Advancing Food Safety in Bangladesh: Challenges and the Promise of Smart Sensor Technology DOI Creative Commons
Md Wadud Ahmed, Mohammed Kamruzzaman

Food Safety and Health, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

ABSTRACT Food safety is a critical public health concern for preventing foodborne illnesses and ensuring consumer protection. hazards may present throughout the food supply chain, from farm to fork, posing significant risks. This comprehensive review explored prevalent in Bangladesh highlighted smart sensor technologies hazard detection. By reviewing recent literature on Bangladeshi web, this study discusses potential consequences of these their detection methods. Finally, evaluation existing challenges sensor‐based techniques are provided. Bacterial pathogens, agrochemical residues, toxic preservatives, adulteration highly chain. The key country lack awareness, unhygienic practices handling preparation, multiplicity laws coordination among regulatory authorities, bureaucratic complexities, inadequate infrastructure skilled human resources. Smart offers promising solution limitations conventional determination techniques, providing rapid accurate results with low cost, portability, ease operation, thereby significantly enhancing country’s scenario. help policymakers, academicians better understand chain develop more effective strategies mitigating risks, safety, health.

Language: Английский

Nondestructive Prediction of Eggshell Thickness Using NIR Spectroscopy and Machine Learning with Explainable AI DOI
Md Wadud Ahmed,

S. Sharar Alam,

Alin Khaliduzzaman

et al.

ACS Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

3
Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry DOI Creative Commons
Md Wadud Ahmed, Carlos Esquerre, Kristen K. Eilts

et al.

Results in Chemistry, Journal Year: 2025, Volume and Issue: 13, P. 102016 - 102016

Published: Jan. 1, 2025

Language: Английский

Citations

1
Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning DOI Creative Commons

Md Wadud Ahmed,

Asher Sprigler,

J.L. Emmert

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100857 - 100857

Published: Feb. 1, 2025

Language: Английский

Citations

1
Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning DOI Creative Commons
Md Wadud Ahmed,

Asher Sprigler,

J.L. Emmert

et al.

Food Control, Journal Year: 2025, Volume and Issue: unknown, P. 111233 - 111233

Published: Feb. 1, 2025

Language: Английский

Citations

0
A systematic review of explainable artificial intelligence for spectroscopic agricultural quality assessment DOI Creative Commons
Md. Toukir Ahmed,

Md Wadud Ahmed,

Mohammed Kamruzzaman

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110354 - 110354

Published: April 4, 2025

Language: Английский

Citations

0
Non‐destructive measurement of eggshell strength using NIR spectroscopy and explainable artificial intelligence DOI Creative Commons

Md Wadud Ahmed,

S. Sharar Alam,

Alin Khaliduzzaman

et al.

Journal of the Science of Food and Agriculture, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Abstract Background Eggshell strength is crucial for ensuring high‐quality eggs, reducing breakage during handling, and meeting consumer expectations freshness integrity. Conventional methods of eggshell measurement are often destructive, time‐consuming unsuitable large‐scale applications. This study evaluated the potential near‐infrared (NIR) spectroscopy combined with explainable artificial intelligence (AI) as a rapid, non‐destructive method determining strength. Various multivariate analysis techniques were explored to enhance prediction accuracy, including spectral pre‐processing variable selection methods. Results Principal component partial least squares discriminant effectively classified eggs based on threshold shell 30 N. Regression models, regression, random forest (RF), light gradient boosting machine K‐nearest neighbors, evaluated. Using only 14 selected variables, RF model achieved very good performance 0.83, root mean square error 1.49 N ratio deviation 2.44. The Shapley additive explanation approach provided insights into contributions, enhancing model's interpretability. Conclusion demonstrated that NIR spectroscopy, integrated AI, robust, environmentally sustainable prediction. innovative holds significant optimizing resource utilization quality control in egg industry. © 2025 Author(s). Journal Science Food Agriculture published by John Wiley & Sons Ltd behalf Society Chemical Industry.

Language: Английский

Citations

0
Integration of Hyperspectral Imaging System and Machine Learning to Predict Amylose Content in Rice DOI

Mahsa Edris,

Sajad Kiani, Mahdi Ghasemi‐Varnamkhasti

et al.

Cereal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

ABSTRACT Background and Objectives This study evaluates the capability of a hyperspectral imaging (HSI) system combined with machine learning techniques as rapid non‐destructive technology to predict percentage amylose content in rice. Ninety pure rice samples were procured from different geographical origins Iran. The scanned using HSI then their concentration was determined (based on ISO 6647‐2) create reference database. Findings Spectral data pre‐processed MSC SG algorithms fed PCA for reduction. Next, four methods, PLSR, SVR, MLP, RBF, applied samples. Results showed that predicted PLSR values R 2 val = 0.929, RMSE p 0.006, SVR 0.971, 0.43, 0.976, RMSEP 0.0038, 0.95, 0.014, respectively. Conclusions artificial intelligence algorithms, MLP have similar but better results than methods. Therefore, provided satisfactory results. Significance Novelty findings this will inform supply chains could be used reliable, out‐lab, fast method predicting

Language: Английский

Citations

0
Advancing Food Safety in Bangladesh: Challenges and the Promise of Smart Sensor Technology DOI Creative Commons
Md Wadud Ahmed, Mohammed Kamruzzaman

Food Safety and Health, Journal Year: 2025, Volume and Issue: unknown

Published: April 20, 2025

ABSTRACT Food safety is a critical public health concern for preventing foodborne illnesses and ensuring consumer protection. hazards may present throughout the food supply chain, from farm to fork, posing significant risks. This comprehensive review explored prevalent in Bangladesh highlighted smart sensor technologies hazard detection. By reviewing recent literature on Bangladeshi web, this study discusses potential consequences of these their detection methods. Finally, evaluation existing challenges sensor‐based techniques are provided. Bacterial pathogens, agrochemical residues, toxic preservatives, adulteration highly chain. The key country lack awareness, unhygienic practices handling preparation, multiplicity laws coordination among regulatory authorities, bureaucratic complexities, inadequate infrastructure skilled human resources. Smart offers promising solution limitations conventional determination techniques, providing rapid accurate results with low cost, portability, ease operation, thereby significantly enhancing country’s scenario. help policymakers, academicians better understand chain develop more effective strategies mitigating risks, safety, health.

Language: Английский

Citations

0