No description available.

Seafood substitution is a worldwide problem due to factors such as limited monitoring coupled with complex supply chains. Red snapper (Lutjanus campechanus) is a highly valued and overfished species that is commonly substituted with other fish, such as tilapia, rockfish, and other snapper species. DNA barcoding is typically used by regulatory agencies to detect seafood substitution; however, it is expensive and time-consuming. A rapid, real-time PCR assay targeting red snapper was developed previously for use in fisheries management; however, it has not been tested for its ability to detect red snapper species substitution. The objective of this study was to assess the ability of the real-time PCR assay to identify red snapper fillets and differentiate red snapper from common substitute fish species in combination with DNA barcoding. A total of 21 fresh/frozen fillets labeled as “red snapper” were tested with real-time PCR, along with 57 fresh/frozen fillets representing 15 of the most common categories of fish mislabeled as red snapper. All samples were tested with DNA barcoding to confirm the identity of fish species. Real-time PCR parameters were optimized to reduce background signals associated with cross-reactivity. Overall, real-time PCR identified 4 samples as red snapper: 3 were authenticated as red snapper with DNA barcoding and 1 was identified as mahi-mahi. Overall, 40% of all samples and 91% of “red snapper” samples were considered mislabeled according to DNA barcoding. Red snapper was substituted with other snapper species (e.g., Lutjanus malabaricus, Lutjanus peru, Ocyurus chrysurus, and Rhomboplites aurorubens), rockfish (Sebastes flavidus and Sebastes brevispinis), sea bream (Pagrus major/Pagrus auratus), and mahi-mahi (Coryphaena hippurus). The real-time PCR assay tested in this study can serve as a rapid screening test for the detection of mislabeled species, which can then be confirmed with sequencing techniques. This species identification technique has the potential to be used by regulatory agencies to rapidly determine the authenticity of red snapper on-site.

Machine learning has become one of the most emerging topics in a lot of research areas, such as pervasive and ubiquitous computing. Such computing applications always rely on the supervised learning approach to recognize user’s context before a suitable level of services are provided. However, since more and more users are involved in modern applications, the monitored data cannot be guaranteed to be always true due to wrong information. This may cause the mislabeling in machine learning and so affects the prediction. The goal of this Ph.D. thesis is to improve the data quality and solve the mislabeling problem caused by considering non-expert users. To achieve this goal, we propose a novel algorithm, called Skeptical Learning, aiming at interacting with the users and filtering out anomalies when an invalid input is monitored. This algorithm guarantees the machine to use the pre-known knowledge to check the availability of its own prediction as well as the label provided by the users. This thesis clarifies how we design this algorithm and makes three main contributions: (i.) we study the predictability of human behavior through the notion of personal context; (ii.)we design and develop Skeptical Learning as a paradigm to deal with the unreliability of users when providing non-confidential labels that describe their personal context; (iii.) we introduce an MCS platform where we implement Skeptical Learning on top of it to solve unreliable labels issue. Our evaluations have shown that Skeptical Learning could be widely used in pervasive and ubiquitous computing applications to better understand the quality of the data relying on the machine knowledge, and thus prevent mislabeling problem due to non-expert information.

No description available.

While the presence of ≥1% of an undeclared species in ground meat generally used as an indicator of intentional mislabeling as opposed to cross-contamination, the actual percent of undeclared species resulting from cross-contamination has not been experimentally determined. The objective of this study was to quantify the effect of sanitation procedures on the crosscontamination of animal species in ground meat products, using undeclared pork in ground beef. Pork (13.6 kg) was processed using a commercial grinder, then one of three sanitation treatments was completed (“no cleaning”, “partial cleaning”, or “complete cleaning”). Next, beef (13.6 kg) was ground using the same equipment. For “no cleaning,” beef was ground immediately after pork without any cleaning step; for “partial cleaning,” the hopper tray was wiped, and excess meat was taken out from the auger; for “complete cleaning,” all parts of the grinder were disassembled and thoroughly cleaned with water and soap. A 100-g sample was collected for each 0.91 kg (2 lb) of beef processed with the grinder and each sanitation treatment was tested twice. Real-time polymerase chain reaction (PCR) was used to quantify pork in ground beef. For “no cleaning,” the first 100-g sample of ground beef run through the grinder contained 24.42 ± 10.41% pork, while subsequent samples contained

Mislabeling of ground meat products is a form of food fraud that can lead to economic deception and interfere with dietary restrictions related to allergens or religious beliefs. In various parts of the world, including Ireland, Mexico and Turkey, high levels of meat mislabeling have been reported between 2000-2015. However, there is currently a lack of information regarding this practice in the United States. Therefore, the objective of this study was to test a variety of ground meat products sold on the U.S. commercial market for the presence of potential mislabeling. Forty-eight ground meat samples were purchased from online and local retail sources, including both supermarkets and specialty meat retailers. DNA was extracted from each sample in duplicate and tested using DNA barcoding of the cytochrome c oxidase subunit I (COI) gene. The resulting sequences were identified at the species level using the Barcode of Life Database (BOLD). Any samples that failed DNA barcoding went through repeat extraction and sequencing. Due to the possibility of a species mixture, these samples were also tested with real-time polymerase chain reaction (PCR) targeting beef, chicken, lamb, turkey, pork and horse. Of the 48 products analyzed in this study, 10 were found to be mislabeled, with nine containing multiple meat species. Meat samples purchased from online specialty meat distributors had a higher rate of being mislabeled (35%) compared to samples purchased from a local butcher (18%) and samples purchased at local vii supermarkets (5.8%). Horsemeat, which is illegal to sell on the U.S. commercial market, was detected in two of the samples acquired from online specialty meat distributors. Overall, the mislabeling detected in this study appears to be due to reasons such as intentional mixing of lower-cost meat species into higher cost products or unintentional mixing of meat species due to cross-contamination during processing.