Готові списки джерел за темами / Detection of food adulteration

Добірка наукової літератури з теми "Detection of food adulteration"

Автор: Grafiati

Опубліковано: 13 вересня 2021

Оновлено: 1 лютого 2022

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Статті в журналах з теми "Detection of food adulteration":

Mburu, 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.

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Chia oil is a valuable source of omega-3-fatty acids and other nutritional components. However, it is expensive to produce and can therefore be easily adulterated with cheaper oils to improve the profit margins. Spectroscopic methods are becoming more and more common in food fraud detection. The aim of this study was to answer following questions: Is it possible to detect chia oil adulteration by spectroscopic analysis of the oils? Is it possible to identify the adulteration oil? Is it possible to determine the amount of adulteration? Two chia oils from local markets were adulterated with three common food oils, including sunflower, rapeseed and corn oil. Subsequently, six chia oils obtained from different sites in Kenya were adulterated with sunflower oil to check the results. Raman, NIR and fluorescence spectroscopy were applied for the analysis. It was possible to detect the amount of adulterated oils by spectroscopic analysis, with a minimum R2 of 0.95 for the used partial least square regression with a maximum RMSEPrange of 10%. The adulterations of chia oils by rapeseed, sunflower and corn oil were identified by classification with a median true positive rate of 90%. The training accuracies, sensitivity and specificity of the classifications were over 90%. Chia oil B was easier to detect. The adulterated samples were identified with a precision of 97%. All of the classification methods show good results, however SVM were the best. The identification of the adulteration oil was possible; less than 5% of the adulteration oils were difficult to detect. In summary, spectroscopic analysis of chia oils might be a useful tool to identify adulterations.

Fiorani, Luca, Florinda Artuso, Isabella Giardina, Antonia Lai, Simone Mannori, and Adriana Puiu. "Photoacoustic Laser System for Food Fraud Detection." Sensors 21, no. 12 (June 18, 2021): 4178. http://dx.doi.org/10.3390/s21124178.

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Economically motivated adulterations of food, in general, and spices, in particular, are an emerging threat to world health. Reliable techniques for the rapid screening of counterfeited ingredients in the supply chain need further development. Building on the experience gained with CO2 lasers, the Diagnostic and Metrology Laboratory of ENEA realized a compact and user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser. The sensor has been challenged with saffron adulteration. Multivariate data analysis tools indicated that the photoacoustic laser system was able to detect adulterants at mass ratios of 2% in less than two minutes.

Čapla, Jozef, Peter Zajác, Jozef Čurlej, Ľubomír Belej, Miroslav Kročko, Marek Bobko, Lucia Benešová, Silvia Jakabová, and Tomáš Vlčko. "Procedures for the identification and detection of adulteration of fish and meat products." Potravinarstvo Slovak Journal of Food Sciences 14 (October 28, 2020): 978–94. http://dx.doi.org/10.5219/1474.

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The addition or exchange of cheaper fish species instead of more expensive fish species is a known form of fraud in the food industry. This can take place accidentally due to the lack of expertise or act as a fraud. The interest in detecting animal species in meat products is based on religious demands (halal and kosher) as well as on product adulterations. Authentication of fish and meat products is critical in the food industry. Meat and fish adulteration, mainly for economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Economically motivated adulteration of food is estimated to create damage of around € 8 to 12 billion per year. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat and fish adulteration. Various analytical methods often based on protein or DNA measurements are utilized to identify fish and meat species. Although many strategies have been adopted to assure the authenticity of fish and meat and meat a fish products, such as the protected designation of origin, protected geographical indication, certificate of specific characteristics, and so on, the coverage is too small, and it is unrealistic to certify all meat products for protection from adulteration. Therefore, effective supervision is very important for ensuring the suitable development of the meat industry, and rapid, effective, accurate, and reliable detection technologies are fundamental technical support for this goal. Recently, several methods, including DNA analysis, protein analysis, and fat-based analysis, have been effectively employed for the identification of meat and fish species.

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.

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Food adulteration means that substances have been added to food that change its composition and reduce its nutritional value. Food adulteration also includes giving a product a misleading name, providing false information on its composition, date of production or expiry date, and any other incorrect labelling. Numerous cases of food adulteration have been recorded in many countries, including Poland. This has led to the creation of a new field of science, known as ‘green criminology’, to combat violations of food law. Over the years, new techniques for identifying food adulterations have been developed. Originally, these were sensory techniques, which proved unreliable. Later, physical analysis of the product was performed on the basis of information on the label and microscopic examination. Later methods, based on identification of lipids and proteins, were also unreliable due to biochemical changes during processing. These problems prompted scientists to become interested in the potential of DNA testing. Due the stability of DNA and the universal applicability of DNA-based methods to all cells, they are ideal for use in practice. Currently, the most reliable test for detecting food adulteration is PCR, as it is a highly sensitive and specific technique.

