Determination of microbial numbers in anaerobically digested biofertilisers.

This study aimed to quantity total numbers of bacteria, fungi and archaea in different types of commercial liquid anaerobic digestates, and to identify common patterns in their microbial numbers post-digestion and possible implications of their use as biofertiliser. Relationships between microbial numbers and physical-chemical traits of the digestates were also investigated. Quantification was performed using culturable and molecular (quantitative PCR) approaches. Bacterial and fungal CFUs ranged up to five orders of magnitude (105-1010; 0-105 g-1 DW, respectively) between different types of anaerobic digestates. Bacterial, archaeal and fungal gene copy numbers (GCN) varied by two orders of magnitude (108-1010; 107-109; 104-106 g-1 DW, respectively) between digestates. All microbial variables analysed showed significant differences between the different types of anaerobic digestate investigated (p < 0.05). Culturable microbial numbers for fungi (6.43 × 104 CFU g-1 DW) were much lower than for bacteria (2.23 × 109 CFU g-1 DW). Gene copy numbers were highest for bacteria (16S) (1.09 × 1010 g-1 DW), followed by archaea (16S) (5.87 × 108 g-1 DW), and fungi (18S) (1.77 × 106 g-1 DW). Liquid anaerobic digestates were predominantly dominated by bacteria, followed by archaeal and fungal populations. At 50% similarity level, the microbial profiles of the eleven anaerobic digestates tested separated into just two groups, indicating a broad relative degree of similarity in terms of microbial numbers. Higher bacterial (16S) GCN was associated with low OM and C/N ratio in digestates.

Physical-chemical traits, phytotoxicity and pathogen detection in liquid anaerobic digestates.

Anaerobic digestates, which are co-products from biogas production, have been recognised as potential biofertilisers for their benefits in nutrient recovery and recycling of different types of organic wastes. Due to the increasing number of different types of organic wastes being used to produce biogas, it is necessary to identify how different types of anaerobic digestates vary in their physical-chemical traits, and how these can impact upon their use as fertilisers. In addition, safe land spreading of anaerobic digestates must be within recommended limits for potentially toxic elements (PTEs) and pathogens. This study analysed physical-chemical traits, phytotoxicity, PTEs and indicator pathogens in a set of eleven different commercial liquid anaerobic digestates from Ireland and the UK, and compared them to the Irish draft standard for digestate. Liquid anaerobic digestates exhibited significant differences (P < 0.001) for most of the physical and chemical traits evaluated, with higher variability found for dry matter (DM) and K (CV = 17.2 and 16.8 respectively), and lower variation for pH and P (CV = 1.78 and 3.55 respectively). PTE concentrations were in general within recommended limits; nevertheless, some digestates showed higher concentrations than the recommended limits for Pb, Zn and Cu. Digestate from wastewater treatment feedstock was shown to be high in PTEs. Anaerobic digestates were found to negatively affect early stages of seed germination, but phytotoxicity effects were decreased by dilution in water. Levels of Salmonella spp. and E. coli were within recommended limits for most of the anaerobic digestates analysed.

Confirmation of declared provenance of European extra virgin olive oil samples by NIR spectroscopy.

The potential of near-infrared transflectance spectroscopy (1100-2498 nm) combined with chemometric techniques to confirm the geographical origin of European olive oil samples was evaluated. In total, 913 extra virgin olive oil samples (210 Ligurian and 703 non-Ligurian) were collected over three consecutive harvests (2005, 2006, and 2007). A multivariate spectral fingerprint for Ligurian olive oil was developed and deployed to confirm or refute a claim that any given sample was Ligurian. Samples were pseudorandomly split into calibration (n = 280) and validation sets (n = 633); the only selection constraint applied was to insist on equal numbers of Ligurian and non-Ligurian samples in the calibration set. Following preliminary examination by principal component analysis, the full spectrum modeling method applied to the spectral data set was discriminant partial least-squares regression; various data pretreatments were also investigated. The best models correctly predicted the origins of samples in the prediction set up to 92.8 and 81.5% for Ligurian and non-Ligurian olive oil samples, respectively, using a first-derivative data pretreatment. The potential of this approach in commercial traceability and quality assurance schemes is noted.

Geographical classification of honey samples by near-infrared spectroscopy: a feasibility study.

The potential of near-infrared (NIR) spectroscopy to determine the geographical origin of honey samples was evaluated. In total, 167 unfiltered honey samples (88 Irish, 54 Mexican, and 25 Spanish) and 125 filtered honey samples (25 Irish, 25 Argentinean, 50 Czech, and 25 Hungarian) were collected. Spectra were recorded in transflectance mode. Following preliminary examination by principal component analysis (PCA), modeling methods applied to the spectral data set were partial least-squares (PLS) regression and soft independent modeling of class analogy (SIMCA); various pretreatments were investigated. For unfiltered honey, best SIMCA models gave correct classification rates of 95.5, 94.4, and 96% for the Irish, Mexican, and Spanish samples, respectively; PLS2 discriminant analysis produced a 100% correct classification for each of these honey classes. In the case of filtered honey, best SIMCA models produced correct classification rates of 91.7, 100, 100, and 96% for the Argentinean, Czech, Hungarian, and Irish samples, respectively, using the standard normal variate (SNV) data pretreatment. PLS2 discriminant analysis produced 96, 100, 100, and 100% correct classifications for the Argentinean, Czech, Hungarian, and Irish honey samples, respectively, using a second-derivative data pretreatment. Overall, while both SIMCA and PLS gave encouraging results, better correct classification rates were found using PLS regression.