Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH2Cl) residual. The decay of monochloramine releases ammonia (NH3), which is converted to nitrite (NO2-) and nitrate (NO3-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L-1.

Retinal and cortical signals initiated by a single cone type can be recorded using the spectral compensation (or silent substitution) paradigm. Moreover, responses to instantaneous excitation increments combined with gradual excitation decreases are dominated by the response to the excitation increment. Similarly, the response to a sudden excitation decrement dominates the overall response when combined with a gradual excitation increase. Here ERGs and VEPs were recorded from 34 volunteers [25.9 ± 10.4 years old (mean ± 1 SD); 25 males, 9 females] to sawtooth flicker (4 Hz) stimuli that elicited L- or M-cone responses using triple silent substitution. The mean luminance (284 cd/m2) and the mean chromaticity (x = 0.5686, y = 0.3716; CIE 1931 color space) remained constant and thus the state of adaptation was the same in all conditions. Color discrimination thresholds along protan, deutan, and tritan axes were obtained from all participants. Dichromatic subjects were genetically characterized by molecular analysis of their opsin genes. ERG responses to L-cone stimuli were absent in protanopes whereas ERG responses to M-cone stimuli were strongly reduced in deuteranopes. Dichromats showed generally reduced VEP amplitudes. Responses to cone-specific stimuli obtained with standard electrophysiological methods may give the same classification as that obtained with the Cambridge Colour Test and in some cases with the genetic analysis of the L- and M-opsin genes. Therefore, cone-specific ERGs and VEPs may be reliable methods to detect cone dysfunction. The present data confirm and emphasize the potential use of cone-specific stimulation, combined with standard visual electrodiagnostic protocols.

Over the past decades, imaging and spectroscopy techniques have been rapidly developing and widely applied in nondestructive fruit and vegetable quality assessment. The physical properties (including size, shape, color, position, and temperature) and biological properties (including cultivar, season, maturity level and geographical origin) of fruits and vegetables vary from one to another. A great variety of physical and biological properties of agricultural products influence the optical propagation properties and interaction behaviors with incident light, thus decreasing the quality inspection accuracy. Many attempts have been made in image correction and spectral compensation methods to improve the inspection accuracy. This paper gives a detailed summary about influence of physical and biological variability, as well as the correction and compensation methods for eliminating or reducing the effects in fruit and vegetable quality nondestructive inspection by using imaging and spectroscopy techniques. The advantages and disadvantages of the solution methods are discussed and summarized. Additionally, the future challenges and potential trends are also reported.

This chapter describes advantages and limitations of imaging flow cytometry (IFC) based on Imagestream instrumentation using a hybrid approach of morphometric measurement and quantitation of multiparametric fluorescent intensities\' distribution in cells and particles. Brief comparison is given of IFC with conventional flow cytometry and fluorescent microscopy. Some future directions of the IFC technology are described and discussed.