Feasibility for Real-Time Monitoring of Bacterial Growth in Raw Milk Using a New Contactless Sensor Article Swipe
Charles Andre Haab
,
Jussiane S. Silva
,
Adriano M. Jaime
,
Vandré Sonza Pinto
,
Geovana M. Mello
,
Darliana Mello Souza
,
Juliano Smanioto Barin
,
Cristiano Menezes
,
Leandro Michels
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.analchem.5c03766
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.analchem.5c03766
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 (R2 = 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.
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- OA Status
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Feasibility for Real-Time Monitoring of Bacterial Growth in Raw Milk Using a New Contactless SensorWork title
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2025Year of publication
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2025-10-30Full publication date if available
- Authors
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Charles Andre Haab, Jussiane S. Silva, Adriano M. Jaime, Vandré Sonza Pinto, Geovana M. Mello, Darliana Mello Souza, Juliano Smanioto Barin, Cristiano Menezes, Leandro MichelsList of authors in order
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https://doi.org/10.1021/acs.analchem.5c03766Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1021/acs.analchem.5c03766Direct OA link when available
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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| referenced_works_count | 31 |
| abstract_inverted_index.8 | 193 |
| abstract_inverted_index.= | 126, 172, 180 |
| abstract_inverted_index.A | 0, 87, 128 |
| abstract_inverted_index.a | 24, 78, 120, 149, 167, 225 |
| abstract_inverted_index.h | 194 |
| abstract_inverted_index.48 | 191 |
| abstract_inverted_index.55 | 90 |
| abstract_inverted_index.an | 17 |
| abstract_inverted_index.be | 188 |
| abstract_inverted_index.by | 42 |
| abstract_inverted_index.in | 13, 37, 232 |
| abstract_inverted_index.no | 138 |
| abstract_inverted_index.of | 8, 50, 89, 102, 123, 151, 153, 211, 221 |
| abstract_inverted_index.on | 23 |
| abstract_inverted_index.or | 57 |
| abstract_inverted_index.to | 76, 109, 175, 192, 195, 201, 228 |
| abstract_inverted_index.95% | 142 |
| abstract_inverted_index.CFU | 156 |
| abstract_inverted_index.No. | 204 |
| abstract_inverted_index.The | 31, 116, 145, 158 |
| abstract_inverted_index.and | 69, 100, 134, 182, 218 |
| abstract_inverted_index.for | 5, 47, 81, 96, 215 |
| abstract_inverted_index.log | 155 |
| abstract_inverted_index.raw | 14, 91, 198 |
| abstract_inverted_index.the | 9, 38, 48, 66, 70, 83, 97, 103, 110, 132, 163, 176, 184, 197, 209, 212, 233 |
| abstract_inverted_index.was | 3, 63, 74 |
| abstract_inverted_index.(β) | 73 |
| abstract_inverted_index.2.40 | 154 |
| abstract_inverted_index.also | 160 |
| abstract_inverted_index.from | 190 |
| abstract_inverted_index.good | 121 |
| abstract_inverted_index.milk | 15, 92, 199 |
| abstract_inverted_index.that | 162, 183 |
| abstract_inverted_index.time | 186 |
| abstract_inverted_index.used | 75, 95 |
| abstract_inverted_index.were | 94, 107 |
| abstract_inverted_index.0.39) | 181 |
| abstract_inverted_index.0.75) | 173 |
| abstract_inverted_index.These | 206 |
| abstract_inverted_index.based | 22 |
| abstract_inverted_index.could | 187 |
| abstract_inverted_index.count | 12, 113 |
| abstract_inverted_index.curve | 61 |
| abstract_inverted_index.dairy | 234 |
| abstract_inverted_index.limit | 150 |
| abstract_inverted_index.model | 80, 99, 118 |
| abstract_inverted_index.novel | 1 |
| abstract_inverted_index.plate | 112 |
| abstract_inverted_index.point | 72 |
| abstract_inverted_index.score | 170 |
| abstract_inverted_index.total | 10, 84, 88 |
| abstract_inverted_index.using | 16, 65 |
| abstract_inverted_index.which | 106 |
| abstract_inverted_index.(score | 171, 179 |
| abstract_inverted_index.0.75). | 127 |
| abstract_inverted_index.Growth | 60 |
