verband hou met proteïen en olie. Parsiële kleinste waarde diskriminant-analise (PKW-DA) is toegepas op die hiperspektrale beelde vir die moontlike onderskeiding tussen swart pepper vervals met verskeie hoeveelhede vervalsingsmiddel (giers of bokwiet). ‘n Klassifikasie koers van 77% is verkry vir die model ontwikkel met giers vervalsde swart pepper terwyl die model ontwikkel met bokwiet vervalsde swarte pepper ‘n klassifikasie koers van 70% bereik het. ‘n Gemiddelde spektrum is bereken vir elke monster in die hiperspektrale beelde en die resulterende spektra is gebruik vir die kwantifisering van vervalsingsmiddels (giers of bokwiet) in gemaalde swart pepper. ‘n ATR FT-IR instrument met spektrale reikwydte van 576-3999 cm-1 is additioneel gebruik vir die analise van alle monsters. Parsiële kleinste waarde regressie is gebruik vir kwantifikasie doeleindes. NIR gebasseerde voorspellings (r2 = 0.99, RMSEP = 3.02% (m/m), PLS faktore = 4) was meer akkuraat as die MIR gebasseerde voorspellings (r2 = 0.56, RMSEP = 19.94% (m/m), PLS faktore = 7). Vooraf behandelde NIR spektra het vervalsingsmiddel verwante absorpsiepieke (1743, 2112 en 2167 nm) aangetoon terwyl vooraf behandelde MIR spektra ‘n bokwiet verwante absorpsiepiek by 1574 cm-1 aangedui het. NIR HB toon goeie potensiaal vir beide kwalitatiewe en kwantitatiewe analise van gepoeierde voedsel produkte. Ons studie kan gesien word as die begin van die inkorporasie van hiperspektrale beelding in die analise van gepoeierde voedsel material en verbeterde resulte kan verkry word deur die vordering in instrumentasie ontwikkeling en verbeterde monstervoorbereiding.
Pillsbury, Laura Anne. "Food cultures, total diet studies and risk management implications for global food policy and public health /." Connect to this title, 2008. http://scholarworks.umass.edu/theses/157/.
Full textSurendera, Babu Aruna. "Food safety communication in Nevada needs assessment /." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433410.
Full textKulas, Megan. "Policy responses to reduce the opportunity for horsemeat adulteration fraud: the case of the European Union." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18243.
Full textDepartment of Diagnostic Medicine/Pathobiology
Justin Kastner
Food production is changing in response to an expanding global population. The ability to distribute and process ingredients amongst many individuals and countries has brought economic benefits while also creating new problems. By increasing the complexity of the supply chain, the food industry has birthed new dynamics, thus creating new opportunities for contamination, fraud, and other threats. One threat dynamic is the varying levels of food safety and quality control at different nodes along a supply chain. Contaminations pinpoint weaknesses of a supply chain, and such weaknesses could be exploited for harm. One way foods are intentionally contaminated is through food fraud. Food fraud involves substitution, mislabeling, dilution, and other means of criminal deception. Routine testing by an independent science-based group led to the discovery of one the largest scales of substitution and mislabeling in history—the 2013 adulteration of beef products with horsemeat. Commonly referred to as the horsemeat scandal of 2013, this important event in the history of the global food system affected several regions, hundreds of products, and thousands of retailers and consumers. To date, this scandal was one of the largest incidents of food fraud. Mostly based in the European Union, the horsemeat scandal prompted the European Commission to take regulatory action. The European Union’s policy response included the creation of a five-point plan that addresses the different facets associated with the scandal. The five-point plan sought to strengthen food fraud prevention; testing programs; horse passports; official control, implementation, penalties; and origin labelling. The five-point plan is intended to decrease the fraud opportunity for the adulteration of beef with horsemeat. According to the crime triangle, a concept frequently cited in the field of criminology, fraud opportunity has three main elements: the victims, the fraudsters, and the guardian and hurdle gaps. When any of these elements change, the opportunity for a fraudster to commit a crime also changes. The research question of this thesis explores the policy responses of the European Commission. The Commission’s five-point plan targets the three elements of fraud opportunity; therefore, future fraud opportunity for the adulteration of beef products with horsemeat will theoretically decrease.
