Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine

Giovenzana, V.; Civelli, R.; Beghi, R.; Oberti, R.; Guidetti, R.

doi:10.1016/j.talanta.2015.06.055

Cited by 54 publications

(29 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…designed and tested a simplified, LED‐based, modular system to be used for the rapid evaluation of fruit and vegetable quality, and preliminary tests performed on a lab scale using dye solutions gave encouraging results. The designed system was tested by the same authors for rapid ripeness evaluation of white grape ( Vitis vinifera L.). An analogous approach could be easily replicated for Ψ prediction.…”

Section: Resultsmentioning

confidence: 99%

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Frequently used evaluation methods include cluster analysis (Kuo, Su, Zulvia, & Lin, ; Płotka‐Wasylka, Simeonov, & Namieśnik, ; Srivastava, Mishra, & Mishra, ), principal component analysis (Carvalho, Pereira, Pereira, Pinto, & Marques, ; Giovenzana, Civelli, Beghi, Oberti, & Guidetti, ; Lv et al, ), neural network (Huang, Liu, & Ren, ; Jeswal & Chakraverty, ; King & Shellie, ), and sensory evaluation (Diako, Vixie, Weller, Dycus, & Ross, ; Tomadoni, Moreira, Pereda, & Ponce, ; Yu, Zhao, Li, Tian, & Ma, ). Płotka‐Wasylka et al () characterized the fruits used for wine‐making using cluster analysis, which is a traditional technique.…”

Section: Introductionmentioning

confidence: 99%

“…Płotka‐Wasylka et al () characterized the fruits used for wine‐making using cluster analysis, which is a traditional technique. Giovenzana et al () performed chemometric analysis to extract the useful information from the optical data of white grape wine, and performed principal component analysis to evaluate the resulting data. King and Shellie () predicted the non‐water‐stressed leaf temperature in wine grape using a neural network method.…”

Section: Introductionmentioning

confidence: 99%

Quality evaluation of Chinese red wine based on cloud model

2019

J Food Biochem

View full text Add to dashboard Cite

Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. Practical applications In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.

show abstract

“…Measurements using LEDs have been reported. Giovenzana et al [16] designed and tested an LED device that used only four wavelengths in the VIS-NIR region for rapid estimation of ripening parameters of white grapes. Schnable et al [17] reported a portable instrument with LED arrays that used six wavelengths for VIS-NIR absorption and turbidity analyses.…”

Section: Introductionmentioning

confidence: 99%

Reagentless Estimation of Urea and Creatinine Concentrations Using Near-Infrared Spectroscopy for Spot Urine Test of Urea-to-Creatinine Ratio

Suzuki

Ogawa

Seino

et al. 2018

ABE

View full text Add to dashboard Cite

In this work, we developed a measurement system that uses LEDs to estimate multiple components such as urea and creatinine in spot urine samples using near-infrared spectroscopy, considering future transition to LED light sources. In this study, we chose LEDs with 10 standard wavelengths (1400-2300 nm, in 100 nm increments). A multiple regression analysis using all combinations of 10 wavelengths was performed. We prepared glucose-added urine samples (GAU, urine samples from 10 healthy adults, each mixed with glucose). Wavelength selection was performed by comparing the minimum standard error of prediction (SEP, calculated from actual concentration and predicted concentration) for each wavelength combination. We obtained high accuracy for estimating urinary urea and creatinine levels (SEP: 42.4 mg/dl and 7.34 mg/dl, respectively) using four wavelengths for urea including two wavelengths showing negative absorbance, and ve wavelengths for creatinine. Furthermore, an extremely high correlation coef cient (γ > 0.99) was obtained for both components. We calculated urea concentration, creatinine concentration, and urea-to-creatinine ratio using this optical, reagentless method. The low SEP and high γ show that our method is suitable for practical determination of urea-to-creatinine ratio. Thus, this method of analyzing urine samples using NIR spectroscopy can be used to assess protein intake in CKD patients.

show abstract

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine

Cited by 54 publications

References 25 publications

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement

Quality evaluation of Chinese red wine based on cloud model

Reagentless Estimation of Urea and Creatinine Concentrations Using Near-Infrared Spectroscopy for Spot Urine Test of Urea-to-Creatinine Ratio

Contact Info

Product

Resources

About