Menon, K. I. Ajay, Pranav S, Sachin Govind, and Yadhukrishna Madhu. "RF SENSOR FOR FOOD ADULTERATION DETECTION." Progress In Electromagnetics Research Letters 93 (2020): 137–42. http://dx.doi.org/10.2528/pierl20090103.

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González-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.

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The adulteration of virgin olive oil with hazelnut oil is a common fraud in the food industry, which makes mandatory the development of accurate methods to guarantee the authenticity and traceability of virgin olive oil. In this work, we demonstrate the potential of a rapid luminescent method to characterize edible oils and to detect adulterations among them. A regression model based on five luminescent frequencies related to minor oil components was designed and validated, providing excellent performance for the detection of virgin olive oil adulteration.

Borková, M., and J. Snášelová. "Possibilities of different animal milk detection in milk and dairy products – a review." Czech Journal of Food Sciences 23, No. 2 (November 15, 2011): 41–50. http://dx.doi.org/10.17221/3371-cjfs.

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Adulteration of milk and dairy products with different types of milk, other than declared, presents a big problem for food monitoring. The evidence of milk adulteration is a difficult task considering similar compositions of various types of milk. The presented review is therefore focused on the study of the composition of milk from different animal species. The aim is to find a useful marker component for the adulterant detection. The analysis of milk proteins is a suitable solution of this problem. The techniques used for research in this area were also studied. As prospective techniques, immunological techniques and techniques based on DNA analysis are especially considered. The first ones are able to determine 0.5% of different milk adulterant, and the second ones even as little as 0.1%. Reverse-phase high-performance liquid chromatography is successfully applied in the quantitative analysis of individual milk adulterants in samples. The most frequent adulteration of ewe and goat milk is its replacement with less expensive and more plentiful bovine milk. Not so typical adulteration is the presence of goat milk in ewe milk or the detection of bovine milk as adulterant in buffalo mozzarella cheese.  

Banti, Misgana. "Food Adulteration and Some Methods of Detection, Review." International Journal of Nutrition and Food Sciences 9, no. 3 (2020): 86. http://dx.doi.org/10.11648/j.ijnfs.20200903.13.

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Bansal, Sangita, Apoorva Singh, Manisha Mangal, Anupam K. Mangal, and Sanjiv Kumar. "Food adulteration: Sources, health risks, and detection methods." Critical Reviews in Food Science and Nutrition 57, no. 6 (June 9, 2015): 1174–89. http://dx.doi.org/10.1080/10408398.2014.967834.

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Inamdar, Prof S. Y. "IoT Based Milk Adulteration Analyser." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2492–95. http://dx.doi.org/10.22214/ijraset.2021.36908.

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Food safety is a more important issue in rural and urban areas as it affects the health of the citizens. Different studies have shown that adulteration of milk is a problem in many countries. Increased adulteration in milk poses a health risk that can lead to life-threatening diseases. So it is necessary to develop a device that allows detection of such life-threatening adulterants present in milk. This is achieved by detecting adulterants in milk using basic principle of spectrometer combined with automated devices.

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Дисертації з теми "Detection of food adulteration":

Gu, Youyang. "Food adulteration detection using neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106015.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This 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.

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.

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Thesis (MSc Food Sc)--University of Stellenbosch, 2011.
ENGLISH 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.

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.

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Absorption bands responding to changes in fat, protein, and lactose concentrations in milk were determined. The effects of milk fat variation and lipolysis on the infrared spectrum were studied. Absorbances from 1283 to 1100 cm-1 correlated with fat, protein, and lactose concentration and showed a low response to milk fat variation and lipolysis. A Fourier transform infrared spectrometer equipped with an attenuated total internal reflectance cell was calibrated using these absorption band s, partial least squares statistics, and milk samples from herds in Minnesota. When the fat, protein, and lactose concentrations in these samples were predicted, the standard deviations of difference (reference - infrared) were .22, .06, and .02% . When the fat, protein, and lactose concentrations in a separate set of samples from herds in California were predicted, the standard deviations of difference were 1.23, .10, and .07%. Substitution of a 15 μm pathlength transmission cell for the attenuated total internal reflectance cell changed the standard deviations of difference to .07, .11, and .06% in the calibration (Minnesota) samples and .09, .10, and .16% in the validation (California) samples. Infrared spectroscopy was used to measure whey powder in an adulterated sample of nonfat dry milk. Mixtures of nonfat dry milk containing whey powder at various concentrations were analyzed using absorption bands between 1400 and 1200 cm-1 in the infrared spectrum. There was a strong correlation (r > .99) between predicted and measured concentrations of whey powder in adulterated samples. Accuracy was not affected by processing conditions , source of nonfat dry milk, and origin of whey powder. A rapid method for detecting soybean oil in process cheese was developed. The infrared spectrum of each sample was collected using an accessory designed for analysis of solid samples. A linear relationship fit (= .98) when the ratio of absorbance at 2957 and 2852 cm-1 was plotted versus percent adulteration.

