The Codex Alimentarius is the international organization created by the United Nations' Food and Agriculture Organization (FAO) and World Health Organization (WHO) to develop food standards, guidelines and other texts under the Joint FAO/WHO Food Standards Programme to protect the health of consumers and ensure fair practices in food trade. As a nongovernmental organization with observer status IFT participates in meetings of select Codex committees and task forces.
With the mandate of raising levels of nutrition, improving agricultural productivity, bettering the lives of rural populations, and contributing to the growth of the world economy, Food and Agriculture Organization (FAO) of the United Nation serves as a knowledge network, shares policy expertise, provides a neutral meeting forum for nations, and brings technical knowledge to the field.
As the directing and coordinating authority for health within the United Nations, the World Health Organization provides leadership on global health matters, shapes the health research agenda, sets norms and standards, articulates evidence-based policy options, provides technical support to countries, and monitors and assesses health trends.
Joint FAO/WHO Expert Committee on Food Additives (JECFA) is an international expert scientific committee administered jointly by FAO and WHO. JECFA serves as an independent scientific committee which performs risk assessments on food additives, contaminants, naturally occurring toxicants and residues of veterinary drugs in food and provides advice for the CAC and its committees.
The European Food Safety Authority (EFSA) is a scientific risk assessment body of the European Union, providing scientific advice on food and feed safety, nutrition, animal health, plant protection, and plant health.
The Food Chemicals Codex (FFC) is a compendium of internationally recognized standards for the authenticity, purity and identity of food ingredients. The compendium features about 1,100 monographs, including food-grade chemicals, processing aids, foods, flavoring agents, vitamins, and functional food ingredients, as well as information on topics such as adulteration, analytical methods and more.
The FCC plays a key role in safeguarding commerce and public health by providing essential criteria and analytical methods to authenticate and determine the quality of food ingredients. FCC standards are beneficial to all stakeholders in the food industry as agreed standards between suppliers and manufacturers aid in distinguishing genuine products from inferior or adulterated ingredients and substances, thereby helping to make the food supply chain safer and assuring consumers of the quality of the food products they consume.
The International Organization for Standardization (ISO) develops and publishes international standards relating to topics, including food ingredients, food safety and quality, and nanotechnology, through a network of national member standards institutes of 162 countries.
The Organisation for Economic Co-operation and Development's (OECD) is an international organization that works to build better policies for better lives. Its goal is to shape policies that foster prosperity, equality, opportunity, and well-being for all. Together with governments, policy makers, and citizens, the OECD works on establishing international norms.
Canadian Food Inspection Agency
The Canadian Food Inspection Agency's (CIFA) highest priority is mitigating risks to food safety, with the health and safety of Canadians the driving force behind the design and development of its programs. In collaboration and partnership with industry, consumers, and federal, provincial, and municipal organizations, CFIA works towards protecting Canadians from preventable health risks related to food and zoonotic diseases.
Food Standards Australia New Zealand (FANZ)
The Food Standards Australia New Zealand (FSANZ) is a statutory authority in the Australian Government Health Portfolio. FSANZ develops food standards for Australia and New Zealand.
Health Canada
Health Canada is responsible for helping Canadians maintain and improve their health, ensuring that high-quality health services are accessible, and working to reduce health risks.
U.K. Foods Standards Agency
The U.K. Food Standards Agency is an independent government department working across England, Wales, and Northern Ireland to protect public health and consumers’ wider interests in food.
United States Food and Drug Administration
The U.S. Food and Drug Administration (FDA) The FDA (Food and Drug Administration) ensures the safety, efficacy, and security of human and veterinary drugs, biological products, and medical devices, and ensures the safety of the nation’s food supply, cosmetics, and products that emit radiation. The FDA's role in food regulation involves ensuring the safety and proper labeling of the nation's food supply, excluding meat, poultry, and certain egg products which are regulated by the USDA. It sets standards, conducts inspections, enforces regulations, and responds to foodborne illness outbreaks to protect public health\.
