Prediction of starch reserves in intact and ground grapevine cane wood tissues using near‐infrared reflectance spectroscopy
| Autor: | Thomas Rodemann, Caroline Claye, Alieta Eyles, B Dambergs, Dugald C. Close, Joanna E. Jones |
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| Rok vydání: | 2020 |
| Předmět: |
030309 nutrition & dietetics
Starch 03 medical and health sciences chemistry.chemical_compound 0404 agricultural biotechnology Partial least squares regression Vitis Least-Squares Analysis Cane Spectral data 0303 health sciences Spectroscopy Near-Infrared Nutrition and Dietetics biology food and beverages 04 agricultural and veterinary sciences biology.organism_classification Wood 040401 food science Starch analysis Horticulture chemistry visual_art Calibration visual_art.visual_art_medium Environmental science Near infrared reflectance spectroscopy Bark Agronomy and Crop Science Food Science Biotechnology |
| Zdroj: | Journal of the Science of Food and Agriculture. 100:2418-2424 |
| ISSN: | 1097-0010 0022-5142 |
| DOI: | 10.1002/jsfa.10253 |
| Popis: | 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 (PLS) 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 (RPD) 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. Conclusion: As indicated by low RMSECV values, NIRS technology has the potential to monitor grapevine starch reserved in intact cane wood samples. |
| Databáze: | OpenAIRE |
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