The aim of this study is to apply the Nutrient Analysis Critical Control Point (NACCP) process to ensure that the highest nutrient levels in food can determine a beneficial effect on the health of the consumer. The NA...The aim of this study is to apply the Nutrient Analysis Critical Control Point (NACCP) process to ensure that the highest nutrient levels in food can determine a beneficial effect on the health of the consumer. The NACCP process involves a sequence of analysis and controls that depart from raw material production to the evaluation of the effect of nutrition on health. It is articulated through the following points: 1) identification of nutrient level in the food;2) identification of critical control points (environmental, genetic data, chemical and physical data, production technology, distribution and administration);3) establishing critical limits that can impoverish and damage the nutrient;4) establishing measures to monitor;5) establishing corrective actions. We selected as biomarkers the total phenolic content (TPC) and total antioxidant capacity (TAC) of a genotyped Italian hazelnut cultivars (Corylus e avellana L.). We performed a clinical study evaluating: a) nutritional status;b) clinical-biochemical parameters;c) low density lipoprotein oxidation (LDL-ox);d) the expression level changes of oxidative stress pathway genes in the blood cell at baseline and after 40 g/die of hazelnut consumption. In this study, we found a significant lowering (p ≤ 0.005) of LDL oxidized proteins, in association with the consumption of 40 g/d of hazelnuts. Also, we found a significant variation (p ≤ 0.005) of gene expression of antioxidant and pro-oxidant genes, between the intake of dietary with and without hazelnuts. This results support the hypothesis that the NACCP process could be applied to obtain significant benefits in terms of primary prevention and for contributing to the amelioration of food management at the consumer level.展开更多
Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to en...Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to enhance farm management,increasing productivity and profitability.For instance,it allows for non-intrusive health assessments,contributing to maintaining a healthy herd while reducing stress associated with handling.In the poultry sector,image analysis can be utilised to monitor and analyse the behaviour of each hen in real time.Researchers have recently used machine learning algorithms to monitor the behaviour,health,and positioning of hens through computer vision techniques.Convolutional neural networks,a type of deep learning algorithm,have been utilised for image analysis to identify and categorise various hen behaviours and track specific activities like feeding and drinking.This research presents an automated system for analysing laying hen movement using video footage from surveillance cameras.With a customised implementation of object tracking,the system can efficiently process hundreds of hours of videos while maintaining high measurement precision.Its modular implementation adapts well to optimally exploit the GPU computing capabilities of the hardware platform it is running on.The use of this system is beneficial for both real-time monitoring and post-processing,contributing to improved monitoring capabilities in precision livestock farming.展开更多
文摘The aim of this study is to apply the Nutrient Analysis Critical Control Point (NACCP) process to ensure that the highest nutrient levels in food can determine a beneficial effect on the health of the consumer. The NACCP process involves a sequence of analysis and controls that depart from raw material production to the evaluation of the effect of nutrition on health. It is articulated through the following points: 1) identification of nutrient level in the food;2) identification of critical control points (environmental, genetic data, chemical and physical data, production technology, distribution and administration);3) establishing critical limits that can impoverish and damage the nutrient;4) establishing measures to monitor;5) establishing corrective actions. We selected as biomarkers the total phenolic content (TPC) and total antioxidant capacity (TAC) of a genotyped Italian hazelnut cultivars (Corylus e avellana L.). We performed a clinical study evaluating: a) nutritional status;b) clinical-biochemical parameters;c) low density lipoprotein oxidation (LDL-ox);d) the expression level changes of oxidative stress pathway genes in the blood cell at baseline and after 40 g/die of hazelnut consumption. In this study, we found a significant lowering (p ≤ 0.005) of LDL oxidized proteins, in association with the consumption of 40 g/d of hazelnuts. Also, we found a significant variation (p ≤ 0.005) of gene expression of antioxidant and pro-oxidant genes, between the intake of dietary with and without hazelnuts. This results support the hypothesis that the NACCP process could be applied to obtain significant benefits in terms of primary prevention and for contributing to the amelioration of food management at the consumer level.
基金National Research Centre and received funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)—MISSIONE 4 COMPONENTE 2,INVESTIMENTO 1.4—D.D.103217/06/2022,CN00000022).
文摘Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to enhance farm management,increasing productivity and profitability.For instance,it allows for non-intrusive health assessments,contributing to maintaining a healthy herd while reducing stress associated with handling.In the poultry sector,image analysis can be utilised to monitor and analyse the behaviour of each hen in real time.Researchers have recently used machine learning algorithms to monitor the behaviour,health,and positioning of hens through computer vision techniques.Convolutional neural networks,a type of deep learning algorithm,have been utilised for image analysis to identify and categorise various hen behaviours and track specific activities like feeding and drinking.This research presents an automated system for analysing laying hen movement using video footage from surveillance cameras.With a customised implementation of object tracking,the system can efficiently process hundreds of hours of videos while maintaining high measurement precision.Its modular implementation adapts well to optimally exploit the GPU computing capabilities of the hardware platform it is running on.The use of this system is beneficial for both real-time monitoring and post-processing,contributing to improved monitoring capabilities in precision livestock farming.