United States Department of Agriculture
The U.S. Department of Agriculture provides leadership on food, agriculture, natural resources, rural development, nutrition, and related issues. The USDA develops and enforces regulations related to agricultural practices, food processing, and nutrition, ensuring the overall health and well-being of the public. Through its Food Safety and Inspection Service (FSIS), it oversees the safety, quality, and proper labeling of meat, poultry, and egg products.
Latest from IFT Scientific Journals
Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk
To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2–20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2 = 0.998 and RP 2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.
Ultrasound‐assisted extraction of sumac fruit oil and analysis of its fatty acid composition
The use of ultrasound for enhancing the extraction of sumac fruit oil was investigated to maximize the utilization of available sumac fruit resources. The optimal extraction parameters were determined using single-factor trials and Box–Behnken design optimization and were found to be a liquid-to-material ratio of 13:1 mL/g, an ultrasound temperature of 47°C, and an ultrasound duration of 32 min. These yielded a 20.59% extraction rate of the oil. The ultrasonic-assisted approach markedly increased the extraction rate and significantly reduced the extraction time when compared to classical Soxhlet extraction. The resulting sumac fruit oil had a brown-yellow color with peroxide, acid, and iodine concentrations of 2.43 mg/g, 0.44 g/100 g, and 135 g/100 g, respectively, and relative density (d20 20), and refractive index (20°C) values of 0.911, and 1.469, respectively. The primary fatty acids in the oil were stearic, linoleic, palmitic, linolenic, and oleic acids. With a 74.14% unsaturated fatty acid content, it has a high nutritional value as well as significant development and usage potential.
Research progress and application of pectin: A review
Pectin, an acidic polysaccharide, is naturally present primarily in the cell walls and inner layers of higher plants. Pectin is extensively used in food, pharmaceutical, cosmetic, and other industries owing to its exceptional attributes encompassing superior gelation, emulsification, antioxidant activity, stability, biocompatibility, and nontoxicity. Due to the increasing demand for pectin, there is a short supply in the domestic pectin market. Currently, the domestic production of pectin is heavily reliant on imports, thus emphasizing the urgent need to enhance its local manufacturing capabilities. Due to the diverse sources of pectin and variations in extraction and purification methods, its content, physicochemical properties, and biological activity are influenced, consequently impacting the market application of pectin. Therefore, this paper comprehensively reviews the extraction and purification process of pectin, in vivo metabolism, and biological activities (including antitumor, immunomodulatory, anti-inflammatory, antioxidant, hypoglycemic and hypolipidemic effects, antimicrobial properties, accelerated wound healing potential, promotion of gastrointestinal peristalsis, and alleviation of constipation as well as cholesterol-lowering effect). Furthermore, it explores the diverse applications of pectin in food science, biomedicine, and other interdisciplinary fields. This review serves as a valuable resource for enhancing the efficiency of pectin content improvement and exploring the potential value and application of pectin in a more scholarly and scientifically rigorous manner.
Modeling and chewing parameters calibration of Euryale ferox based on discrete element method
Euryale ferox was chosen for this study to examine its mechanical properties during chewing. Experiments and the discrete element method were used to conduct the study. Initially, the intrinsic and contact parameters of E. ferox were established through physical tests. The maximum compressive force of breakage and chewing mechanical properties (hardness, springiness, and chewiness) were measured using a texture profile analyzer (TPA). A Hertz–Mindlin with bonding model of the E. ferox was constructed. Optimal values for significant factors, including the normal stiffness per unit area, shear stiffness per unit area, and bonding radius, were obtained through single-factor, Plackett–Burman, steepest ascent, and Box–Behnken response surface tests, with the maximum compressive force as the index. Under optimal parameter combination conditions, the relative error between the simulated and experimental values of the maximum compressive force was 0.79%, and the relative errors between the simulated and TPA test values of all indicators were less than 5.65%. This study provides a valuable reference for simulating the textural characteristics of agricultural products.
Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers
A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56 × 56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in R v 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.
Latest News
Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk
To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2–20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2 = 0.998 and RP 2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.
Ultrasound‐assisted extraction of sumac fruit oil and analysis of its fatty acid composition
The use of ultrasound for enhancing the extraction of sumac fruit oil was investigated to maximize the utilization of available sumac fruit resources. The optimal extraction parameters were determined using single-factor trials and Box–Behnken design optimization and were found to be a liquid-to-material ratio of 13:1 mL/g, an ultrasound temperature of 47°C, and an ultrasound duration of 32 min. These yielded a 20.59% extraction rate of the oil. The ultrasonic-assisted approach markedly increased the extraction rate and significantly reduced the extraction time when compared to classical Soxhlet extraction. The resulting sumac fruit oil had a brown-yellow color with peroxide, acid, and iodine concentrations of 2.43 mg/g, 0.44 g/100 g, and 135 g/100 g, respectively, and relative density (d20 20), and refractive index (20°C) values of 0.911, and 1.469, respectively. The primary fatty acids in the oil were stearic, linoleic, palmitic, linolenic, and oleic acids. With a 74.14% unsaturated fatty acid content, it has a high nutritional value as well as significant development and usage potential.
Research progress and application of pectin: A review
Pectin, an acidic polysaccharide, is naturally present primarily in the cell walls and inner layers of higher plants. Pectin is extensively used in food, pharmaceutical, cosmetic, and other industries owing to its exceptional attributes encompassing superior gelation, emulsification, antioxidant activity, stability, biocompatibility, and nontoxicity. Due to the increasing demand for pectin, there is a short supply in the domestic pectin market. Currently, the domestic production of pectin is heavily reliant on imports, thus emphasizing the urgent need to enhance its local manufacturing capabilities. Due to the diverse sources of pectin and variations in extraction and purification methods, its content, physicochemical properties, and biological activity are influenced, consequently impacting the market application of pectin. Therefore, this paper comprehensively reviews the extraction and purification process of pectin, in vivo metabolism, and biological activities (including antitumor, immunomodulatory, anti-inflammatory, antioxidant, hypoglycemic and hypolipidemic effects, antimicrobial properties, accelerated wound healing potential, promotion of gastrointestinal peristalsis, and alleviation of constipation as well as cholesterol-lowering effect). Furthermore, it explores the diverse applications of pectin in food science, biomedicine, and other interdisciplinary fields. This review serves as a valuable resource for enhancing the efficiency of pectin content improvement and exploring the potential value and application of pectin in a more scholarly and scientifically rigorous manner.
Modeling and chewing parameters calibration of Euryale ferox based on discrete element method
Euryale ferox was chosen for this study to examine its mechanical properties during chewing. Experiments and the discrete element method were used to conduct the study. Initially, the intrinsic and contact parameters of E. ferox were established through physical tests. The maximum compressive force of breakage and chewing mechanical properties (hardness, springiness, and chewiness) were measured using a texture profile analyzer (TPA). A Hertz–Mindlin with bonding model of the E. ferox was constructed. Optimal values for significant factors, including the normal stiffness per unit area, shear stiffness per unit area, and bonding radius, were obtained through single-factor, Plackett–Burman, steepest ascent, and Box–Behnken response surface tests, with the maximum compressive force as the index. Under optimal parameter combination conditions, the relative error between the simulated and experimental values of the maximum compressive force was 0.79%, and the relative errors between the simulated and TPA test values of all indicators were less than 5.65%. This study provides a valuable reference for simulating the textural characteristics of agricultural products.
Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers
A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross-sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL-CNN, ML-CNN, and HL-CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56 × 56 input image size. Among the three types of CNN fusion models, the HL-CNN obtained the best performance, resulting in R v 2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry.