Rapid testing of irradiation dose in beef and potatoes by reaction-based optical sensing technique

Shik, Anna V.; Sobolev, Pavel V.; Zubritskaya, Yana V.; Baytler, Maksim O.; Stepanova, Irina A.; Chernyaev, Alexander P.; Borschegovskaya, Polina Yu.; Zolotov, Sergey A.; Doroshenko, Irina A.; Podrugina, Tatyana A.; Bliznyuk, Ulyana A.; Rodin, Igor A.; Beklemishev, Mikhail K.

doi:10.1016/j.jfca.2023.105946

Cited by 5 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methodology as a whole is empirical, so the search for indicator reactions is empirical, too. However, the selection can be facilitated by using the pool of reactions previously developed for the discrimination of various samples. − …”

Section: Discussionmentioning

confidence: 99%

“…The data columns were considered as variables when the software was constructing the LDA model; the most significant variables were selected during that process (the algorithm is qualitatively described in paper 44 ). The accuracy of the model was evaluated by using the validation procedure in which a randomly selected portion of observations (usually, one of each six observations) was not used in model construction (validation set); when the model was built, it attempted to assign each of the test observations to one of the classes.…”

Section: Reaction-based Opticalmentioning

confidence: 99%

“…Most commonly, optical fingerprinting of samples was based on absorbance or intrinsic fluorescence measurements . The use of dyes to produce colorimetric sensor arrays started in late 1990s, and the addition of fluorophores to samples has been practiced since 2007; the latter approach makes it possible to measure intrinsic emission along with the fluorophore signal that is dependent on the composition of the sample. , Since late 2010s, a kinetic version of the technique has been employed, in which an indicator reaction (such as dye oxidation) is conducted in the presence of samples to be discriminated. − The kinetic approach adds dimensionality to the optical data on the one hand and allows to obtain signals from compounds and samples that do not exhibit intense fluorescence or absorption and do not form colored complexes, etc., on the other hand. This strategy showed its efficiency in the discrimination of bottled mineral waters, rennet samples, engine oils, and estimation of doses received by irradiated food. , …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Different Water Types for Engine Oil–Water-Soluble Fraction and Iron(2+) Using a Reaction-Based Optical Sensing Strategy: A Proof-of-Concept Study

Skorobogatov,

Shik,

Sobolev

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

Optical sensing is an inexpensive and simple technique in water pollution control that offers high sample throughput and field applications. In this work, we applied an innovative reaction-based optical fingerprinting strategy for the recognition of clean and synthetically contaminated waters. Samples of spring, well, borehole, pond, and tap water were introduced into the reaction mixtures containing carbocyanine dyes, the absorbance and fluorescence of which were monitored photographically at different times; the data were processed by linear discriminant analysis. Up to 11 clean water samples were completely discriminated; the samples artificially polluted with the soluble fraction of different brand engine oil [water-soluble fraction (WSF)] and iron(2+) supplied as Mohr's salt were discriminated from uncontaminated ones; the presence of 35-fold diluted WSF in water was also detected. Iron(2+) was detected in water samples at 3 μg/L using the same indicator reactions. The proposed strategy can be useful in water pollution monitoring.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Reaction-based Opticalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Different Water Types for Engine Oil–Water-Soluble Fraction and Iron(2+) Using a Reaction-Based Optical Sensing Strategy: A Proof-of-Concept Study

Skorobogatov,

Shik,

Sobolev

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results indicate that the k-nearest neighbour algorithm has the highest accuracy among algorithms for categorizing and comparing k-nearest neighbour patterns based on accuracy [48]. The k-nearest neighbours (k-NN) method is employed to process data [49]. In classification tasks, the class majority of K's nearest neighbours are used by k-NN to derive the class label of an input data point.…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

Texture Image Classification Using Wavelet Haar Approach and Gray Level Co-occurrence Matrix (GLCM)

Kiswanto,

Hadiyanto,

Sediyono

2024

Preprint

View full text Add to dashboard Cite

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

show abstract

“…The results showed that k-NN had the highest accuracy among algorithms for categorizing and comparing k-NN patterns, based on their accuracy [48]. The k-NN method was used for data processing [49].…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

Kiswanto,

Hadiyanto,

Sediyono

2024

ASI

View full text Add to dashboard Cite

This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.

show abstract

Rapid testing of irradiation dose in beef and potatoes by reaction-based optical sensing technique

Cited by 5 publications

References 33 publications

Monitoring Different Water Types for Engine Oil–Water-Soluble Fraction and Iron(2+) Using a Reaction-Based Optical Sensing Strategy: A Proof-of-Concept Study

Monitoring Different Water Types for Engine Oil–Water-Soluble Fraction and Iron(2+) Using a Reaction-Based Optical Sensing Strategy: A Proof-of-Concept Study

Texture Image Classification Using Wavelet Haar Approach and Gray Level Co-occurrence Matrix (GLCM)

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

Contact Info

Product

Resources

About