Menevseoglu, Ahmed. "METABOLOMICS APPROACH FOR AUTHENTICATION OF PISCO AND DETECTION OF CONTAMINANTS." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574841283680933.

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Woodbury, Simon Edward. "Application of gas chromatography combustion-isotope ratio mass spectrometry to the detection of adulteration of vegetable oils." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246268.

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Kelly, Simon Douglas. "The development of continuous-flow isotope ratio mass spectrometry methods and their application to the detection of food adulteration." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251500.

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Prachárová, Adriana. "Stanovení autenticity potravinářských výrobků s ovocnou složkou." Master's thesis, Vysoké učení technické v Brně. Fakulta chemická, 2021. http://www.nusl.cz/ntk/nusl-449765.

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The aim of this thesis was to determine the authenticity of fruit food for infants using molecular and instrumental methods. In the experimental part, plant DNA isolations from fruit leaves (peaches, apricots, plums and apples) and bananas were performed. Further, DNA was isolated also from five commercial products, and from model mixtures that were prepared in terms of content identical to the commercial mixtures. The isolated DNA was characterized and verified by qPCR with plant DNA-specific ITS2 primers. Three triple primer pairs were selected, and their specificity was evaluated when performing multiplex PCR. This method makes it possible to detect more types of fruit in one reaction, reducing the economic and time requirements for detection. As none of the selected primer pairs were sufficiently specific for the apricot, the evidence from the plum and peach was further realized using duplex PCR. High resolution melting curve analysis was used for better DNA type recognition. Subsequently, agarose gel electrophoresis was performed to analyse the fragment lengths. Furthermore, experiments have been made to identify some specific phenolic substances in commercial and model fruit mixtures by HPLC. Since phenolic substances are degradable under unsuitable storage conditions, the presence of individual compounds was not detected by this method.

Plášková, Anna. "Stanovení autenticity potravin rostlinného původu pomocí molekulárních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta chemická, 2020. http://www.nusl.cz/ntk/nusl-433058.

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The aim of presented diploma thesis was to determination of authenticity of fruit baby foods for early infant feeding using molecular methods. In the experimental part, isolation kit was used for isolation of plant DNA from fruits (strawberry, apricot, raspberry, apple) and from six commercial fruit products for children. Isolated DNA was characterized and verified using PCR methods with primers specific for plant rDNA (ITS2). Specific primer pairs were designed to amplify DNA for the detection of one fruit species. Primer specificity was assessed with four fruit species. A mixture of fruit puree from the two fruits was used to determine the sensitivity of the multiplex PCR assay. Six commercial fruit products were evaluated to verify the applicability of the multiplex PCR assay. The methodology of molecular detection of fruit DNA by qPCR and multiplex qPCR (duplex) includes approaches, which enable to detect two fruits (strawberry-raspberry, apricot-apple) in one reaction and thus reduces time and money requirements.

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.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This 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.

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/.

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Книги з теми "Detection of food adulteration":

Rosette, Jack L. Product tampering detection: Field investigations manual. Atlanta: Forensic Packaging Concepts, 1992.

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MacPhee, S. Evaluation of the EiaFoss Listeria system for the detection of Listeria species from foods. Chipping Campden: Campden & Chorleywood Food Research Association, 1997.

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Wallin, P. Foreign body prevention, detection, and control: A practical approach. London: Blackie Academic & Professional, 1998.

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Edwards, M. C. Detecting foreign bodies in food. Boca Raton: CRC Press, 2004.

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Office, General Accounting. Food safety and quality: Existing detection and control programs minimize aflatoxin : report to the chairman, Subcommittee on Wheat, Soybeans, and Feed Grains, Committee on Agriculture, House of Representatives. Washington, DC: The Office, 1991.

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Nijhawan, V. K., Manmohan Lal Sarin, and Bharti Seth. Food adulteration digest, 1984-2000. Delhi: Vinod Publications, 2001.

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Gupta, S. R. Prevention of food adulteration programme. New Delhi: National Institute of Health and Family Welfare, 2005.

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Johanson, Paula. Processed food. New York: Rosen Central, 2008.

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Sharma, Prachi. Food adulteration in Rajasthan: An economic analysis. Delhi: Gaur Publishers & Distributors, 2010.

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Malik, Sumeet. Handbook of food adulteration and safety laws. Lucknow: Eastern Book Co., 2011.

