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Early cancer detection and treatment is an emerging and fascinating field of plasmonic nanobiosensor research. It paves to enrich a life without affecting living cells leading to a possible survival of the patient. This review describes a past and future prospect of an integrated research field on nanostructured metamaterials, microwave transmission, surface plasmonic resonance, nanoantennas, and their manifested versatile properties with nano-biosensors towards early cancer detection to preserve human health. Interestingly, (i) microwave transmission shows more advantages than other electromagnetic radiation in reacting with biological tissues, (ii) nanostructured metamaterial (Au) with special properties like size and shape can stimulate plasmonic effects, (iii) plasmonic based nanobiosensors are to explore the efficacy for early cancer tumour detection or single molecular detection and (iv) nanoantenna wireless communication by using microwave inverse scattering nanomesh (MISN) technique instead of conventional techniques can be adopted to characterize the microwave scattered signals from the biomarkers. It reveals that the nanostructured material with plasmonic nanobiosensor paves a fascinating platform towards early detection of cancer tumour and is anticipated to be exploited as a magnificent field in the future.

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In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

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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.

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Many reports have revealed that the abundance of microalgae in shrimp ponds vary with changes in environmental factors such as light, temperature, pH, salinity and nutrient level throughout a shrimp culture period. In this study, shrimp cultivation period was divided into three stages (initial = week 0-5, mid = week 6-10 and final = week 11-15). Physical and chemical parameters throughout the cultivation period were studied and species composition of microalgae was monitored. Physical parameters were found to fluctuate widely with light intensity ranging between 182.23-1278 µmol photon m(-2)s(-1), temperature between 29.56°C -31.59°C, dissolved oxygen (DO) between 4.56-8.21 mg/l, pH between 7.65-8.49 and salinity between 20‰-30‰. Ammonium (NH4 (+)-N), nitrite (NO2 (-)-N), nitrate (NO3 (-)-N), and orthophosphate (PO4 (3-)-P) concentrations in the pond at all cultivation stages ranged from 0.017 to 0.38 mg/l, 0.24 to 2.12 mg/l, 0.06 to 0.98 mg/l and 0.16 to 1.93 mg/l respectively. Statistical test (ANOVA) showed that there were no significant difference (p<0.05) in nutrients concentrations among the cultivation stages. All nutrients concentrations however were still in the tolerable level and safe for shrimp culture. The chlorophyll a contents were found to range from 5.03±2.17 to 32.61±0.35 µg/l throughout the cultivation period. A total of 19 microalgae species were found in the shrimp pond, with diatoms contributing up to 72% of the species followed by Chlorophyta (11%) and Cyanophyta (11%). However, weekly species abundance varied through the study period. At the initial stage, when there were no shrimps in the pond, Anabaena spp. and Oscillatoria spp. (Cyanophyta) were the dominant species, followed by Chlorella sp. and Dunaliella sp. (Chlorophyta). When shrimps were introduced into the pond, Amphora sp., Navicula sp. Gyrosigma sp. and Nitzschia sp. (diatoms) started to exist. At the middle and towards the final stage of the shrimp culture period diatoms were the dominant species. The Chlorophyta (Chlorella sp.) domination took place only twice, which was at week 2 and 13. The absence of some of the coastal water microalgae species in the shrimp pond was most likely due to the fact that they could not tolerate the physicochemical factors of harsh environment. In this study, Cylindrotheca closterium was regarded as the most tolerant species among the microalgae due to its ability to exist for 6 weeks out of the 15 weeks of cultivation.

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Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

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An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

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A disposable screen-printed e-tongue based on sensor array and pattern recognition that is suitable for the assessment of water quality in fish tanks is described. The characteristics of sensors fabricated using two kinds of sensing materials, namely (i) lipids (referred to as Type 1), and (ii) alternative electroactive materials comprising liquid ion-exchangers and macrocyclic compounds (Type 2) were evaluated for their performance stability, sensitivity and reproducibility. The Type 2 e-tongue was found to have better sensing performance in terms of sensitivity and reproducibility and was thus used for application studies. By using a pattern recognition tool i.e. principal component analysis (PCA), the e-tongue was able to discriminate the changes in the water quality in tilapia and catfish tanks monitored over eight days. E-tongues coupled with partial least squares (PLS) was used for the quantitative analysis of nitrate and ammonium ions in catfish tank water and good agreement were found with the ion-chromatography method (relative error, ±1.04- 4.10 %).