Comparison between Hyperspectral Imaging and Chemical Analysis of Polyphenol Oxidase Activity on Fresh-Cut Apple Slices

Shrestha, Luna; Kulig, Boris; Moscetti, Roberto; Massantini, Riccardo; Pawelzik, Elke; Hensel, Oliver; Sturm, Barbara

doi:10.1155/2020/7012525

Cited by 9 publications

(9 citation statements)

References 56 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparison between devices was made by the Passing-Bablok regression, the orthogonal regression (Deming regression), and the Bland-Altman plot analysis [16-18]. A paired dataset was built by the TBLN or IVC from the portable ultrasound machine as the X-variable and the TBLN or IVC from the handheld ultrasound machine as the Y-variable for the Passing-Bablok regression and the Deming regression analysis.…”

Section: Methodsmentioning

confidence: 99%

Ultrasonographic Assessment of Extravascular Lung Water in Hospitalized Patients Requiring Hemodialysis: A Prospective Observational Study

et al. 2021

View full text Add to dashboard Cite

Introduction: Sonographic technologies can estimate extravascular lung water (EVLW) in hemodialysis (HD) patients. This study investigated the suitability of a handheld scanner in contrast to a portable scanner for quantifying EVLW in hospitalized patients requiring HD. Methods: In this prospective study, 54 hospitalized HD patients were enrolled. Bedside lung ultrasound was performed within 30 min before and after dialysis using handheld (phased array transducer, 1.7–3.8 MHz) and portable (curved probe, 5–2 MHz) ultrasound devices. Eight lung zones were scanned for total B-lines number (TBLN). The maximum diameter of inferior vena cava (IVC) was measured. We performed Passing-Bablok regression, Deming regression, Bland-Altman, and logistic regression analysis. Results: The 2 devices did not differ in measuring TBLN and IVC (p > 0.05), showing a high correlation (r = 0.92 and r = 0.51, respectively). Passing-Bablok regression had a slope of 1.11 and an intercept of 0 for TBLN, and the slope of Deming regression was 1.02 within the CI bands of 0.94 and 1.11 in the full cohort. TBLN was logarithmically transformed for Bland-Altman analysis, showing a bias of 0.06 (TBLN = 1.2) between devices. The slope and intercept of the Deming regression in IVC measurements were 0.77 and 0.46, respectively; Bland-Altman plot showed a bias of −0.07. Compared with predialysis, TBLN significantly (p < 0.001) decreased after dialysis, while IVC was unchanged (p = 0.16). Univariate analysis showed that cardiovascular disease (odds ratio [OR] 8.94 [2.13–61.96], p = 0.002), smoking history (OR 5.75 [1.8–20.46], p = 0.003), and right pleural effusion (OR 5.0 [1.2–25.99], p = 0.03) were strong predictors of EVLW indicated by TBLN ≥ 4. Conclusion: The lung and IVC findings obtained from handheld and portable ultrasound scanners are comparable and concordant. Cardiovascular disease and smoking history were strong predictors of EVLW. The use of TBLN to assess EVLW in hospitalized HD patients is feasible. Further studies are needed to determine if TBLN can help guide volume removal in HD patients.

show abstract

Section: Methodsmentioning

confidence: 99%

Ultrasonographic Assessment of Extravascular Lung Water in Hospitalized Patients Requiring Hemodialysis: A Prospective Observational Study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The computed results from both regression methods in this study show the slopes and intercepts of PBR and DR regressions as in Table 3 are not confidently different from 1 and 0 for all quality parameters with orthogonal fit ratios of 1.52, 0.99, 0.81, 0.54, and 0.52 for total carotenoids, moisture content (% wb), L* , a* , and b* , respectively. The observed orthogonal fit ratios which differ from an ideal value of 1 imply that both measuring techniques possess different measurement uncertainties due to analytical errors, seasonal variations, or environmental conditions that could possibly contribute to quality variations for each root and finally influence the measured values (Shrestha et al, 2020; Ludbrook, 2010). A proportional and systematic difference (or BIAS) between the laboratory analysis and the HSI method was not observed in this study since both intercepts and slopes are confidently different from 0 and 1.…”