| abstract_inverted_index.count. | 86 |
| abstract_inverted_index.curves | 52 |
| abstract_inverted_index.green, | 217 |
| abstract_inverted_index.growth | 20, 51 |
| abstract_inverted_index.higher | 168 |
| abstract_inverted_index.method | 2, 33, 147, 165, 178, 214 |
| abstract_inverted_index.model, | 68 |
| abstract_inverted_index.rapid, | 216 |
| abstract_inverted_index.sample | 55 |
| abstract_inverted_index.sensor | 21 |
| abstract_inverted_index.showed | 137 |
| abstract_inverted_index.between | 131 |
| abstract_inverted_index.changes | 36 |
| abstract_inverted_index.control | 231 |
| abstract_inverted_index.coupled | 26 |
| abstract_inverted_index.growth, | 223 |
| abstract_inverted_index.induced | 41 |
| abstract_inverted_index.level). | 144 |
| abstract_inverted_index.method, | 105 |
| abstract_inverted_index.method. | 115 |
| abstract_inverted_index.methods | 136 |
| abstract_inverted_index.quality | 230 |
| abstract_inverted_index.reagent | 58 |
| abstract_inverted_index.reduced | 189 |
| abstract_inverted_index.results | 159 |
| abstract_inverted_index.samples | 93 |
| abstract_inverted_index.without | 53 |
| abstract_inverted_index.55/2020. | 205 |
| abstract_inverted_index.Gompertz | 67 |
| abstract_inverted_index.allowing | 46, 224 |
| abstract_inverted_index.analysis | 62, 130, 185 |
| abstract_inverted_index.classify | 196 |
| abstract_inverted_index.compared | 108, 174 |
| abstract_inverted_index.findings | 207 |
| abstract_inverted_index.monitors | 35 |
| abstract_inverted_index.presents | 166 |
| abstract_inverted_index.proposed | 32, 104, 133, 146, 164, 213 |
| abstract_inverted_index.standard | 111 |
| abstract_inverted_index.Normative | 202 |
| abstract_inverted_index.according | 200 |
| abstract_inverted_index.activity, | 45 |
| abstract_inverted_index.addition. | 59 |
| abstract_inverted_index.bacterial | 11, 19, 43, 85, 222 |
| abstract_inverted_index.construct | 77 |
| abstract_inverted_index.detection | 152 |
| abstract_inverted_index.detector. | 30 |
| abstract_inverted_index.developed | 4 |
| abstract_inverted_index.frequency | 29, 40 |
| abstract_inverted_index.greenness | 169 |
| abstract_inverted_index.highlight | 208 |
| abstract_inverted_index.industry. | 235 |
| abstract_inverted_index.metabolic | 44 |
| abstract_inverted_index.performed | 64 |
| abstract_inverted_index.presented | 148 |
| abstract_inverted_index.promising | 226 |
| abstract_inverted_index.real-time | 6, 219 |
| abstract_inverted_index.reference | 114, 135, 177 |
| abstract_inverted_index.requiring | 54 |
| abstract_inverted_index.resonance | 28, 39 |
| abstract_inverted_index.confidence | 143 |
| abstract_inverted_index.difference | 140 |
| abstract_inverted_index.electrical | 18 |
| abstract_inverted_index.inflection | 71 |
| abstract_inverted_index.monitoring | 220 |
| abstract_inverted_index.predictive | 79, 98, 117 |
| abstract_inverted_index.Instruction | 203 |
| abstract_inverted_index.alternative | 227 |
| abstract_inverted_index.application | 101 |
| abstract_inverted_index.coefficient | 122 |
| abstract_inverted_index.comparative | 129 |
| abstract_inverted_index.contactless | 27 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.determining | 82 |
| abstract_inverted_index.feasibility | 210 |
| abstract_inverted_index.significant | 139 |
| abstract_inverted_index.capacitively | 25 |
| abstract_inverted_index.construction | 49 |
| abstract_inverted_index.continuously | 34 |
| abstract_inverted_index.demonstrated | 119 |
| abstract_inverted_index.pretreatment | 56 |
| abstract_inverted_index.determination | 124 |
| abstract_inverted_index.quantification | 7 |
| abstract_inverted_index.(<i>t</i>-test, | 141 |
| abstract_inverted_index.microbiological | 229 |
| abstract_inverted_index.mL<sup>-1</sup>. | 157 |
| abstract_inverted_index.(<i>R</i><sup>2</sup> | 125 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5030519854 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I33501960 |
| citation_normalized_percentile |