Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms

Identifying key wavenumbers that improve prediction of amylose in rice samples utilizing advanced wavenumber selection techniques

This study utilizes advanced wavenumber selection techniques to improve the prediction of amylose content in grounded rice samples with near-infrared spectroscopy. Four different wavenumber selection techniques, i.e. covariate selection (CovSel), variable combination population analysis (VCPA), bootstrapping soft shrinkage (BOSS) and variable combination population analysis-iteratively retains informative variables (VCPA-IRIV), were used for model optimization and key wavenumbers selection. The results of the several wavenumber selection techniques were compared with the predictions reported previously on the same data set. All the four wavenumber selection techniques improved the predictive performance of amylose in rice samples. The best performance was obtained with VCPA, where, with only 11 wavenumbers-based model, the prediction error was reduced by 19% compared to what reported previously on the same data set. The selected wavenumbers can help in development of low-cost multi-spectral sensors for amylose prediction in rice samples.

NIR Analysis of Intact Grape Berries: Chemical and Physical Properties Prediction Using Multivariate Analysis

Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is not possible to test multiple times or perform any other analysis on the same sample. In this article, near-infrared (NIR) spectroscopy was applied to intact berries of table grape cv. Regal Seedless. NIR spectra were employed to predict both the physical parameter “hardness”, which is correlated with the crunchiness of berry flesh and the sweetness, which is correlated with the total soluble solids content (TSS, as °Brix). The chemometric analysis was carried out exclusively based on an open-source software environment, producing results readily usable for any operator, besides the specific level of experience with NIR spectroscopy.

Application of Near-Infrared Spectroscopy to Investigate Some Endogenic Properties of Pleurotus ostreatus Cultivars

Fourteen different Pleurotus ostreatus cultivars (Po_1–Po_14) were tested for free amino acid content (fAA), total polyphenol content (TPC), and antioxidant capacity (Ferric Reducing Ability of Plasma—FRAP) to select the cultivars with the most favorable traits. Automatic amino acid analyzer (fAA) and spectrophotometric assay (TPC, FRAP) results as well as Fourier-transform near infrared (FT-NIR) spectra were evaluated with different chemometric methods (Kruskal–Wallis test, Principal Component Analysis—PCA, Linear Discriminant Analysis—LDA). Based on total free amino acid concentrations and FRAP values, the Po_2 cultivar was found to be the most favorable. Types Po_3, Po_8, Po_10 and Po_12 were separated using PCA. Based on the spectral profile, they may contain polyphenols and reducing compounds of different qualities. LDA classification that was based on the concentrations of all free amino acids, cysteine, and proline of the cultivars was performed with an accuracy of over 90%. LDA classification that was based on the TPC and FRAP values was performed with an accuracy of over 83%.