Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
This study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classificati...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5676 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257458042241024 |
|---|---|
| author | Josías N. Molina-Courtois Yaquelin Josefa Aguilar Morales Luis Escalante-Zarate Mario Castelán Yojana J. P. Carreón Jorge González-Gutiérrez |
| author_facet | Josías N. Molina-Courtois Yaquelin Josefa Aguilar Morales Luis Escalante-Zarate Mario Castelán Yojana J. P. Carreón Jorge González-Gutiérrez |
| author_sort | Josías N. Molina-Courtois |
| collection | DOAJ |
| description | This study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classification of milk types and detection of adulteration. Dried droplets of milk containing NaCl concentrations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> were analyzed, revealing distinct morphologies, including amorphous, cross-shaped, and dendritic crystals. These structures were quantitatively characterized using lacunarity to assess their discriminatory power. Two classification approaches were evaluated: one based on lacunarity analysis alone and another incorporating deep learning. Both methods yielded high classification accuracies, with lacunarity achieving <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.04</mn><mo>%</mo><mo>±</mo><mn>6.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while deep learning reached <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.22</mn><mo>%</mo><mo>±</mo><mn>4.47</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Notably, the highest performance was obtained with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> NaCl, where lacunarity reached <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>97.08</mn><mo>%</mo><mo>±</mo><mn>2.27</mn><mo>%</mo></mrow></semantics></math></inline-formula> and deep learning <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.88</mn><mo>%</mo><mo>±</mo><mn>2.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, indicating improved precision and stability. While deep learning demonstrated more consistent performance across test cases, lacunarity alone captured highly discriminative structural features, making it a valuable complementary tool. The integration of NaCl and lacunarity analysis offers a robust and interpretable methodology for ensuring the quality and authenticity of dairy products, particularly in detecting adulteration, where morphological contrast is less evident. |
| format | Article |
| id | doaj-art-ac3fec317396419a878bfaf50da4bbb8 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ac3fec317396419a878bfaf50da4bbb82025-08-20T01:56:25ZengMDPI AGApplied Sciences2076-34172025-05-011510567610.3390/app15105676Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep LearningJosías N. Molina-Courtois0Yaquelin Josefa Aguilar Morales1Luis Escalante-Zarate2Mario Castelán3Yojana J. P. Carreón4Jorge González-Gutiérrez5Facultad de Ciencias en Física y Matemáticas, Universidad Autónoma de Chiapas, Tuxtla Gutiérrez 29050, Chiapas, MexicoFacultad de Ciencias en Física y Matemáticas, Universidad Autónoma de Chiapas, Tuxtla Gutiérrez 29050, Chiapas, MexicoFacultad de Ciencias en Física y Matemáticas, Universidad Autónoma de Chiapas, Tuxtla Gutiérrez 29050, Chiapas, MexicoRobotics and Advanced Manufacturing, Center for Research and Advanced Studies of the National Polytechnic Institute, Ramos Arizpe 25900, Coahuila, MexicoFacultad de Ciencias en Física y Matemáticas, Universidad Autónoma de Chiapas, Tuxtla Gutiérrez 29050, Chiapas, MexicoFacultad de Ciencias en Física y Matemáticas, Universidad Autónoma de Chiapas, Tuxtla Gutiérrez 29050, Chiapas, MexicoThis study introduces a novel method for classifying whole and lactose-free milk and the detection of water adulteration through analyzing dried droplets. The key innovation is the addition of NaCl, which modulates crystallization to enhance structural differentiation and facilitate the classification of milk types and detection of adulteration. Dried droplets of milk containing NaCl concentrations of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> were analyzed, revealing distinct morphologies, including amorphous, cross-shaped, and dendritic crystals. These structures were quantitatively characterized using lacunarity to assess their discriminatory power. Two classification approaches were evaluated: one based on lacunarity analysis alone and another incorporating deep learning. Both methods yielded high classification accuracies, with lacunarity achieving <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.04</mn><mo>%</mo><mo>±</mo><mn>6.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, while deep learning reached <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.22</mn><mo>%</mo><mo>±</mo><mn>4.47</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Notably, the highest performance was obtained with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> NaCl, where lacunarity reached <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>97.08</mn><mo>%</mo><mo>±</mo><mn>2.27</mn><mo>%</mo></mrow></semantics></math></inline-formula> and deep learning <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.88</mn><mo>%</mo><mo>±</mo><mn>2.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, indicating improved precision and stability. While deep learning demonstrated more consistent performance across test cases, lacunarity alone captured highly discriminative structural features, making it a valuable complementary tool. The integration of NaCl and lacunarity analysis offers a robust and interpretable methodology for ensuring the quality and authenticity of dairy products, particularly in detecting adulteration, where morphological contrast is less evident.https://www.mdpi.com/2076-3417/15/10/5676lacunaritydeep learningCNNpattern recognitionmilk adulterationdried droplets |
| spellingShingle | Josías N. Molina-Courtois Yaquelin Josefa Aguilar Morales Luis Escalante-Zarate Mario Castelán Yojana J. P. Carreón Jorge González-Gutiérrez Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning Applied Sciences lacunarity deep learning CNN pattern recognition milk adulteration dried droplets |
| title | Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning |
| title_full | Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning |
| title_fullStr | Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning |
| title_full_unstemmed | Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning |
| title_short | Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning |
| title_sort | pattern recognition in dried milk droplets using lacunarity and deep learning |
| topic | lacunarity deep learning CNN pattern recognition milk adulteration dried droplets |
| url | https://www.mdpi.com/2076-3417/15/10/5676 |
| work_keys_str_mv | AT josiasnmolinacourtois patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning AT yaquelinjosefaaguilarmorales patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning AT luisescalantezarate patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning AT mariocastelan patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning AT yojanajpcarreon patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning AT jorgegonzalezgutierrez patternrecognitionindriedmilkdropletsusinglacunarityanddeeplearning |