Mendenhall, Ivan Von. "Rapid Determination of Milk Components and Detection of Adulteration Using Fourier Transform Infrared Technology." DigitalCommons@USU, 1991. https://digitalcommons.usu.edu/etd/5367.
Full textDi, Anibal Carolina Vanesa. "Determination of banned sudan dyes in culinary spices through spectroscopic techniques and multivariate analysis." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/52794.
Full textThis thesis is focused at developing multivariate analytical screening methodologies for determining the adulteration of culinary spices with Sudan I, II, III and IV dyes. Such dyes are prohibited to be used as additive in foods according to the European legislation because they are Class 3 carcinogens. The proposed methodologies are based on the use of spectroscopic techniques such as UV-Visible, 1H-NMR and Raman along with multivariate data treatment. The applied chemometric tools include the establishment and application of supervised classification techniques combined with exploratory data analysis, data processing and variable selection techniques to extract the maximum possible information from the spectral data. Otherwise some strategies to improve the classification have been evaluated such as data fusion strategies and multivariate transfer (standardization) methods.
Books on the topic "Food adulteration"
Gupta, S. R. Prevention of food adulteration programme. New Delhi: National Institute of Health and Family Welfare, 2005.
Find full textNijhawan, V. K., Manmohan Lal Sarin, and Bharti Seth. Food adulteration digest, 1984-2000. Delhi: Vinod Publications, 2001.
Find full textSharma, Prachi. Food adulteration in Rajasthan: An economic analysis. Delhi: Gaur Publishers & Distributors, 2010.
Find full textMalik, Sumeet. Handbook of food adulteration and safety laws. Lucknow: Eastern Book Co., 2011.
Find full textSteven, Nagy, Attaway John A. 1930-, and Rhodes Martha E, eds. Adulteration of fruit juice beverages. New York: M. Dekker, 1988.
Find full text1942-, Finley John W., Robinson Susan F. 1946-, Armstrong David J. 1942-, American Chemical Society. Division of Agricultural and Food Chemistry., and American Chemical Society Meeting, eds. Food safety assessment. Washington, D.C: American Chemical Society, 1992.
Find full textRaynes, Paul M. State programs and services in food and drug control. Rockville, Md: Dept. of Health and Human Services, Public Health Service, Food and Drug Administration, 1991.
Find full textIndia. The Prevention of Food Adulteration Act & Rules: As on 31.05.2008. New Delhi: Confederation of Indian Industry, 2008.
Find full textSteven, Nagy, and Wade Robert L. 1939-, eds. Methods to detect adulteration of fruit juice beverages. Auburndale, Fla: Agscience, 1994.
Find full textBook chapters on the topic "Food adulteration"
Dennis, Abigail. "Food Adulteration." In The Palgrave Encyclopedia of Victorian Women’s Writing, 592–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78318-1_292.
Full textCozzolino, Daniel. "Food Adulteration." In Spectroscopic Methods in Food Analysis, 353–62. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315152769-13.
Full textDennis, Abigail. "Food Adulteration." In The Palgrave Encyclopedia of Victorian Women's Writing, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-02721-6_292-1.
Full textSanchez, Marc C. "Adulteration." In Food Science Text Series, 69–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12472-8_3.
Full textSanchez, Marc C. "Adulteration." In Food Science Text Series, 69–100. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71703-6_3.
Full textTsagkaris, Aristidis S., Constantinos A. Papachristidis, Georgios P. Danezis, and Constantinos A. Georgiou. "Adulteration Stories." In Food Authentication, 423–29. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118810224.ch14.
Full textKamruzzaman, M. "Food Adulteration and Authenticity." In Food Safety, 127–48. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39253-0_7.
Full textAjayi, Ebenezer I. O. "Food Preservation, Spoilage and Food Adulteration." In Nutrition and Diet in Health, 40–53. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003361497-6.
Full textHazra, Tanmay, Rohit G. Sindhav, C. H. V. K. Sudheendra, and Vimal M. Ramani. "Milk Adulteration: Current Scenario and Challenges." In Biological and Chemical Hazards in Food and Food Products, 143–66. New York: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003189183-8.
Full textGupta, Karan, and Nitin Rakesh. "IoT-Based Solution for Food Adulteration." In Proceedings of First International Conference on Smart System, Innovations and Computing, 9–18. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5828-8_2.
