Food safety interventions in low‐ and middle‐income countries in Asia: A systematic review

Kwoba, Emmah; Oduori, David Obiero; Lambertini, Elisabetta; Thomas, Lian F.; Grace, Delia; Mutua, Florence

doi:10.1111/zph.13028

Cited by 6 publications

(2 citation statements)

References 58 publications

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“…For example, a study of a food safety intervention among college students reported short-term success of food safety training interventions in improving the knowledge, attitudes, and practices of target beneficiaries [25]. Furthermore, a review of 25 food safety interventions evaluated in Asia indicated a focus on changes in knowledge, attitudes, and practices of intervention beneficiaries conducted using before-and-after studies (60%) and randomized controlled trials (40%) where 24 of the 25 interventions showed some degree of success [26]. The scholars identified a gap in determining intervention efficacy and cost-effectiveness and recommended research on factors of food safety intervention effectiveness and sustainability.…”

Section: Introductionmentioning

confidence: 99%

Drivers of Scale and Sustainability of Food Safety Interventions in Informal Markets: Lessons from the Tanzanian Dairy Sector

Kinyua,

Thebe

2023

Sustainability

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Food safety is critical but poorly addressed in African countries. A food safety training and certification intervention piloted in Kenya, India, Tanzania, and Nigeria was scaled and sustained in India but not the African countries. There is limited knowledge about how contextual factors facilitated or limited the scale and sustainability of the intervention in African countries. This research analysed the reach and contextual drivers of scale and sustainability of the intervention in Tanzania’s informal dairy sector four years post-implementation to draw lessons around the scale and sustainability of such interventions in African contexts. We utilized a convergence mixed method study design. We compiled data using document review, surveys with dairy traders, and key informant interviews with key dairy stakeholders. The intervention reach was limited. Critical incentives for traders and intervention implementers to engage with the intervention were lacking due to the absence of government commitment to support the intervention through policy. The traders and intervention implementers also lacked adequate capacities, compromising intervention delivery. For the intervention to achieve scale and sustainability in Tanzania and similar contexts, governments must be committed to food safety and provide enabling policy environments. The interventions must also consider the capacities of the beneficiaries and implementers.

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Section: Introductionmentioning

confidence: 99%

Drivers of Scale and Sustainability of Food Safety Interventions in Informal Markets: Lessons from the Tanzanian Dairy Sector

Kinyua,

Thebe

2023

Sustainability

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“…In recent years, a significant rise in food safety incidents, including melamine, plasticizers, contaminated starch, toxic soy sauce, recycled cooking oil, and tainted eggs, has brought about substantial threats and risks to human life and health [1][2][3]. As a result, there has been an increased emphasis on food safety.…”

Section: Introductionmentioning

confidence: 99%

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Ku,

Lung,

Chi

2023

Foods

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Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

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Food safety policy enforcement and associated actions reduce lead chromate adulteration in turmeric across Bangladesh

Forsyth

Baker

Nurunnahar

et al. 2023

Environmental Research

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Food safety interventions in low‐ and middle‐income countries in Asia: A systematic review

Cited by 6 publications

References 58 publications

Drivers of Scale and Sustainability of Food Safety Interventions in Informal Markets: Lessons from the Tanzanian Dairy Sector

Drivers of Scale and Sustainability of Food Safety Interventions in Informal Markets: Lessons from the Tanzanian Dairy Sector

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Food safety policy enforcement and associated actions reduce lead chromate adulteration in turmeric across Bangladesh

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