Development of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach

Küçük Resim Yok

Tarih

2023

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Sage Publications Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

The purpose of this study was to create a tool for predicting the growth of total mesophilic bacteria in spinach using machine learning-based regression models such as support vector regression, decision tree regression, and Gaussian process regression. The performance of these models was compared to traditionally used models (modified Gompertz, Baranyi, and Huang models) using statistical indices like the coefficient of determination (R-2) and root mean square error (RMSE). The results showed that the machine learning-based regression models provided more accurate predictions with an R-2 of at least 0.960 and an RMSE of at most 0.154, indicating that they can be used as an alternative to traditional approaches for predictive total mesophilic. Therefore, the developed software in this work has a significant potential to be used as an alternative simulation method to traditionally used approach in the predictive food microbiology field.

Açıklama

Anahtar Kelimeler

Data Mining, Prediction Tool, Gaussian Process Regression, Predictive Microbiology, Random Forest, Growth-Rate, Lag Phase, Contamination, Environment

Kaynak

Food Science and Technology International

WoS Q Değeri

N/A

Scopus Q Değeri

Q2

Cilt

Sayı

Künye