Full textConference papers on the topic "Food adulteration"
Kovacs, Zoltan, George Bazar, Behafarid Darvish, Frederik Nieuwenhuijs, and Isabel Hoffmann. "Simultaneous detection of melamine and urea in gluten with a handheld NIR scanner." In OCM 2017 - 3rd International Conference on Optical Characterization of Materials. KIT Scientific Publishing, 2017. http://dx.doi.org/10.58895/ksp/1000063696-2.
Full textPerumal, B., Subash Balaji A, Vijaya Dharshini M, Aravind C, J. Deny, and R. Rajasudharsan. "Detection of Food Adulteration using Arduino IDE." In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021. http://dx.doi.org/10.1109/icesc51422.2021.9532720.
Full textNatarajan, Sowmya, and Vijayakumar Ponusamy. "A Review on Quantification of Food Adulteration Detection." In 2021 Smart Technologies, Communication and Robotics (STCR). IEEE, 2021. http://dx.doi.org/10.1109/stcr51658.2021.9588915.
Full textChen, Miao-Sheng, Ching-Yi Lin, and Po-Yu Chen. "Model design to analyze food safety regulations on food adulteration in Taiwan." In The 2nd Annual 2016 International Conference on Mechanical Engineering and Control System (MECS2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813208414_0058.
Full textKashani Zadeh, Hossein, Mitchell Sueker, Sayed Asaduzzaman, Nicholas MacKinnon, Gregory Bearman, Jianwei Qin, Rosalee S. Hellberg, et al. "Multimode spectroscopy for food quality, adulteration, and traceability (QAT) applications." In Sensing for Agriculture and Food Quality and Safety XVI, edited by Moon S. Kim and Byoung-Kwan Cho. SPIE, 2024. http://dx.doi.org/10.1117/12.3014191.
Full textDhangar, Vijay D., Pravin V. Dhole, Sulochana D. Shejul, and Bharti W. Gawali. "Assessment of Adulteration from Food Products using ASD Field Spec4." In 2023 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2023. http://dx.doi.org/10.1109/inc457730.2023.10262925.
Full textIsmail, Shereen, Mitchell Sueker, Sayed Asaduzzaman, Hassan Reza, Fartash Vasefi, and Hossein Kashani Zadeh. "Seafood quality, adulteration, and traceability technology integrated with blockchain supply chain." In Sensing for Agriculture and Food Quality and Safety XVI, edited by Moon S. Kim and Byoung-Kwan Cho. SPIE, 2024. http://dx.doi.org/10.1117/12.3014185.
Full textClapper, Gina, and Tongtong Xu. "Mitigation of Avocado Oil Adulteration – the Food Chemicals Codex Identity Standard." In Virtual 2021 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2021. http://dx.doi.org/10.21748/am21.205.
Full textSneha, S., S. Surjith, and S. M. Alex Raj. "A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges." In 2023 International Conference on Control, Communication and Computing (ICCC). IEEE, 2023. http://dx.doi.org/10.1109/iccc57789.2023.10165065.
Full textFiorani, Luca, Florinda Artuso, Isabella Giardina, Marcello Nuvoli, and Fabio Pollastrone. "Application of quantum cascade laser to rapid detection of food adulteration." In XV International Conference on Pulsed Lasers and Laser Applications, edited by Victor F. Tarasenko, Anton V. Klimkin, and Maxim V. Trigub. SPIE, 2021. http://dx.doi.org/10.1117/12.2605801.
Full textReports on the topic "Food adulteration"
Gafner, Stefan, and Josef Brinckmann. Adulteration of European Elder (Sambucus nigra) Berries and Berry Extracts. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, June 2023. http://dx.doi.org/10.59520/bapp.bapb/dgms7687.
Full textShulha, Oleksandr. English Lavender Essential Oil Laboratory Guidance Document. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, September 2023. http://dx.doi.org/10.59520/bapp.lgd/dhaf0609.
Full textKupina, Steve, Mark Kelm, Maria Monagas, and STEFAN GAFNER. Grape Seed Extract Laboratory Guidance Document. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, February 2019. http://dx.doi.org/10.59520/bapp.lgd/dozo2637.
Full text