A new alcohol oxidase (AOX) enzyme-based formaldehyde biosensor based on acrylic microspheres has been developed. Hydrophobic poly(n-butyl acrylate-N-acryloxy-succinimide) [poly(nBA-NAS)] microspheres, an enzyme immobilization matrix, was synthesized using photopolymerization in an emulsion form. AOX-poly(nBA-NAS) microspheres were deposited on a pH transducer made from a layer of photocured and self-plasticized polyacrylate membrane with an entrapped pH ionophore coated on a Ag/AgCl screen printed electrode (SPE). Oxidation of formaldehyde by the immobilized AOX resulted in the production of protons, which can be determined via the pH transducer. Effects of buffer concentrations, pH and different amount of immobilization matrix towards the biosensor's analytical performance were investigated. The formaldehyde biosensor exhibited a dynamic linear response range to formaldehyde from 0.3-316.2 mM and a sensitivity of 59.41 ± 0.66 mV/decade (R(2) = 0.9776, n = 3). The lower detection limit of the biosensor was 0.3 mM, while reproducibility and repeatability were 3.16% RSD (relative standard deviation) and 1.11% RSD, respectively (n = 3). The use of acrylic microspheres in the potentiometric formaldehyde biosensor improved the biosensor's performance in terms of response time, linear response range and long term stability when compared with thick film immobilization methods.
A novel method for the rapid modification of fullerene for subsequent enzyme attachment to create a potentiometric biosensor is presented. Urease was immobilized onto the modified fullerene nanomaterial. The modified fullerene-immobilized urease (C60-urease) bioconjugate has been confirmed to catalyze the hydrolysis of urea in solution. The biomaterial was then deposited on a screen-printed electrode containing a non-plasticized poly(n-butyl acrylate) (PnBA) membrane entrapped with a hydrogen ionophore. This pH-selective membrane is intended to function as a potentiometric urea biosensor with the deposition of C60-urease on the PnBA membrane. Various parameters for fullerene modification and urease immobilization were investigated. The optimal pH and concentration of the phosphate buffer for the urea biosensor were 7.0 and 0.5 mM, respectively. The linear response range of the biosensor was from 2.31 × 10-3 M to 8.28 × 10-5 M. The biosensor's sensitivity was 59.67 ± 0.91 mV/decade, which is close to the theoretical value. Common cations such as Na+, K+, Ca2+, Mg2+ and NH4+ showed no obvious interference with the urea biosensor's response. The use of a fullerene-urease bio-conjugate and an acrylic membrane with good adhesion prevented the leaching of urease enzyme and thus increased the stability of the urea biosensor for up to 140 days.
The characteristics of a potentiometric biosensor for the determination of permethrin in treated wood based on immobilised cells of the fungus Lentinus sajor-caju on a potentiometric transducer are reported this paper. The potentiometric biosensor was prepared by immobilisation of the fungus in alginate gel deposited on a pH-sensitive transducer employing a photocurable acrylic matrix. The biosensor gave a good response in detecting permethrin over the range of 1.0-100.0 µM. The slope of the calibration curve was 56.10 mV/decade with detection limit of 1.00 µM. The relative standard deviation for the sensor reproducibility was 4.86%. The response time of the sensor was 5 min at optimum pH 8.0 with 1.00 mg/electrode of fungus L. sajor-caju. The permethrin biosensor performance was compared with the conventional method for permethrin analysis using high performance liquid chromatography (HPLC), and the analytical results agreed well with the HPLC method (at 95% confidence limit). There was no interference from commonly used organophosphorus pesticides such as diazinon, parathion, paraoxon, and methyl parathion.
A potentiometric whole cell biosensor based on immobilized marine bacterium, Pseudomonas carrageenovora producing κ-carrageenase and glycosulfatase enzymes for specific and direct determination of κ-carrageenan, is described. The bacterial cells were immobilized on the self-plasticized hydrogen ion (H+)-selective acrylic membrane electrode surface to form a catalytic layer. Hydrogen ionophore I was incorporated in the poly(n-butyl acrylate) [poly(nBA)] as a pH ionophore. Catalytic decomposition of κ-carrageenan by the bienzymatic cascade reaction produced neoagarobiose, an inorganic sulfate ion and a proton. The latter was detectable by H+ ion transducer for indirect potentiometric quantification of κ-carrageenan concentration. The use of a disposable screen-printed Ag/AgCl electrode (SPE) provided no cleaning requirement and enabled κ-carrageenan detection to be carried out conveniently without cross contamination in a complex food sample. The SPE-based microbial biosensor response was found to be reproducible with high reproducibility and relative standard deviation (RSD) at 2.6% (n = 3). The whole cell biosensor demonstrated a broad dynamic linear response range to κ-carrageenan from 0.2-100 ppm in 20 mM phosphate buffer saline (PBS) at pH 7.5 with a detection limit at 0.05 ppm and a Nernstian sensitivity of 58.78±0.87 mV/decade (R2 = 0.995). The biosensor showed excellent selectivity towards κ-carrageenan compared to other types of carrageenans tested e.g. ι-carrageenan and λ-carrageenan. No pretreatment to the food sample was necessary when the developed whole cell biosensor was employed for direct assay of κ-carrageenan in dairy product.
In this study, a potentiometric sensor composed of palm shell activated carbon modified with trioctylmethylammonium thiosalicylate (TOMATS) was used for the potentiometric determination of mercury ions in water samples. The proposed potentiometric sensor has good operating characteristics towards Hg (II), including a relatively high selectivity; a Nernstian response to Hg (II) ions in a concentration range of 1.0 × 10(-9) to 1.0 × 10(-2) M, with a detection limit of 1 × 10(-10) M and a slope of 44.08 ± 1.0 mV/decade; and a fast response time (~5 s). No significant changes in electrode potential were observed when the pH was varied over the range of 3-9. Additionally, the proposed electrode was characterized by good selectivity towards Hg (II) and no significant interferences from other cationic or anionic species.
The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.