John C. Cancilla

Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase.

The use of molecularly modified Si nanowire field effect transistors (SiNW FETs) for selective detection in the liquid phase has been successfully demonstrated. In contrast, selective detection of chemical species in the gas phase has been rather limited. In this paper, we show that the application of artificial intelligence on deliberately controlled SiNW FET device parameters can provide high selectivity toward specific volatile organic compounds (VOCs). The obtained selectivity allows identifying VOCs in both single-component and multicomponent environments as well as estimating the constituent VOC concentrations. The effect of the structural properties (functional group and/or chain length) of the molecular modifications on the accuracy of VOC detection is presented and discussed. The reported results have the potential to serve as a launching pad for the use of SiNW FET sensors in real-world counteracting conditions and/or applications.

A Highly Sensitive Diketopyrrolopyrrole‐Based Ambipolar Transistor for Selective Detection and Discrimination of Xylene Isomers

An ambipolar poly(diketopyrrolopyrrole-terthiophene)-based field-effect transistor (FET) sensitively detects xylene isomers at low ppm levels with multiple sensing features. Combined with pattern-recognition algorithms, a sole ambipolar FET sensor, rather than arrays of sensors, can discriminate highly similar xylene structural isomers from one another.

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.

Estimation with neural networks of the water content in imidazolium-based ionic liquids using their experimental density and viscosity values

A multilayer perceptron neural network (NN) model has been created for the estimation of the water content present in the following ionic liquids (ILs): 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium methylsulfate, 1,3-dimethylimidazolium methylsulfate and 1-ethyl-3-methylimidazolium ethylsulfate. To achieve this goal, their density and viscosity values were used. The experimental values of these physicochemical properties, employed to design the NN model, were measured and registered at 298.15K. They were determined at different relative humidity values ranging from 11.1 to 84.3%. The estimated results were then compared with the experimental measurements of the water content, which were carried out by the Karl Fischer technique, and the difference between the real and estimated values was less than 0.05 and 3.1% in the verification and validation processes, respectively. In addition, an external validation process was developed using four bibliographical references. In this case, the mean prediction error was less than 6.3%. In light of these results, the NN model shows an acceptable goodness of fit, sufficient robustness, and an adequate estimative capacity to determine the water content inside the studied range of the ILs analyzed.

Neural networks to estimate the water content of imidazolium-based ionic liquids using their refractive indices.

A non-linear model has been developed to estimate the water content of 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium methylsulfate, and 1,3-dimethylimidazolium methylsulfate ionic liquids using their respective refractive index values. The experimental values measured to design the neural network (NN) model were registered at 298.15K. These were determined at different relative humidity values which ranged from 11.1% to 84.3%. The estimated results were compared with experimental measurements of water content obtained by the Karl Fischer technique, and the differences between the real and estimated values were less than 0.06% in the internal validation process. In addition, an external validation test was developed using bibliographical references. In this case, the mean prediction error was less than 5.4%. In light of these results, the NN model shows an acceptable goodness of fit, sufficient robustness, and a more than adequate predictive capacity to estimate the water content of the ILs through the analysis of their refractive index.

Linking Chemical Parameters to Sensory Panel Results through Neural Networks To Distinguish Olive Oil Quality

A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.

Identifying and Quantifying Adulterants in Extra Virgin Olive Oil of the Picual Varietal by Absorption Spectroscopy and Nonlinear Modeling

In this research, the detection and quantification of adulterants in one of the most common varieties of extra virgin olive oil (EVOO) have been successfully carried out. Visible absorption information was collected from binary mixtures of Picual EVOO with one of four adulterants: refined olive oil, orujo olive oil, sunflower oil, and corn oil. The data gathered from the absorption spectra were used as input to create an artificial neural network (ANN) model. The designed mathematical tool was able to detect the type of adulterant with an identification rate of 96% and to quantify the volume percentage of EVOO in the samples with a low mean prediction error of 1.2%. These significant results make ANNs coupled with visible spectroscopy a reliable, inexpensive, user-friendly, and real-time method for difficult tasks, given that the matrices of the different adulterated oils are practically alike.

Programmed Nanoparticles for Tailoring the Detection of Inflammatory Bowel Diseases and Irritable Bowel Syndrome Disease via Breathprint

Chemical sensors based on programmable molecularly modified gold nanoparticles are tailored for the detection and discrimination between the breathprint of irritable bowel syndrome (IBS) and inflammatory bowel diseases (IBD). The sensors are examined in both lab- and real-world clinical conditions. The results reveal a discriminative power accuracy of 81% between IBD and IBS and 75% between Crohns and Colitis states.

Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems.

A common phenomenon that takes place in bottled extra virgin olive oil (EVOO) is the photooxidation of its pigments, especially chlorophyll, which acts as a singlet-oxygen sensitizer. This translates into a severe decrease of quality, potentially leading to oxidized and rancid olive oils by the time they reach to the consumers. In this current research, the photochemical degradation has been monitored for 45 days in binary mixtures of four monovarietal EVOOs (Arbequina, Hojiblanca, Cornicabra, and Picual) through UV-Visible spectroscopy. A multilayer perceptron-based model was optimized to estimate the photodegradation suffered by the samples, in terms of photodegradation time, relying on the spectroscopic information gathered and attaining an error rate of 2.43 days (5.3%) in the determination of this parameter.

Neural networks applied to characterize blends containing refined and extra virgin olive oils

The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2.14%. These results turn visible spectroscopy and MLP models into a trustworthy, user-friendly, low-cost technique which can be implemented on-line to characterize olive oil mixtures containing refined olive oil and EVOOs.