Salma Mohamad Yusop, Mohd Azri Azman, Premy Puspitawati Rahayu, Nurul Huda
The authors wish to report the following corrections to the above-mentioned article. A post-publication audit of the reference list and in-text citations identified multiple citation errors organised into four parts below: (I) removal of an unverifiable reference, (II) removal of unverifiable in-text citations, and (III) correction of incorrect bibliographic details. PART I – Removal of Unverifiable Reference: USDA-FSIS (2020). One reference cited throughout the manuscript—“USDA-FSIS (United States Department of Agriculture – Food Safety and Inspection Service). (2020). Compliance guideline for dry-aged beef. Washington, DC: USDA-FSIS”—has been identified as unverifiable. This document could not be located as a published FSIS guideline. The reference has been removed from the reference list, and all eight in-text citations have been corrected as follows. Correction I-1 – Section 2, Page 2 (Processing Principles and Control). Original text: “Systematic application of HACCP to dry-aging requires hazard analysis (Principle 1) to identify biological, chemical, and physical hazards, followed by CCP designation (Principle 2) at critical process steps such as raw material reception and chamber environmental control (USDA-FSIS, 2020).” Corrected text: “… followed by CCP designation (Principle 2) at critical process steps such as raw material reception and chamber environmental control (Codex Alimentarius, 2005; Koutsoumanis et al., 2023).” Correction I-2 – Section 2, Page 2 (Processing Principles and Control). Original text: “… where deviation above 4 °C for extended periods creates irreversible pathogen proliferation risk (Codex Alimentarius, 2005; Koutsoumanis et al., 2023; USDA-FSIS, 2020).” Corrected text: “… where deviation above 4 °C for extended periods creates irreversible pathogen proliferation risk (Codex Alimentarius, 2005; Koutsoumanis et al., 2023).” Correction I-3 – Section 4.2, Page 7 (Pathogens and Spoilage Organisms of Concern). Original text: “The main microbiological hazards in dry-aged beef include Listeria monocytogenes, Salmonella spp., Escherichia coli O157:H7, and toxigenic Staphylococcus aureus (Koutsoumanis et al., 2023; USDA-FSIS, 2020).” Corrected text: “The main microbiological hazards in dry-aged beef include Listeria monocytogenes, Salmonella spp., Escherichia coli O157:H7, and toxigenic Staphylococcus aureus (Koutsoumanis et al., 2023; Campbell et al., 2023).” Correction I-4 – Section 4.2, Page 7 (Pathogens and Spoilage Organisms of Concern). Original text: “MLA (2016) and USDA-FSIS (2020) both emphasise validated pre-aging decontamination measures such as carcass washing, organic acid sprays, and UV-C treatment, all with the aim to ensure compliance with HACCP prerequisites.” Corrected text: “MLA (2016) and Koutsoumanis et al. (2023) both emphasise validated pre-aging decontamination measures such as carcass washing, organic acid sprays, and UV-C treatment, all with the aim to ensure compliance with HACCP prerequisites.” Correction I-5 – Section 4.3, Page 7 (Environmental and Process Hygiene). Original text: “Facility sanitation and air-quality management are key determinants of microbial control (USDA-FSIS, 2020).” Corrected text: “Facility sanitation and air-quality management are key determinants of microbial control (Koutsoumanis et al., 2023; Campbell et al., 2023).” Correction I-6 – Section 4.3, Page 7 (Environmental and Process Hygiene). Original text: “Integrating microbiological data into the facility's HACCP plan ensures continuous verification of process hygiene (USDA-FSIS, 2020).” Corrected text: “Integrating microbiological data into the facility's HACCP plan ensures continuous verification of process hygiene (Koutsoumanis et al., 2023; MLA, 2016).” Correction I-7 – Section 4.4, Page 7 (HACCP and Regulatory Frameworks). Original text: “Koutsoumanis et al. (2023) and USDA-FSIS (2020) similarly outline validated control measures, documentation standards, and environmental hygiene criteria adopted in commercial facilities.” Corrected text: “Koutsoumanis et al. (2023) and MLA (2016) similarly outline validated control measures, documentation standards, and environmental hygiene criteria adopted in commercial facilities.” Correction I-8 – Table 3, Page 8 (Critical Control Points). Original text: In Table 3, the “Receiving temp” control point row cites “USDA-FSIS (2020)” as the sole reference. Corrected text: The reference for the “Receiving temp” control point row in Table 3 is corrected to “Koutsoumanis et al. (2023); MLA (2016).” PART II – Removal of Unverifiable In-Text Citations. Two additional in-text citations reference sources that could not be verified through scholarly databases and do not appear in the published reference list. The associated text has been revised as follows. Correction II-1 – Section 4.5 (Predictive Modelling and Microbial Risk Assessment). Original text: “Koutsoumanis et al. (2023) have emphasized coupling predictive models with continuous process monitoring for aging facilities. A practical example is the study by Li and Zhou (2021), where environmental sensors in pilot-scale dry-aging rooms automatically triggered airflow and humidity adjustments when water-activity data exceeded preset thresholds, effectively stabilizing surface microbial counts below 6 log CFU cm-2. Collectively, these examples demonstrate that predictive analytics can transform process verification from static to dynamic control. When coupled with real-time data, automated predictive systems can pre-empt deviations such as excessive dehydration or microbial overgrowth through targeted interventions which include humidity correction and air-speed modulation.” Corrected text: No publication by “Li and Zhou (2021)” on environmental sensors in dry-aging rooms could be identified in any scholarly database. “Koutsoumanis et al. (2023) emphasized