# Feasibility for Real-Time Monitoring of Bacterial Growth in Raw Milk Using a New Contactless Sensor

## TL;DR

A new sensor allows real-time tracking of bacteria in raw milk without chemicals, offering a faster and eco-friendlier alternative to traditional methods.

## Contribution

The study introduces a contactless sensor for real-time bacterial growth monitoring in raw milk with reduced analysis time and higher greenness.

## Key findings

- The predictive model achieved a coefficient of determination (R²) of 0.75.
- The proposed method has a detection limit of 2.40 log CFU mL⁻¹.
- The method reduces analysis time from 48 to 8 hours and has a higher greenness score than traditional methods.

## Abstract

A novel method was developed for real-time quantification
of the
total bacterial count in raw milk using an electrical bacterial growth
sensor based on a capacitively coupled contactless resonance frequency
detector. The proposed method continuously monitors changes in the
resonance frequency induced by bacterial metabolic activity, allowing
for the construction of growth curves without requiring sample pretreatment
or reagent addition. Growth curve analysis was performed using the
Gompertz model, and the inflection point (β) was used to construct
a predictive model for determining the total bacterial count. A total
of 55 raw milk samples were used for the predictive model and application
of the proposed method, which were compared to the standard plate
count reference method. The predictive model demonstrated a good coefficient
of determination (R
2 = 0.75). A comparative
analysis between the proposed and reference methods showed no significant
difference (t-test, 95% confidence level). The proposed
method presented a limit of detection of 2.40 log CFU mL–1. The results also demonstrate that the proposed method presents
a higher greenness score (score = 0.75) compared to the reference
method (score = 0.39) and that the analysis time could be reduced
from 48 to 8 h to classify the raw milk according to Normative Instruction
No. 55/2020. These findings highlight the feasibility of the proposed
method for rapid, green, and real-time monitoring of bacterial growth,
allowing a promising alternative to microbiological quality control
in the dairy industry.