Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO₂, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

In the present study, we used 12 genotypes of sorghum originated from different countries (five sweet, four grain and three forage). These different genotypes and types of sorghum were evaluated for the agro-morphological traits that are associated with the estimated sugar and bioethanol yield to estimate their phenotypic diversity. Analysis of variance showed significant differences between different types of sorghum for all the evaluated traits. Sweet sorghum genotypes, however, showed better performance with respect to all studied traits than the other genotypes. A positive significant correlation was observed between plant height, leaf number, leaf area, biomass yield, cane and bagasse yields, and the predicted bioethanol yield. Both, cluster and principal component analysis were performed to group the genotypes according to their agro-morphological and molecular similarity coefficients. For analytical approaches, the Iranian grain and forage genotypes clustered separately from the other genotypes. The clustering patterns obtained from the molecular dominant markers had higher discriminatory power than using morphological characters to separate sweet genotypes from the forage and grain sorghum ones. The results clearly indicated that sweet sorghum can be grown in Germany and maintains its superiority in biomass production and sugar yield over grain and forage sorghum types.

Sweet, grain, and dual-purpose sorghums differ in a number of important traits, including biomass production, total solutes in the stem juice, and sugar accumulation across the stem. Ten dual-purpose hybrids, two sweet genotypes, and two grain landraces of sorghums were characterized under temperate environmental conditions to determine their potential for bioethanol production. Five sorghum hybrids (Ganymed, Hannibal, Tarzan, Merlin, and Zerberus) performed better with respect to cane yield, juice yield, potential sugar, and ethanol yields compared to sweet and grain genotypes. While the sweet genotype KIT1 produced the highest sugar concentration in the stem, the lowest concentration was produced by the grain landrace Razinieh. The study showed that plant height, leaf number, leaf weight, cane yield, and juice yield were positively correlated with the sugar yield in fresh stalk. Sugar accumulation was higher in the central internodes of all genotypes. Clustering analysis showed that sweet genotypes are located more closely to dual-purpose hybrids than grain landraces. We discuss the results with respect to the potential of dual-purpose sorghum hybrids for bio-economy in Germany.

Amaranth has acquired great economic impact as functional food, with species originating from Mexico dominating global trade. In contrast, the Peruvian A. caudatus (kiwicha) has been vastly neglected, although it is endowed with very promising nutritive traits. Morphological plasticity and taxonomic ambiguities render authentication of Amaranth difficult, such that the identity of commercial samples is often unclear. To safeguard the authenticity of kiwicha and, thus, consumer safety, we characterised a germplasm collection of 84 Amaranth accessions on both, the morphological and the genetic level. We show that kiwicha can be delineated phenotypically from other species by its late flowering, taller posture, and lower grain yields. Instead, flower and seed color, often used as proxy for identity, do not qualify as taxonomic markers. Using the plastidic barcoding marker psbA-trnH igs we were able to identify a specific Single Nucleotide Polymorphism (SNP) that separated kiwicha from all other species of Amaranth. This allowed us to develop a sequencing-free authentication assay using an Amplified Refractory Mutation System (ARMS) strategy. As a result kiwicha in commercial samples can be authenticated by a single duplex-PCR yielding a diagnostic side band reporting A. caudatus against all other species of Amaranthus. This fingerprinting assay will help to develop the nutritive potential of kiwicha and to safeguard seed material for A. caudatus against adulteration by the far more prevalent species from Mexico.

Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.