Section: Resultsmentioning

confidence: 99%

“…The VIP plots of total carotenoids, moisture content (%wb), L*, a*, and b* of carrots at 323 wavelengths from 500.55 to 1006.58 nm are displayed as in Figure 6. In this study, any wavelengths with a VIP score of more than 0.8 are considered as an important and significant wavelength for quality prediction (Shrestha et al, 2020). 1 show noticeable peaks at the wavelength between 520 to 545 nm and the highest peak in that region was observed at 531 nm which can be attributed to total carotenoids content (Haas et al, 2019).…”

Section: Analysis Of Spectral Reflectance and Wavelength Selection Ba...mentioning

confidence: 99%

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Saleh

Kulig

Arefi

et al. 2022

Food Processing Preservation

Self Cite

View full text Add to dashboard Cite

The objective of this paper was to evaluate the performance of Partial Least Square Regression (PLSR) model and to assess the statistical agreement between two different measurement techniques, that is, Vis–NIR hyperspectral imaging (HSI) and standard laboratory methods for quality evaluation of dried carrots throughout the hot‐air drying process. Carrots at commercial maturity of 3.5 months after planting were harvested in two seasons (2017 and 2018) and dried in a convective hot air dryer at 50°C, 60°C, and 70°C. Quality measurements were examined at intervals of 30 minutes. PLSR was performed as a regression model to predict quality attributes in carrots, while Passing–Bablok and Deming regressions alongside Blant–Altman analysis were applied as method comparisons. Excellent prediction performance for moisture content was observed with high R2T and R2v at 0.92 and 0.90 with values of RMSET and RMSEv at 8.15% and 8.16%. Satisfactory prediction accuracies were observed for total carotenoids (R2v = 0.64 and RMSEv = 32.62) μg/g, L* (R2v = 0.68 and RMSEv = 32.62), a* (R2v = 0.69 and RMSEv = 1.18), and b* (R2v = 0.60 and RMSEv = 1.45). Selected wavelengths for total carotenoids, moisture content, L*, a*, and b* based on the highest score of VIP loadings were 531, 973, 531, 531, and 680 nm, respectively. An adequate agreement of Blant–Altman analysis between the two methods within the upper and lower limits of 95% confidence interval (CI) were obtained for total carotenoids from 95.68 μg/g to 82.34 μg/g, moisture content (25.18% to 22.93%), L* (2.88 to −3.30), a* (4.15 to 3.43), and b* (4.53 to −3.11) with mean differences at 6.67, 1.12, −0.21, 0.36, and 0.71, respectively. Good correlation coefficients (r) were also observed at 0.89, 0.91, 0.78, and 0.83 for moisture content, L*, a*, and b* with a moderate correlation of total carotenoids at 0.69. The results indicate the potential feasibility of using non‐invasive measurement of quality attributes using hyperspectral imaging during the drying of carrots. Novelty impact statement non‐invasive measurement using hyperspectral imaging for quality determination in carrots during convective drying demonstrated promising results. Multivariate analysis of Partial Least Square Regression showed a good modeling performance for quality prediction in dried carrots. A good statistical agreements between non‐invasive quality measurements using hyperspectral imaging and standard laboratory analysis were achieved by comparative analysis using Blant–Altman plot, Deming, and Passing–Bablok regression.

show abstract

“…They analyzed the correlation indices between the spectral reflectance parameters and the phenolic compounds. Similarly, there are various studies focus on analyzing the chemical compounds in plants and food products (Wold et al, 2016;Morales-Sillero et al, 2018;Shrestha et al, 2020;Amanah et al, 2021). These studies provide an opportunity to analyze the SWIR and VIS/NIR spectra and measure and analyze the changes in phenolic compounds in plants grown under various stress conditions at the same time.…”

Section: Model Performancementioning

confidence: 99%

Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions

et al. 2022

View full text Add to dashboard Cite

Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400–1,000 nm (VIS/NIR) and 900–2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants’ total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.

show abstract

Comparison between Hyperspectral Imaging and Chemical Analysis of Polyphenol Oxidase Activity on Fresh-Cut Apple Slices

Cited by 9 publications

References 56 publications

Ultrasonographic Assessment of Extravascular Lung Water in Hospitalized Patients Requiring Hemodialysis: A Prospective Observational Study

Ultrasonographic Assessment of Extravascular Lung Water in Hospitalized Patients Requiring Hemodialysis: A Prospective Observational Study

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions

Contact Info

Product

Resources

About