Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy

What is the best spectroscopic method for simultaneous analysis of organic acids and (poly)saccharides in biological matrices: Example of Aloe vera extracts?

“…Moreover, NIR and FTIR spectroscopy were used for analysis of terpens in Yaobitong capsules and crude soapnuts extracts, respectively [25,26]. Artificial neural networks (ANN) models were used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra [27]. The errors below 6% were established for the determination of hyperforin ((1R,5S,6R,7S)-4-Hydroxy-6-methyl-1,3,7-tris polysaccharide analysis, ATR-FTIR spectroscopy was able to differentiate cell wall polysaccharides (CWPs) according to the degree of methylation [29].…”

Development of ANN Models for Prediction of Physical and Chemical Characteristics of Oil-in-Aqueous Plant Extract Emulsions Using Near-Infrared Spectroscopy

“…According to the findings for the emulsions with oregano (Table 2), the ANNs established for the estimation of emulsion chemical characteristics had the maximum training, testing, and validation performances, followed by the ANNs established for the simultaneous assessment of chemical and physical characteristics and the ANNs established for the assessment of physical characteristics. According to the findings for the emulsions with oregano (Table 2), the ANNs established for the estimation of emulsion chemical characteristics had the maximum training, Equivalent outcomes were produced by Valinger et al [5] for modeling the physical and chemical properties of olive leaf aqueous extract based on NIR spectra, by Marić et al [53] and Jurinjak Tušek et al [54] for modeling the physical and chemical properties of root vegetable extract based on NIR spectra, and Valinger et al [26] for modeling the physical and chemical properties of industrial hemp aqueous extract based on NIR spectra. The same trend can be seen for raw spectra and the selected preprocessing methods.…”

Influence of Sample Matrix on Determination of Histamine in Fish by Surface Enhanced Raman Spectroscopy Coupled with Chemometric Modelling

“…To predict histamine concentration in fish samples, ANN were used in combination with PCA. Our experience in modelling with ANN in combination with PCA [40,42,43] has shown that preprocessing of spectral data before PCA analysis can sometimes improve the final result in terms of model performance [43]. Since preprocessing large amounts of data is time-consuming, laborious and requires modeling experience, in this work PCA was performed using the raw SERS spectra of the fish samples in order to minimize the required time for overall data analysis and thus provide faster method.…”