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

Giovenzana, V.; Beghi, R.; Parisi, Simone; Brancadoro, Lucio; Guidetti, R.

doi:10.1002/jsfa.8676

Cited by 17 publications

(8 citation statements)

References 44 publications

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“…NIRS is a powerful technique that has enabled rapid analysis of various compositional parameters in wine grapes, must and grapevine tissues, grapevine water potential and starch. Accurate predictive models of starch have been reported for a range of woody tree systems including grapevine, Vitis spp., 73 tree species and Eucalyptus globulus using dried and ground samples.…”

Section: Introductionmentioning

confidence: 99%

“…12 Chemometrics combined with near-infrared reflectance spectroscopy (NIRS) has the potential to provide a faster and more reliable approach for quantification of starch concentrations in various tissues. 13,14 NIRS is a powerful technique that has enabled rapid analysis of various compositional parameters in wine grapes, must and grapevine tissues, 13,15,16 grapevine water potential [17][18][19] and starch. Accurate predictive models of starch have been reported for a range of woody tree systems including grapevine, Vitis spp., 8,20,21 73 tree species 22 and Eucalyptus globulus 23 using dried and ground samples.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of starch reserves in intact and ground grapevine cane wood tissues using near‐infrared reflectance spectroscopy

et al. 2020

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BACKGROUND Near‐infrared reflectance spectroscopy (NIRS) technology can be a powerful analytical technique for the assessment of plant starch, but generally samples need to be freeze‐dried and ground. This study investigated the feasibility of using NIRS technology to quantify starch concentration in ground and intact grapevine cane wood samples (with or without the bark layer). A partial least squares regression was used on the sample spectral data and was compared against starch analysis using a conventional wet chemistry method. RESULTS Accurate calibration models were obtained for the ground cane wood samples (n = 220), one based on 17 factors (R2 = 0.88, root mean square error of validation (RMSEV) of 0.73 mg g−1) and the other based on 10 factors (R2 = 0.85, RMSEV of 0.80 mg g−1). In contrast, the prediction of starch within intact cane wood samples was very low (R2 = 0.19). Removal of the cane bark tissues did not substantially improve the accuracy of the model (R2 = 0.34). Despite these poor correlations and low ratio of prediction to deviation values of 1.08–1.24, the root mean square error of cross‐validation (RMSECV) values were 0.75–0.86 mg g−1, indicating good predictability of the model. CONCLUSIONS As indicated by low RMSECV values, NIRS technology has the potential to monitor grapevine starch reserves in intact cane wood samples. © 2020 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of starch reserves in intact and ground grapevine cane wood tissues using near‐infrared reflectance spectroscopy

et al. 2020

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“…However, as a result of the effects of the spectrometer, some wave bands with poor spectral quality lead to model instability. In addition, redundant spectral information result in complex computations and increases the prediction performance 11 . Sampaio et al 12 .…”

Section: Introductionmentioning

confidence: 99%

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

et al. 2020

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BACKGROUND Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis. RESULTS First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS‐PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS‐CARS‐PLSDA model is as high as 98.41%. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry

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“…Due to the effects of the spectrometer, some wave bands with poor spectral quality will result in model instability. At the same time, redundant spectral information will lead to complex calculations and decrease prediction accuracy (Giovenzana, Beghi, Parisi, Brancadoro, & Guidetti, ). Therefore, it is necessary to carry out wavelength selection when constructing a near‐infrared analysis model.…”

Section: Introductionmentioning

confidence: 99%

Determination of 10‐Hydroxy‐2‐Decenoic Acid of Royal Jelly Using Near‐Infrared Spectroscopy Combined with Chemometrics

Yang

Wang³

et al. 2019

Journal of Food Science

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A rapid quantitative analysis model for determining the hydroxy‐2‐decenoic acid (10‐HDA) content of royal jelly based on near‐infrared spectroscopy combining with PLS has been developed. Firstly, near‐infrared spectra of 232 royal jelly samples with different 10‐HDA concentrations (0.35% to 2.44%) were be collected. Second‐order derivative processing of the spectra was carried out to construct a full‐spectrum PLS model. Secondly, GA‐PLS, CARS‐PLS, and Si‐PLS were used to select characteristic wavelengths from the second‐order derivative spectrum to construct a PLS calibration model. Finally, 58 samples were used to select the best predictive model for 10‐HDA content. The result show that the PLS model constructed after wavelength selection was significantly more accurate than the full spectrum model. The Si‐PLS algorithm performed best and the corresponding characteristic wavelength range were: 980 to 1038, 1220 to 1278, 1340 to 1398, and 1688 to 1746 nm. The prediction results were RMSEP = 0.1496% and RP = 0.9380. Hence, it is feasible to employ near‐infrared spectra to analyze 10‐HDA in royal jelly.

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

Cited by 17 publications

References 44 publications

Prediction of starch reserves in intact and ground grapevine cane wood tissues using near‐infrared reflectance spectroscopy

Prediction of starch reserves in intact and ground grapevine cane wood tissues using near‐infrared reflectance spectroscopy

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

Determination of 10‐Hydroxy‐2‐Decenoic Acid of Royal Jelly Using Near‐Infrared Spectroscopy Combined with Chemometrics

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