Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

Ramírez-Morales, Iván; Aguilar, Lenin; Fernández-Blanco, Enrique; Rivero, Daniel; Pérez, Jhonny E.

doi:10.3390/app112210751

Cited by 3 publications

(5 citation statements)

References 48 publications

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“…The physiological and metabolic changes are the foundation for NIRS profiling to distinguish between healthy and infected animals. biochemical changes that caused the difference between the blood plasma of healthy and infected animal (Meilina et al, 2009;Kinoshita et al, 2012b;Ramirez-Morales et al, 2021). In 2012, Mycobacterium tuberculosis was detected by infrared and near-infrared spectroscopy (Pesala et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…It can be used to detect and monitor infection in dairy cattle. The profile of NIR spectra reflects the animal’s immune response to JD agent by applying MVA and the aquaphotomics approach based on the biochemical changes that caused the difference between the blood plasma of healthy and infected animal ( Meilina et al., 2009 ; Kinoshita et al., 2012b ; Ramirez-Morales et al., 2021 ). In 2012, Mycobacterium tuberculosis was detected by infrared and near-infrared spectroscopy ( Pesala et al., 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…The PCA and SIMCA models were used to diagnose mastitis in dairy cattle ( Tsenkova and Atanassova, 2001 ; Kinoshita et al., 2012b ; Ramirez-Morales et al., 2021 ) by identifying NIR features of blood plasma.…”

Section: Discussionmentioning

confidence: 99%

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A novel diagnostic approach to Paratuberculosis in dairy cattle using near-infrared spectroscopy and aquaphotomics

Behdad,

Pakdel,

Massudi

2024

Front. Cell. Infect. Microbiol.

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IntroductionAs a contagious and chronic disease in the livestock industry, Paratuberculosis is a significant threat to dairy herds’ genetic and economic resources. Due to intensive breeding and high production of dairy cattle, the incidence and prevalence are higher. Developing non-destructive diagnostic methods for the early detection and identification of healthy animals is paramount for breeding programs. Conventional methods are almost entirely destructive, have low accuracy, lack precision, and are time-consuming. Near-infrared spectroscopy (NIRS) and aquaphotomics can detect changes in biofluids and thus have the potential to diagnose disease. This study aimed to investigate the diagnostic ability of NIRS and aquaphotomics for Paratuberculosis in dairy cattle. MethodsBlood plasma from dairy cattle was collected in the NIR range (1,300 nm to 1,600 nm) 60 days before and 100 days to 200 days after calving in two groups, positive and negative, using the same consecutive enzyme-linked immunosorbent assay test results three times as a reference test.ResultsNIRS and aquaphotomics methods invite 100% accuracy, sensitivity, and specificity to detect Paratuberculosis using data mining by unsupervised method, Principal Component Analysis, and supervised methods: Soft Independent Modeling of Class Analogiest, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Partial Least Square–Discriminant Analysis, and Support Vector Machine models.DiscussionThe current study found that monitoring blood plasma with NIR spectra provides an opportunity to analyze antibody levels indirectly via changes in water spectral patterns caused by complex physiological changes, such as the amount of antibodies related to Paratuberculosis by aquagram.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A novel diagnostic approach to Paratuberculosis in dairy cattle using near-infrared spectroscopy and aquaphotomics

Behdad,

Pakdel,

Massudi

2024

Front. Cell. Infect. Microbiol.

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“…Finally, also data from other technologies such as NIR are successfully utilised. Mastitis can be detected from NIR data processed through ML as Ramirez-Morales et al [35] described. They assessed the feasibility of developing a low-cost, real-time, field-applicable tool to detect the presence and the severity of bovine mastitis.…”

Section: Disease Detectionmentioning

confidence: 99%

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

Trapanese,

Hostens,

Salzano

et al. 2024

Acta IMEKO

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In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.

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“…Additionally, a parameter k that can be changed determines the number of neighbors examined. The Euclidean distance, the most popular and utilized in this study, must therefore be established as a metric to measure the distance between two data points [22].…”

Section: Analysis Of Data Using Machine Learningmentioning

confidence: 99%

Classification of Harvesting Age of Mango Based on NIR Spectra Using Machine Learning Algorithms

Arianti¹,

Muslih²,

Irawan³

et al. 2023

MMEP

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The established assessment of post-harvest attributes, such as the age of harvesting day, requires destructive sampling that the availability of fruit of trees can often limit and is expensive. In contrast, non-destructive post-harvest attribute assessment utilizing the NIR data spectrum is fast and reliable, especially for mango. However, NIR spectral data frequently produce non-linearity with the reference dataset used. Therefore, this study conducted research on using NIR spectral data to classify the harvesting age of mango fruits using machine learning algorithms. A total of five supervised machine learning algorithms were explored to generate the classification model, including gradient boost (GB), k-nearest neighbor (k-NN), decision tree (DT), random forest (RF), and linear discriminant analysis (LDA). In this study, 237 NIR spectral data from mango fruits with Arumanis cultivars from orchard sites in the Garut district, West Java Province (Indonesia) were measured to determine the appropriate harvest time using NIR spectra 1000 to 2500 nm. The data sets were randomly divided into training and testing datasets, 80% and 20%, respectively. Hyperparameter optimization was performed using the GridSearchCV function from scikit-learn by observing the evaluation of the confusion matrix. Generally, all machine learning algorithms can show performance in classifying the harvest age of mango fruit based on NIR spectra data. Based on the accuracy evaluation matrix, the best machine learning algorithm arranged to classify the age of mango fruit harvest is DT>GB>LDA>RF>k-NN. Finally, predictions generated using the DT algorithm from more established machine learning algorithms as a training and testing set consistently yielded higher prediction accuracy in classification models. This study provides a framework for understanding the feasibility of machine learning algorithms on NIR data spectral to the accuracy of classification prediction of the harvesting age of mango. In addition, this study presents the importance of assessing the performance of the classification model using confusion metrics.

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Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

Cited by 3 publications

References 48 publications

A novel diagnostic approach to Paratuberculosis in dairy cattle using near-infrared spectroscopy and aquaphotomics

A novel diagnostic approach to Paratuberculosis in dairy cattle using near-infrared spectroscopy and aquaphotomics

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

Classification of Harvesting Age of Mango Based on NIR Spectra Using Machine Learning Algorithms

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