Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15201.1 ◽

2019 ◽

Vol 4 ◽

pp. 76 ◽

Cited By ~ 1

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. 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 other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

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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.15201.2 ◽

2019 ◽

Vol 4 ◽

pp. 76

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

10.1101/414342 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mario González-Jiménez ◽

Simon A. Babayan ◽

Pegah Khazaeli ◽

Margaret Doyle ◽

Finlay Walton ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Mosquito Species ◽

Age Distribution ◽

Supervised Machine Learning ◽

Malaria Vectors ◽

Malaria Vector Control ◽

Species Identity ◽

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 12 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 total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

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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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Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Molecules ◽

10.3390/molecules25214987 ◽

2020 ◽

Vol 25 (21) ◽

pp. 4987

Author(s):

Hongyan Zhu ◽

Jun-Li Xu

Keyword(s):

Machine Learning ◽

Fourier Transform ◽

Infrared Spectroscopy ◽

Learning Algorithms ◽

Back Propagation ◽

Model Performance ◽

Machine Learning Algorithms ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

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Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas

Photodiagnosis and Photodynamic Therapy ◽

10.1016/j.pdpdt.2021.102308 ◽

2021 ◽

pp. 102308

Author(s):

Fangfang Chen ◽

Chunzhi Meng ◽

Hanwen Qu ◽

Chen Cheng ◽

Chen Chen ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Human Serum ◽

Rapid Detection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

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Soil Urea Analysis Using Mid-Infrared Spectroscopy and Machine Learning

Intelligent Automation & Soft Computing ◽

10.32604/iasc.2022.022547 ◽

2022 ◽

Vol 32 (3) ◽

pp. 1867-1880

Author(s):

J. Haritha ◽

R. S. Valarmathi ◽

M. Kalamani

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

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

10.1101/19001206 ◽

2019 ◽

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 ◽

Test Results ◽

Mid Infrared ◽

Blood Spots

AbstractBackgroundEpidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study shows that mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.MethodsFilter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in twelve wards in south-eastern 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 vapor 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.ResultsLogistic 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 P. ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen.ConclusionThese results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in dried human blood spots. The approach could have potential for rapid and high-throughput screening of Plasmodium infections in both non-clinical settings (e.g. field surveys) and clinical settings (diagnosis to aid case management). However, full utility will require further advances in classification algorithms, field validation of this technology in other study sites and an in-depth evaluation of the biological basis of the observed test results. Training the models on larger datasets could also improve specificity and sensitivity of the technique. The MIR-ML spectroscopy system is robust, low-cost, and requires minimum maintenance.

Download Full-text