Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

The objective of this study was to determine whether mid-infrared diffuse reflectance spectroscopy could be used in the same manner as near-infrared diffuse reflectance spectroscopy to quantitatively determine the protein content of ground wheat samples. One hundred and thirty hard red winter wheat samples were assayed for protein by combustion and scanned in the near- and mid-infrared. Samples (UDY ground) were scanned neat in the near-infrared from 1100 nm (9091 cm−1) to 2498 nm (4003 cm−1) on a scanning monochromator and in the mid-infrared from 4000 cm−1 (2500 nm) to 400 cm−1 (25,000 nm) on a Fourier transform spectrometer at 4-and 16-cm−1 resolutions. Protein content varied from a low of 8.98% to a high of 18.70% (average of 12.86% with a standard deviation of 1.66%). Calibrations developed with the use of partial least-squares gave an R2 and bias-corrected standard error of performance of 0.999 and 0.054 for the near-infrared and 0.997 and 0.085 for the mid-infrared (4 cm−1 resolution). Calibration results based on mid-infrared spectra, while not as good as those for near-infrared spectra, were nevertheless quite good. These results demonstrate that it is possible to develop satisfactory calibrations for protein in ground wheat with the use of mid-infrared spectra without the need for sample dilution with KBr.

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Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done?

Geoderma ◽

10.1016/j.geoderma.2009.04.005 ◽

2010 ◽

Vol 158 (1-2) ◽

pp. 3-14 ◽

Cited By ~ 191

Author(s):

James B. Reeves

Keyword(s):

Diffuse Reflectance ◽

Diffuse Reflectance Spectroscopy ◽

Soil Analysis ◽

Reflectance Spectroscopy ◽

Mid Infrared ◽

Site Analysis

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Automated classification of brain tissue: comparison between hyperspectral imaging and diffuse reflectance spectroscopy

Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling ◽

10.1117/12.2548754 ◽

2020 ◽

Cited By ~ 1

Author(s):

Marco Lai ◽

Simon Skyrman ◽

Caifeng Shan ◽

Elvira Paulussen ◽

Francesca Manni ◽

...

Keyword(s):

Brain Tissue ◽

Hyperspectral Imaging ◽

Diffuse Reflectance ◽

Diffuse Reflectance Spectroscopy ◽

Reflectance Spectroscopy ◽

Automated Classification

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O-OGC03 Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancer specimens using diffuse reflectance spectroscopy

British Journal of Surgery ◽

10.1093/bjs/znab429.035 ◽

2021 ◽

Vol 108 (Supplement_9) ◽

Author(s):

Scarlet Nazarian ◽

Ioannis Gkouzionis ◽

Michal Kawka ◽

Nisha Patel ◽

Ara Darzi ◽

...

Keyword(s):

Real Time ◽

Diffuse Reflectance ◽

Diffuse Reflectance Spectroscopy ◽

Tracking System ◽

Reflectance Spectroscopy ◽

Tumour Tissue ◽

Acquisition Time ◽

Margin Assessment ◽

Real Time Tracking

Abstract Background Diffuse reflectance spectroscopy (DRS) is a technique that allows discrimination of normal and abnormal tissue based on spectral data. It is a promising technique for cancer margin assessment. However, application in a clinical setting is limited by the inability of DRS to mark the tissue that has been scanned and its lack of continuous real-time spectral measurements. This aim of this study was to develop a real-time tracking system to enable localisation of the tip of a handheld DRS probe to aid classification of tumour and non-tumour tissue. Methods A coloured marker was attached to the DRS fibre probe and was detected using colour segmentation. A Kalman filter was used to estimate the probe’s tip position during scanning of the tissue specimen. In this way, the system was robust to partial occlusion allowing real-time detection and tracking. Supervised classification algorithms were used for the discrimination between tumour and non-tumour tissue, and evaluated in terms of overall accuracy, sensitivity, specificity, and the area under the curve (AUC). A live augmented view with all the tracked and classified optical biopsy sites were presented, providing visual feedback to the surgeons. Results A green coloured marker was successfully used to track the DRS probe. The measured root mean square error of probe tip tracking was 1.18±0.58mm and 1.05±0.28mm for the X and Y directions, respectively, whilst the maximum measured error was 1.76mm. Overall, 47 distinct sets of tumour and non-tumour tissue data were recorded through real-time tracking of ex vivo oesophageal and gastric tissue. The overall diagnostic accuracy of the system to classify tumour and non-tumour tissue in real-time was 94% for stomach and 96% for the oesophagus. Conclusions We have been able to successfully develop a real-time tracking system for a DRS probe when used on stomach and oesophageal tissue for tumour detection, and the accuracy derived demonstrates the strength and clinical value of the technique. The method allows real-time tracking and classification with short data acquisition time to aid margin assessment in cancer resection surgery.

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