the importance of coupling predictive microbiological models with continuous process monitoring in aging facilities. In dry-aging systems, this approach can be operationalized through the integration of real-time environmental monitoring of temperature, relative humidity, airflow, and surface water activity. By continuously evaluating these parameters, process conditions may be dynamically adjusted to reduce the risk of excessive dehydration and microbial proliferation. Collectively, such data-driven strategies illustrate how predictive analytics can shift process verification from static compliance toward dynamic, risk-based control, enabling timely interventions such as humidity correction and airflow modulation to maintain product stability and safety.” Correction II-2 – Section 4.5 (Predictive Modelling and Microbial Risk Assessment). Original text: “Machine-learning integration with sensor data has been validated in real meat-aging systems. Zhou et al. (2022) developed deep learning models for real-time monitoring of psychrophilic spoilage bacteria (Pseudomonas and Lactobacillus) in chilled beef, achieving high prediction accuracy (R2 >0.90) using hyperspectral imaging combined with time–temperature data. Similarly, Kim et al. (2022)reported a digital twin model combining continuous humidity sensors and infrared surface temperature mapping to predict crust formation and microbial load dynamics in Hanwoo dry-aging chambers, providing near real-time risk scores for deviation alerts.” Corrected text: This sentence is deleted. No publication by “Zhou et al. (2022)” matching this description could be identified. The reference does not appear in the published reference list. “Machine-learning approaches combined with hyperspectral imaging have been increasingly explored for non-destructive prediction of microbial load and quality attributes in chilled beef. Several studies have demonstrated high predictive performance (R2 > 0.90) when spectral data are integrated with time–temperature information to estimate total viable counts and spoilage progression. These advances illustrate the potential of data-driven modelling frameworks to support real-time decision-making in meat aging systems, particularly when coupled with continuous environmental monitoring platforms.” PART III – Correction of Incorrect Bibliographic Details in the Reference List. Three entries in the published reference list contain incorrect bibliographic details including wrong journal names, years, volumes, or page numbers. These are corrected as follows. Correction III-1 – Álvarez et al. (2021). As published: Álvarez, S., Mullen, A. M., Hamill, R., O'Neill, E., & Álvarez, C. (2021). Dry-aging of beef as a tool to improve meat quality: A study on Limousin × Friesian steers. Irish Journal of Agricultural and Food Research 60(1), 120–131. Corrected to: Álvarez, S., Mullen, A. M., Hamill, R., O'Neill, E., & Álvarez, C. (2021). Dry-aging of beef as a tool to improve meat quality. Impact of processing conditions on the technical and organoleptic meat properties. Advances in Food and Nutrition Research, 95, 97–130. https://doi.org/10.1016/bs.afnr.2020.10.001. Correction III-2 – Lee et al. (2018). As published: Lee, H. J., Yoon, J. W., Kim, M., Oh, H., Yoon, Y., & Jo, C. (2018). Influences of different aging conditions on the quality characteristics of beef loins. Korean Journal of Food Science of Animal Resources, 38(6), 1131–1143. Corrected to: Lee, H. J., Choe, J., Yoon, J. W., Kim, S., Oh, H., Yoon, Y., & Jo, C. (2018). Determination of salable shelf-life for wrap-packaged dry-aged beef during cold storage. Korean Journal of Food Science of Animal Resources, 38(2), 251–258. https://doi.org/10.5851/kosfa.2018.38.2.251. Correction III-3 – Kim, Frandsen & Rosenvold (2018). As published: Kim, Y. H. B., Frandsen, M., & Rosenvold, K. (2018). Effect of aging prior to freezing on color stability, lipid oxidation and protein oxidation of bovine longissimus muscle. Meat Science, 145, 204–211. Corrected to: Kim, Y. H. B., Frandsen, M., & Rosenvold, K. (2011). Effect of ageing prior to freezing on colour stability of ovine longissimus muscle. Meat Science, 88(3), 332–337. https://doi.org/10.1016/j.meatsci.2010.12.020. Reference List Corrections – Summary. Removed (1 entry): USDA-FSIS (United States Department of Agriculture – Food Safety and Inspection Service). (2020). Compliance guideline for dry-aged beef. Washington, DC: USDA-FSIS. Corrected (3 entries): (a) Álvarez et al. (2021): Journal corrected from Irish Journal of Agricultural and Food Research to Advances in Food and Nutrition Research; volume corrected to 95; pages corrected to 97–130; DOI added (10.1016/bs.afnr.2020.10.001). (b) Lee et al. (2018): Title, author list, volume, and pages corrected. See Correction III-2 for details. (c) Kim, Frandsen & Rosenvold: Year corrected from (2018) to (2011); title, species, volume, and pages corrected. See Correction III-3 for details. All replacement citations for Part I (Koutsoumanis et al., 2023; Campbell et al., 2023; MLA, 2016; Codex Alimentarius, 2005) are already included in the published reference list and require no new additions. The authors sincerely apologise for these errors and confirm that they do not affect the validity of the core scientific content, conclusions, or recommendations presented in the article. The quality of the review, the integrity of the data synthesis, and the practical guidance offered for dry-aging process control remain unchanged. © 2026 Elsevier Ltd
Department of Food Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Bangi, 43600, Malaysia; Livestock Science Research Center, Malaysian Agricultural Research and Development Institute, Persiaran MARDI-UPM, Selangor, Serdang, 43400, Malaysia; Faculty of Animal Science, Universitas Brawijaya, East Java, Malang, Indonesia; Postgraduate School, Universitas Brawijaya, East Java, Malang, Indonesia
