Composition, color and mechanical characteristics of pretreated candied chestnuts

Raw-peeled chestnuts were treated with citric acid or sodium metabisulphite, steamed, and dipped into sugar solutions containing dextrose and sucrose, or dextrose and fructose. Composition, mineral content, weight change, rheological properties, and color were measured at each step. Carbohydrate content increased during processing. Candied chestnuts were low in protein (1.31-1.35%) and lipids (0.29-0.78%) but high in carbohydrates (73.48-76.13%). Their mineral concentrations were: Ca 19.08-46.70, Cu 0.19-0.52, Fe 0.88-1.98, K 180.5-659.1, Mg 26.83-69.57, Mn 0.70-2.42, Zn 1.51-6.95 mg/100 g sample. Rheological properties were affected by processing steps. Dipping into sugar solutions did not affect rheological properties. Color changes were quantified, and average L*, a*, and b*values measured

Odor evaluation of shrimp treated with different chemicals using an electronic nose and a sensory panel

An electronic nose with 12 conducting polymer sensors was used to measure odors of raw shrimp treated with different chemicals. Headless shell-on pink shrimp (Pandalus jordani) were treated with bleach (0, 25, 50, 100 and 200 ppm), phosphates (0, 2, 4 and 6% w/v) and sulfites (0, 0.75, 1.25 and 2% w/v) and stored at 2°C for 48 hours. Odors were evaluated by sensory panels and an electronic nose. Aerobic plate counts were performed. Discriminant function analysis was used as the pattern recognition technique to differentiate samples based on odors. Results showed that the electronic nose could discriminate differences in odor due to chemicals present in shrimp. The correct classification rates for bleach, phosphate and sulfite treated shrimp were 92.7, 95.8, and 99.2%, respectively

Quality evaluation of Alaska pollock (Theragra chalcogramma) roe by image analysis. Part I: Weight prediction

Roe is an important product of the Alaska pollock (Theragra chalcogramma) industry. About 31% of the value for all pollock products comes from roe, yet roe is 5% of the weight of the fish. Currently, the size (weight), color, and maturity of the roe are subjectively evaluated. The objective of this study was to develop methods to predict the weight of Alaska pollock roe based on its view area from a camera and to differentiate between single and double roes. One hundred and forty-two pollock roes were picked from a processing line in a Kodiak, AK plant. Each roe was weighed, placed in a light box equipped with a digital video camera, images were taken at two different angles from one side, then turned over and presented at two different angles again (four images for each roe). A reference square of known surface area was placed by the roe. The following equations were used to fit the view area (X) versus weight (Y) data: linear, power, and second-order polynomial. Error rates for the classification of roes by weight decreased significantly when weight prediction equations for single and double roes were developed separately. A turn angle method, a box method, and a modified box method were tested to differentiate single and double roes by image analysis. Machine vision can accurately determine the weight of pollock roe. Practical Application Abstract: An image analysis method to accurately determine if pollock roe is a single or a double was developed. Then view area versus weight correlations were found for single and double roes that reduced incorrect weight classification rates to half that of human graders. © 2012 Copyright Taylor and Francis Group, LLC

Prediction of the weight of Alaskan Pollock using image analysis

Determining the size and quality attributes of fish by machine vision is gaining acceptance and increasing use in the seafood industry. Objectivity, speed, and record keeping are advantages in using this method. The objective of this work was to develop the mathematical correlations to predict the weight of whole Alaskan Pollock (Theragra chalcogramma) based on its view area from a camera. One hundred and sixty whole Pollock were obtained fresh, within 2 d after catch from a Kodiak, Alaska, processing plant. The fish were first weighed, then placed in a light box equipped with a Nikon D200 digital camera. A reference square of known surface area was placed by the fish. The obtained image was analyzed to calculate the view area of each fish. The following equations were used to fit the view area (X) compared with weight (Y) data: linear, power, and 2nd-order polynomial. The power fit (Y = A·XB) gave the highest R2 for the fit (0.99). The effect of fins and tail on the accuracy of the weight prediction using view area were evaluated. Removing fins and tails did not improve prediction accuracy. Machine vision can accurately predict the weight of whole Pollock. © 2010 Institute of Food Technologists®

Determination of volume of alaska pollock (Theragra chalcogramma) by image analysis

The objective of this study was to develop two methods to predict the volume of whole Alaska pollock and to compare the results with the experimentally measured volumes. One hundred fifty-five whole pollock, obtained from a Kodiak processor, were individually immersed in a graduated cylinder equipped with an outflow tube to catch the displaced water as a result of immersion. The weight of the water was recorded. Then the fish were placed in a light box equipped with a digital video camera, and the side view and top view recorded (2 images for each fish). A reference square of known surface area was placed by the fish. A cubic spline method to predict volume by integration of cross-sectional area slices based on the top and side views and an empirical equation using dimensional (length L, width W, depth D) measurements at three locations of the fish image were developed. The R 2 value for the correlation between the L × W × D versus measured volume was 0.987. The best R 2 for the correlation of the predicted volume by the cubic spline method versus the measured volume was 0.99. Image analysis can be used reliably to predict the volume of whole Alaska pollock. © Taylor & Francis Group, LLC.University of Alaska Fairbank

Time delays in each step from symptom onset to treatment in acute myocardial infarction: Results from a nation-wide TURKMI registry

Objective: In this study, we aimed to analyze the TURKMI registry to identify the factors associated with delays from symptom onset to treatment that would be the focus of improvement efforts in patients with acute myocardial infarction (AMI) in Turkey. Methods: The TURKMI study is a nation-wide registry that was conducted in 50 centers capable of 24/7 primary percutaneous coronary intervention (PCI). All consecutive patients (n=1930) with AMI admitted to coronary care units within 48 hours of symptom onset were prospectively enrolled during a predefined 2-week period between November 1, 2018, and November 16, 2018. All the patients were examined in detail with regard to the time elapsed at each step from symptom onset to initiation of treatment, including door-to-balloon time (D2B) and total ischemic time (TIT). Results: After excluding patients who suffered an AMI within the hospital (2.6%), the analysis was conducted for 1879 patients. Most of the patients (49.5%) arrived by self-transport, 11.8% by emergency medical service (EMS) ambulance, and 38.6% were transferred from another EMS without PCI capability. The median time delay from symptom-onset to EMS call was 52.5 (15-180) min and from EMS call to EMS arrival 15 (10-20) min. In ST-segment elevation myocardial infarction (STEMI), the median D2B time was 36.5 (25-63) min, and median TIT was 195 (115-330) min. TIT was significantly prolonged from 151 (90-285) min to 250 (165-372) min in patients transferred from non-PCI centers. The major significant factors associated with time delay were patient-related delay and the mode of hospital arrival, both in STEMI and non-STEMI. Conclusion: The baseline evaluation of the TURKMI study revealed that an important proportion of patients presenting with AMI within 48 hours of symptom onset reach the PCI treatment center later than the time proposed in the guidelines, and the use of EMS for admission to hospital is extremely low in Turkey. Patient-related factors and the mode of hospital admission were the major factors associated with the time delay to treatment

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)