Synergistic Effects of Pseudocolor Imaging, Differentiation, and Square and Logarithmic Conversion on Accuracy of Quantification of Chemical Characteristics Using Test Strips and Similar Products

Doi, Ryoichi

doi:10.1080/00032719.2018.1556276

Cited by 5 publications

(12 citation statements)

References 38 publications

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“…The normalized mean absolute errors were 0.384% and 0.347% for the original ten and 83 images' datasets, respectively. The normalized mean absolute error was approximately comparable to the corresponding coefficient of variation [4]. From this viewpoint, the normalized mean absolute error was very small compared with those regarded as acceptable values of 10% in areas of analytical chemistry [18] or 5 to 10% in food chemistry [19].…”

Section: Resultsmentioning

confidence: 97%

“…Although the R 2 value of 0.916 for the ‐cRGB a ∗ grayscale intensity may seem to be large, it was much smaller than the 0.992 result for the multiple regression model (Figure 6). This indicates that the multiple regression model was achieved using the combination of less linear variables as previously described [4].…”

Section: Resultsmentioning

confidence: 99%

“…Details of the image processing methods are described elsewhere [4, 14]. With Adobe Photoshop CS2 software (Adobe Systems Inc., California, USA), the tag image file format image generated ten grayscale tag image file format images that indicated the grayscale intensity values for the color components of RGB, cyan-magenta-yellow-key black (CMYK), and the International Commission and Illumination's L ∗ a ∗ b ∗ color models (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…For this reason, PackTest was originally a tool with discrete measures for the chemical characteristics. However, recent studies indicated the possibility that the PackTest products can be tools for accurately and quantitatively determining the chemical characteristics of liquid samples [4]. These tools can be used with continuous measures provided by computation such as regression.…”

Section: Introductionmentioning

confidence: 99%

“…The colored pack was optically scanned. The color image was used as the starting material for 83 grayscale images to enhance the information [4]. The grayscale intensity values for selected pixels that indicate the chemical characteristics were read and used for deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Doi

2019

Journal of Analytical Methods in Chemistry

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Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Doi

2019

Journal of Analytical Methods in Chemistry

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Review of present method of glucose from human blood and body fluids assessment

Fiedorova

Augustýnek

Kubíček

et al. 2022

Biosensors and Bioelectronics

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Hue‐shifting for accurate and precise quantification of biochemical substances using diagnostic test strips

Doi

2021

Coloration Technology

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Some test strips have high feasibility for the quick and approximate quantification of the biochemical substances in samples. Recently, test strips became more precise and accurate by involving the quantitative observation of coloration and regression modelling. Their accuracy and precision can be significantly improved by hue‐shifting the colours of the original sample and those of the standards. The standard colour images included in the instructions of test strips for the detection of four urinary and two salivary biochemical substances were used. Images of glucose‐loaded test strips for urinary glucose detection were also prepared. Using the red‐green‐blue (RGB) colour image, the author prepared 11 hue‐shifted colour images by changing the hue of the original RGB colour image at intervals of 30°. From each of the 12 colour images, 10 greyscale images indicating values of the intensity of RGB, cyan, magenta, yellow, key black, L*, a* and b* were prepared. In regression modelling, the additional 110 greyscale images improved the values of the coefficient of determination and normalised mean absolute error in the quantification of protein, specific gravity, urobilinogen and glucose in urine, as well as of alcohol and nitric oxide in saliva. Further analyses showed that the hue‐shifting technique yielded new greyscale images that were more significantly related to the values of the biochemical variables in question than any previous greyscale images. This improvement was realised by the most significant linear measures for the description of concentrations of biochemical substances, and synergetic effects between significant greyscale images.

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Synergistic Effects of Pseudocolor Imaging, Differentiation, and Square and Logarithmic Conversion on Accuracy of Quantification of Chemical Characteristics Using Test Strips and Similar Products

Cited by 5 publications

References 38 publications

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Review of present method of glucose from human blood and body fluids assessment

Hue‐shifting for accurate and precise quantification of biochemical substances using diagnostic test strips

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