Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage
Models that predict bacterial growth in food products can help industry with decision-making with regard to microbial food spoilage. Such models have recently been developed using machine learning (ML) rather than a mechanistic understanding of bacterial growth. Thus, our aim was to compare the perf...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Journal of Food Protection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X24002011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841553867437768704 |
|---|---|
| author | Sriya Sunil Sarah I. Murphy Ruixi Chen Wei Chen Joseph Guinness Li-Qun Zhang Renata Ivanek Martin Wiedmann |
| author_facet | Sriya Sunil Sarah I. Murphy Ruixi Chen Wei Chen Joseph Guinness Li-Qun Zhang Renata Ivanek Martin Wiedmann |
| author_sort | Sriya Sunil |
| collection | DOAJ |
| description | Models that predict bacterial growth in food products can help industry with decision-making with regard to microbial food spoilage. Such models have recently been developed using machine learning (ML) rather than a mechanistic understanding of bacterial growth. Thus, our aim was to compare the performance of mechanistic (M) models and the Gaussian process (GP) model (i.e., an ML approach) for predicting bacterial growth on spinach from a US-based supply chain as well as a China-based supply chain; models were developed using previously published data, as well as new data collected in this study from the China-based supply chain. For the packaged spinach collected in this study from the China-based supply chain, the mean net growth of aerobic, mesophilic bacteria over 10 days of shelf life was 1.16 log10 (n = 11, local distribution) and 1.29 log10 (n = 8, eCommerce distribution); bacterial growth on spinach did not differ significantly by distribution channel. The data obtained in this study, as well as previously published data on the growth of (i) individual bacterial strains (i.e., strain-level growth) and (ii) the overall bacterial population on baby spinach (i.e., population-level growth), were used to fit models. Specifically, GP models were fit to population-level growth data only, while M models were fit to strain-level and population-level growth data. The RMSE values for the M models (i.e., 0.72, 0.77 and 1.09 log10 CFU/g, for three M models assessed here) and GP models (i.e., 0.68 and 0.81 log10 CFU/g, for the two GP models assessed here) are similar, which suggests that both M and GP models show comparable accuracy at predicting bacterial growth on spinach. |
| format | Article |
| id | doaj-art-0e497567b5b44788b7298941beb9c30b |
| institution | Kabale University |
| issn | 0362-028X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Food Protection |
| spelling | doaj-art-0e497567b5b44788b7298941beb9c30b2025-01-09T06:12:34ZengElsevierJournal of Food Protection0362-028X2025-01-01881100417Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated StorageSriya Sunil0Sarah I. Murphy1Ruixi Chen2Wei Chen3Joseph Guinness4Li-Qun Zhang5Renata Ivanek6Martin Wiedmann7Department of Food Science, Cornell University, Ithaca, NY 14853, United StatesDepartment of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, United StatesDepartment of Food Science, Cornell University, Ithaca, NY 14853, United StatesDepartment of Plant Pathology, China Agricultural University, Beijing, ChinaDepartment of Statistics and Data Science, Cornell University, Ithaca, NY, 14853, United StatesDepartment of Plant Pathology, China Agricultural University, Beijing, ChinaDepartment of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, United StatesDepartment of Food Science, Cornell University, Ithaca, NY 14853, United States; Corresponding author at: 341 Stocking Hall, Ithaca, NY 14853, United States.Models that predict bacterial growth in food products can help industry with decision-making with regard to microbial food spoilage. Such models have recently been developed using machine learning (ML) rather than a mechanistic understanding of bacterial growth. Thus, our aim was to compare the performance of mechanistic (M) models and the Gaussian process (GP) model (i.e., an ML approach) for predicting bacterial growth on spinach from a US-based supply chain as well as a China-based supply chain; models were developed using previously published data, as well as new data collected in this study from the China-based supply chain. For the packaged spinach collected in this study from the China-based supply chain, the mean net growth of aerobic, mesophilic bacteria over 10 days of shelf life was 1.16 log10 (n = 11, local distribution) and 1.29 log10 (n = 8, eCommerce distribution); bacterial growth on spinach did not differ significantly by distribution channel. The data obtained in this study, as well as previously published data on the growth of (i) individual bacterial strains (i.e., strain-level growth) and (ii) the overall bacterial population on baby spinach (i.e., population-level growth), were used to fit models. Specifically, GP models were fit to population-level growth data only, while M models were fit to strain-level and population-level growth data. The RMSE values for the M models (i.e., 0.72, 0.77 and 1.09 log10 CFU/g, for three M models assessed here) and GP models (i.e., 0.68 and 0.81 log10 CFU/g, for the two GP models assessed here) are similar, which suggests that both M and GP models show comparable accuracy at predicting bacterial growth on spinach.http://www.sciencedirect.com/science/article/pii/S0362028X24002011Bacterial growthFood qualityGaussian process modelMachine learningPrimary growth modelSpinach |
| spellingShingle | Sriya Sunil Sarah I. Murphy Ruixi Chen Wei Chen Joseph Guinness Li-Qun Zhang Renata Ivanek Martin Wiedmann Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage Journal of Food Protection Bacterial growth Food quality Gaussian process model Machine learning Primary growth model Spinach |
| title | Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage |
| title_full | Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage |
| title_fullStr | Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage |
| title_full_unstemmed | Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage |
| title_short | Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage |
| title_sort | application of mechanistic models and the gaussian process model to predict bacterial growth on baby spinach during refrigerated storage |
| topic | Bacterial growth Food quality Gaussian process model Machine learning Primary growth model Spinach |
| url | http://www.sciencedirect.com/science/article/pii/S0362028X24002011 |
| work_keys_str_mv | AT sriyasunil applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT sarahimurphy applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT ruixichen applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT weichen applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT josephguinness applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT liqunzhang applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT renataivanek applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage AT martinwiedmann applicationofmechanisticmodelsandthegaussianprocessmodeltopredictbacterialgrowthonbabyspinachduringrefrigeratedstorage |