Back to Search
Start Over
Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction
- Source :
- Foods, Vol 11, Iss 3, p 281 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- In this article, a combination of non-destructive NIR spectroscopy and machine learning techniques was applied to predict the texture parameters and the total soluble solids content (TSS) in intact berries. The multivariate models obtained by building artificial neural networks (ANNs) and applying partial least squares (PLS) regressions showed a better prediction ability after the elimination of uninformative spectral ranges. A very good prediction was obtained for TSS and springiness (R2 0.82 and 0.72). Qualitative models were obtained for hardness and chewiness (R2 0.50 and 0.53). No satisfactory calibration model could be established between the NIR spectra and cohesiveness. Textural parameters of grape are strictly related to the berry size. Before any grape textural measurement, a time-consuming berry-sorting step is compulsory. This is the first time a complete textural analysis of intact grape berries has been performed by NIR spectroscopy without any a priori knowledge of the berry density class.
- Subjects :
- PCA
ANN
PLS
MC-UVE
β coefficients
R statistics
Chemical technology
TP1-1185
Subjects
Details
- Language :
- English
- ISSN :
- 23048158
- Volume :
- 11
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Foods
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f7d442a24f2f4fa9980c88cbb5e821e4
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/foods11030281