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Частини книг з теми "Detection of food adulteration":

Chen, Wenbo, Hui Li, Yong Wang, Pre De Silva, Benu Adhikari, and Bo Wang. "Advances in Technologies used in the Detection of Food Adulteration." In Biotechnological Approaches in Food Adulterants, 49–78. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429354557-3.

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Jabeur, Hazem, Akram Zribi, and Mohamed Bouaziz. "Detection of Extra Virgin Olive Oil Adulteration." In Olives and Olive Oil as Functional Foods, 537–53. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119135340.ch29.

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Özen, Banu, and Figen Tokatli. "Infrared Spectroscopy for the Detection of Adulteration in Foods." In Infrared and Raman Spectroscopy in Forensic Science, 593–602. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119962328.ch9d.

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Barman, Arpan, Amrita Namtirtha, Animesh Dutta, and Biswanath Dutta. "Food Safety Network for Detecting Adulteration in Unsealed Food Products Using Topological Ordering." In Intelligent Information and Database Systems, 451–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42058-1_38.

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Steenkamp, Paul A., Lucia H. Steenkamp, and Dalu T. Mancama. "Profiling of Botanical Extracts for Authentication, Detection of Adulteration and Quality Control Using UPLC-QTOF-MS." In Food Supplements Containing Botanicals: Benefits, Side Effects and Regulatory Aspects, 303–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62229-3_10.

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Karim, Norsuhada Abdul, and Ida Idayu Muhamad. "Detection Methods and Advancement in Analysis of Food and Beverages: A Short Review on Adulteration and Halal Authentication." In Proceedings of the 3rd International Halal Conference (INHAC 2016), 397–414. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7257-4_36.

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Azad, Tanzina, and Shoeb Ahmed. "Detection of Adulterations." In Handbook of Dairy Foods Analysis, 755–75. 2nd ed. Second edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9780429342967-41.

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Sanchez, 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.

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Sanchez, 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.

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Cozzolino, 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.

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Тези доповідей конференцій з теми "Detection of food adulteration":

Perumal, 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.

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Liu, Hong, Jie Cui, Ying Ma, Cuihong Dai, Dongjie Zhang, and Lili Qian. "Application of DNA fingerprint based on SSR in rice adulteration detection and origin traceability." In 2015 International Conference on Food Hygiene, Agriculture and Animal Science. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813100374_0012.

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Thazin, Yu, Tanthip Eamsa-Ard, Theerapat Pobkrut, and Teerakiat Kerdcharoen. "Formalin Adulteration Detection in Food Using E-nose based on Nanocomposite Gas Sensors." In 2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2019. http://dx.doi.org/10.1109/icce-asia46551.2019.8941601.

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Chen, 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.

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Clapper, 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.

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Ravindran, Ajith, Flavia Princess Nesamani, and D. Nirmal. "A Study on the use of Spectroscopic Techniques to Identify Food Adulteration." In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET). IEEE, 2018. http://dx.doi.org/10.1109/iccsdet.2018.8821197.

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Pasic-Juhas, E., L. C. Czegledi, A. Hodzic, A. Hrkovic-Porobija, and I. Bozic. "74. Determination of Travnik’s sheep cheese adulteration using the mPCR-method." In 14th Congress of the European Society for Agricultural and Food Ethics. The Netherlands: Wageningen Academic Publishers, 2018. http://dx.doi.org/10.3920/978-90-8686-869-8_74.

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Kulkarni, Shilpa, and Sujata Patrikar. "Fiber optic detection of kerosene adulteration in petrol." In INTERNATIONAL CONFERENCE ON PHOTONICS, METAMATERIALS & PLASMONICS: PMP-2019. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5120937.

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Brighty, S. Prince Sahaya, G. Shri Harini, and N. Vishal. "Detection of Adulteration in Fruits Using Machine Learning." In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2021. http://dx.doi.org/10.1109/wispnet51692.2021.9419402.

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Kanyathare, Boniphace, Buratin Khampirat, Kai Peiponen, and Boonsong Sutapun. "Rapid Detection of Variability and Adulteration of Diesel Oils." In Frontiers in Optics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/fio.2018.jw4a.125.

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Звіти організацій з теми "Detection of food adulteration":

Nelson, Matthew P., and Patrick J. Treado. Optical Detection of Biological and Chemical Threats in Food and Water. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada455251.

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Arani, P. Year 4 Report Multiplex Assay Development for Detection of Potential Bioterrorism Agents in Food Matrices. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1088435.

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Naraghi-Arani, Pejman, and Marc Beal. Highly Multiplexed Assays for Detection of Biothreat and Food Safety Agents Final Report CRADA No. TC02156.0. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1432973.

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Jorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset, and Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, June 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.

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Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.

Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.