Sensitivity improvement in fluorescence‐based particle detection

Kettlitz, Siegfried W.; Moosmann, Carola; Valouch, Sebastian; Lemmer, Uli

doi:10.1002/cyto.a.22499

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…UV LED at 365 nm, are readily available and can be integrated into compact and cost-effective imaging systems. This makes them practical for various laboratory and field applications [16,17]. We collected color texture features [18,19] from fluorescent images and piperine content values from three color classifications (green, orange, and red) of Javanese long pepper as the dataset for ANN modeling.…”

Section: Introductionmentioning

confidence: 99%

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

Sandra,

Damayanti,

Juniar Nainggolan

et al. 2024

BIO Web Conf.

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The conventional method for determining piperine content involves a series of labor-intensive steps, including drying the pepper samples, grinding them, and then extracting them using high-grade ethanol through a reflux method. While effective, this process is time-consuming and resource-intensive, posing limitations in terms of efficiency and the ability to address potential variations. Therefore, there is an urgent need to explore more efficient and rapid approaches for accurately measuring and predicting piperine content, with machine learning approach. This research aims to explore the potential of using fluorescence imaging methods and ANN models to increase the efficiency of measuring piperine content on Javanese long pepper. We propose a machine learning approach using UV-induced fluorescence imaging of Javanese long pepper. UV LEDs (365 nm) induced fluorescence, with color variation indicating piperine content. An artificial neural network (ANN) model, trained on color texture features from fluorescence images, predicted piperine content, achieving an R2 value of 0.88025 with ten selected features using the One-R attribute. The final ANN, configured with 'trainoss' learning, 'tansig' activation, 0.1 learning rate, and 10-40-10 nodes, demonstrated a testing R2 of 0.8943 and MSE of 0.0875. LED-induced fluorescence enhances machine learning's piperine content prediction. This research contributes to more efficient piperine content measurement methods.

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Section: Introductionmentioning

confidence: 99%

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

Sandra,

Damayanti,

Juniar Nainggolan

et al. 2024

BIO Web Conf.

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“…The widely employed excitation light sources for lanthanide chelates were until recently Xenon flash lamps [17,18]. UV LEDs at 365 nm have been implemented for flow cytometry [19,20] and fluorescence microscopy [21,22] however these do not match the excitation spectra of lanthanides well. 340 nm LEDs have recently been used for immunoassay detection with a performance similar to that of flash lamp based systems [23].…”

Section: Introductionmentioning

confidence: 99%

High-sensitivity detection of cardiac troponin I with UV LED excitation for use in point-of-care immunoassay

Rodenko¹,

Eriksson²,

Tidemand-Lichtenberg³

et al. 2017

Biomed. Opt. Express

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High-sensitivity cardiac troponin assay development enables determination of biological variation in healthy populations, more accurate interpretation of clinical results and points towards earlier diagnosis and rule-out of acute myocardial infarction. In this paper, we report on preliminary tests of an immunoassay analyzer employing an optimized LED excitation to measure on a standard troponin I and a novel research high-sensitivity troponin I assay. The limit of detection is improved by factor of 5 for standard troponin I and by factor of 3 for a research high-sensitivity troponin I assay, compared to the flash lamp excitation. The obtained limit of detection was 0.22 ng/L measured on plasma with the research highsensitivity troponin I assay and 1.9 ng/L measured on tris-saline-azide buffer containing bovine serum albumin with the standard troponin I assay. We discuss the optimization of time-resolved detection of lanthanide fluorescence based on the time constants of the system and analyze the background and noise sources in a heterogeneous fluoroimmunoassay. We determine the limiting factors and their impact on the measurement performance. The suggested model can be generally applied to fluoroimmunoassays employing the dry-cup concept.

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Fluorescence particle detection using microfluidics and planar optoelectronic elements

Kettlitz

Moosmann

Valouch

et al. 2014

Biophotonics: Photonic Solutions for Better Health Care IV

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Detection of fluorescent particles is an integral part of flow cytometry for analysis of selectively stained cells. Established flow cytometer designs achieve great sensitivity and throughput but require bulky and expensive components which prohibit mass production of small single-use point-of-care devices. The use of a combination of innovative technologies such as roll-to-roll printed microfluidics with integrated optoelectronic components such as printed organic light emitting diodes and printed organic photodiodes enables tremendous opportunities in cost reduction, miniaturization and new application areas. In order to harvest these benefits, the optical setup requires a redesign to eliminate the need for lenses, dichroic mirrors and lasers. We investigate the influence of geometric parameters on the performance of a thin planar design which uses a high power LED as planar light source and a PIN-photodiode as planar detector. Due to the lack of focusing optics and inferior optical filters, the device sensitivity is not yet on par with commercial state of the art flow cytometer setups. From noise measurements, electronic and optical considerations we deduce possible pathways of improving the device performance. We identify that the sensitivity is either limited by dark noise for very short apertures or by noise from background light for long apertures. We calculate the corresponding crossover length. For the device design we conclude that a low device thickness, low particle velocity and short aperture length are necessary to obtain optimal sensitivity.

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Sensitivity improvement in fluorescence‐based particle detection

Cited by 3 publications

References 28 publications

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

High-sensitivity detection of cardiac troponin I with UV LED excitation for use in point-of-care immunoassay

Fluorescence particle detection using microfluidics and planar optoelectronic elements

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