Machine Learning Augmented VOC Identification by Mid-Infrared Nanoantennas with Microfluidics Chambers

This work aims at determining the best performing mid and far-infrared (MIR and FIR) joint spectral interval to identify and classify clouds in the Antarctic region by mean of a machine learning algorithm.About 1700 spectral-resolved radiances, collected during 2013 by the ground based Radiation Explorer in the Far InfraRed-Prototype for Applications and Development, REFIR-PAD (Palchetti et al., 2015) at Dome C, Antarctic Plateau, are selected in coincidence with the co-located with backscatter and depolarization profiles derived from a tropospheric lidar system (Ricaud et al., 2017) to pre-classify clear sky, ice clouds, or mixed phase clouds.A machine learning cloud identification and classification algorithm named CIC (Maestri et al., 2019), trained with a pre-selected set of REFIR-PAD spectra, is applied to this dataset by assuming that no other information than the spectrum itself is known.The CIC algorithm is applied by considering different spectral intervals, in order to maximize the classification results for each class (clear sky, ice clouds, mixed phase clouds). A CIC "threat score" is defined as the classification true positives divided by the sum of true positives, false positives, and false negatives. The maximization of the threat score is used to assess the algorithm performances that span from 58% to 96% in accordance with the selected interval. The best performing spectral range is the 380-1000 cm-1. The result, besides suggesting the importance of a proper algorithm calibration in accordance with the used sensor, highlights the fundamental role of the FIR part of the spectrum.The calibrated CIC algorithm is then applied to a larger REFIR-PAD dataset of about 90000 spectra collected from 2012 to 2015. Some results of the full dataset cloud classification are also presented.The present work contributes to the preparatory studies for the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission that has recently been selected as ESA&#8217;s 9th Earth Explorer mission, scheduled for launch in 2026.&#160;&#160;References:Maestri, T., Cossich, W., and Sbrolli, I., 2019: Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared, Atmos. Meas. Tech., 12, pp. 3521 - 3540Palchetti, L., Bianchini, G., Di Natale, G., and Del Guasta, M., 2015: Far infrared radiative properties of water vapor and clouds in Antarctica. Bull. Amer. Meteor. Soc., 96, 1505&#8211;1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.Ricaud, P., Bazile, E., del Guasta, M., Lanconelli, C., Grigioni, P., and Mahjoub, A., 2017: Genesis of diamond dust, ice fog and thick cloud episodes observed and modelled above Dome C, Antarctica, Atmos. Chem. Phys., 17, 5221&#8211;5237, https://doi.org/10.5194/acp-17-5221-2017.

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Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods

Animals ◽

10.3390/ani11082154 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2154

Author(s):

Brenda Contla Hernández ◽

Nicolas Lopez-Villalobos ◽

Matthieu Vignes

Keyword(s):

Neural Network ◽

Machine Learning ◽

Milk Sample ◽

Time Window ◽

Early Stage ◽

Machine Learning Techniques ◽

Cow Milk ◽

Economic Losses ◽

Support Vector ◽

Mid Infrared

The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques—random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models—were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%), the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.

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Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets

Journal of Dairy Science ◽

10.3168/jds.2021-20812 ◽

2021 ◽

Author(s):

M. Frizzarin ◽

T.F. O'Callaghan ◽

T.B. Murphy ◽

D. Hennessy ◽

A. Casa

Keyword(s):

Machine Learning ◽

Infrared Spectra ◽

Cow Milk ◽

Learning Methods ◽

Mid Infrared ◽

Total Mixed Ration ◽

Machine Learning Methods

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Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Wellcome Open Research ◽

10.12688/wellcomeopenres.15201.3 ◽

2019 ◽

Vol 4 ◽

pp. 76 ◽

Cited By ~ 11

Author(s):

Mario González Jiménez ◽

Simon A. Babayan ◽

Pegah Khazaeli ◽

Margaret Doyle ◽

Finlay Walton ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Malaria Vector ◽

Mosquito Species ◽

Age Distribution ◽

Supervised Machine Learning ◽

Malaria Vector Control ◽

Promising Alternative ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

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Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Malaria Journal ◽

10.1186/s12936-019-2982-9 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 4

Author(s):

Emmanuel P. Mwanga ◽

Elihaika G. Minja ◽

Emmanuel Mrimi ◽

Mario González Jiménez ◽

Johnson K. Swai ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Infrared Spectroscopy ◽

Human Blood ◽

Supervised Machine Learning ◽

Classification Algorithms ◽

Clinical Settings ◽

Mid Infrared ◽

Blood Spots ◽

Model Training

Abstract Background Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

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