Browsing by Subject "Chicory"

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Continuous synthesis of 5‐hydroxymethylfurfural from biomass in on‐farm biorefinery
(2022) Świątek, Katarzyna; Olszewski, Maciej P.; Kruse, Andrea
5‐hydroxymethylfurfural (HMF) is the object of extensive research in recent times. The challenge in the industrial production of HMF is the choice of cheap, hexose feedstock. This study compares continuous HMF synthesis from hexoses—fructose and glucose, and biomass—Miscanthus × giganteus and chicory roots. The experiments were conducted in technical‐scale biorefinery (TRL 6/7). In the first stage, optimal conditions for the production of HMF from hexoses were selected using sulfuric acid as a catalyst in an aqueous medium. The following conditions were chosen for fructose: temperature of 200°C, the reaction time of 18 min, and pH = 2, and for glucose: 210°C, 18 min, and pH = 3. Under these conditions, the HMF yield was 56.5 mol% (39.6 wt.%) from fructose and 18.1 mol% (12.6 wt.%) from glucose. From the biomass, the HMF yields were 36.7 and 16.2 wt.% for miscanthus and chicory roots, respectively. Some results from the conversion of biomass solutions are unexpected and show a need for further investigations. This work has demonstrated the capacity to produce HMF from biomass as part of an environmentally friendly process in a biorefinery. Further research in this field and process optimization will be a step forward in the sustainable production of bioplastics.
Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder
(2023) Munyendo, Leah; Njoroge, Daniel; Zhang, Yanyan; Hitzmann, Bernd
Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.