Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs

Overview

Journal Front Plant Sci

Date 2021 Sep 20

PMID 34539716

Citations 3

Authors

Elizma van Wyngaard

Erna Blancquaert

Helene Nieuwoudt

Jose Luis Aleixandre-Tudo

Affiliations

Soon will be listed here.

Abstract

The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.

Citing Articles

A Comprehensive Study for Determination of Free Fatty Acids in Selected Biological Materials: A Review.

Ucar B, Gholami Z, Svobodova K, Hradecka I, Honig V Foods. 2024; 13(12).

PMID: 38928832 PMC: 11203194. DOI: 10.3390/foods13121891.

Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra.

Wakiuchi A, Jasial S, Asano S, Hashizume R, Hatanaka M, Ohnishi Y ACS Omega. 2023; 8(22):19781-19788.

PMID: 37305275 PMC: 10249027. DOI: 10.1021/acsomega.3c01515.

Optical Property Mapping of Apples and the Relationship With Quality Properties.

Peng H, Zhang C, Sun Z, Sun T, Hu D, Yang Z Front Plant Sci. 2022; 13:873065.

PMID: 35548279 PMC: 9084185. DOI: 10.3389/fpls.2022.873065.

References

Zapata C, Deleens E, Chaillou S, Magne C . Mobilisation and distribution of starch and total N in two grapevine cultivars differing in their susceptibility to shedding. Funct Plant Biol. 2020; 31(11):1127-1135. DOI: 10.1071/FP04028. View

Milovanovic M, Zeravik J, Oboril M, Pelcova M, Lacina K, cakar U . A novel method for classification of wine based on organic acids. Food Chem. 2019; 284:296-302. DOI: 10.1016/j.foodchem.2019.01.113. View

Fernandez-Novales J, Garde-Cerdan T, Tardaguila J, Gutierrez-Gamboa G, Perez-Alvarez E, Diago M . Assessment of amino acids and total soluble solids in intact grape berries using contactless Vis and NIR spectroscopy during ripening. Talanta. 2019; 199:244-253. DOI: 10.1016/j.talanta.2019.02.037. View

Gao W, Shu T, Liu Q, Ling S, Guan Y, Liu S . Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform-Raman Spectrometry. ACS Omega. 2021; 6(12):8578-8587. PMC: 8015071. DOI: 10.1021/acsomega.1c00400. View

Gutierrez S, Tardaguila J, Fernandez-Novales J, Diago M . Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer. PLoS One. 2015; 10(11):e0143197. PMC: 4658183. DOI: 10.1371/journal.pone.0143197. View

Aleixandre-Tudo J, Nieuwoudt H, Aleixandre J, du Toit W . Chemometric compositional analysis of phenolic compounds in fermenting samples and wines using different infrared spectroscopy techniques. Talanta. 2017; 176:526-536. DOI: 10.1016/j.talanta.2017.08.065. View

Turker-Kaya S, Huck C . A Review of Mid-Infrared and Near-Infrared Imaging: Principles, Concepts and Applications in Plant Tissue Analysis. Molecules. 2017; 22(1). PMC: 6155813. DOI: 10.3390/molecules22010168. View

Noronha H, Silva A, Dai Z, Gallusci P, Rombola A, Delrot S . A molecular perspective on starch metabolism in woody tissues. Planta. 2018; 248(3):559-568. PMC: 6096779. DOI: 10.1007/s00425-018-2954-2. View

Musingarabwi D, Nieuwoudt H, Young P, Eyeghe-Bickong H, Vivier M . A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis. Food Chem. 2015; 190:253-262. DOI: 10.1016/j.foodchem.2015.05.080. View

10.

Diago M, Fernandez-Novales J, Gutierrez S, Maranon M, Tardaguila J . Development and Validation of a New Methodology to Assess the Vineyard Water Status by On-the-Go Near Infrared Spectroscopy. Front Plant Sci. 2018; 9:59. PMC: 5797612. DOI: 10.3389/fpls.2018.00059. View

11.

Massart D . The use of information theory for evaluating the quality of thin-layer chromatographic separations. J Chromatogr. 1973; 79:157-63. DOI: 10.1016/s0021-9673(01)85284-5. View

12.

Pasquini C . Near infrared spectroscopy: A mature analytical technique with new perspectives - A review. Anal Chim Acta. 2018; 1026:8-36. DOI: 10.1016/j.aca.2018.04.004. View

13.

Linnet K . Evaluation of regression procedures for methods comparison studies. Clin Chem. 1993; 39(3):424-32. View

14.

Xu Z, Chen X, Meng L, Yu M, Li L, Shi W . Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model. Appl Spectrosc. 2019; 73(7):747-758. DOI: 10.1177/0003702819852174. View

15.

Aleixandre-Tudo J, Nieuwoudt H, du Toit W . Towards on-line monitoring of phenolic content in red wine grapes: A feasibility study. Food Chem. 2018; 270:322-331. DOI: 10.1016/j.foodchem.2018.07.118. View

16.

Tan C, Chen H, Wang C, Zhu W, Wu T, Diao Y . A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry. Spectrochim Acta A Mol Biomol Spectrosc. 2013; 105:1-7. DOI: 10.1016/j.saa.2012.12.023. View

17.

Schmidtke L, Smith J, Muller M, Holzapfel B . Rapid monitoring of grapevine reserves using ATR-FT-IR and chemometrics. Anal Chim Acta. 2012; 732:16-25. DOI: 10.1016/j.aca.2011.10.055. View

18.

Frizzarin M, Gormley I, Berry D, Murphy T, Casa A, Lynch A . Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods. J Dairy Sci. 2021; 104(7):7438-7447. DOI: 10.3168/jds.2020-19576. View

19.

Yen M, Lo L . Examining test-retest reliability: an intra-class correlation approach. Nurs Res. 2002; 51(1):59-62. DOI: 10.1097/00006199-200201000-00009. View

20.

Petrovic G, Aleixandre-Tudo J, Buica A . Viability of IR spectroscopy for the accurate measurement of yeast assimilable nitrogen content of grape juice. Talanta. 2019; 206:120241. DOI: 10.1016/j.talanta.2019.120241. View