Machine learning accurately predicts food exchange list and the exchangeable portion.

Introduction: Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion. Methods: Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost. Results: The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents. Discussion: Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

Development of a surface plasmon resonance based immunosensor for diclofenac quantification in water.

A Surface Plasmon Resonance (SPR) biosensor based on an inhibition immunoassay was developed for the detection of diclofenac (DCF) in aqueous solution. Due to the small size of DCF, an hapten-protein conjugate was produced by coupling DCF to bovine serum albumin (BSA). DCF-BSA conjugate formation was confirmed via MALDI-TOF mass spectrometry. The resulting conjugate was immobilized onto the surface of a sensor fabricated via e-beam deposition of a 2 nm chromium adhesion layer followed by a 50 nm gold layer onto precleaned BK7 glass slides. Immobilization onto the nano thin gold surface was accomplished by covalent amide linkage through a self-assembled monolayer. Samples were composed of a mixture of antibody at a fixed concentration and DCF at different known concentrations in deionized water, causing the inhibition of anti-DCF on the sensor. The DCF-BSA was obtained with a ratio of 3 DCF molecules per BSA. A calibration curve was performed using concentrations between 2 and 32 µg L-1. The curve was fitted using the Boltzmann equation, reaching a limit of detection (LOD) of 3.15 µg L-1 and limit of quantification (LOQ) of 10.52 µg L-1, the inter-day precision was calculated and an RSD value of 1.96% was obtained; and analysis time of 10 min. The developed biosensor is a preliminary approach to the detection of DCF in environmental water samples, and the first SPR biosensor developed for DCF detection using a hapten-protein conjugate.

Optical Biosensors and Their Applications for the Detection of Water Pollutants.

The correct detection and quantification of pollutants in water is key to regulating their presence in the environment. Biosensors offer several advantages, such as minimal sample preparation, short measurement times, high specificity and sensibility and low detection limits. The purpose of this review is to explore the different types of optical biosensors, focusing on their biological elements and their principle of operation, as well as recent applications in the detection of pollutants in water. According to our literature review, 33% of the publications used fluorescence-based biosensors, followed by surface plasmon resonance (SPR) with 28%. So far, SPR biosensors have achieved the best results in terms of detection limits. Although less common (22%), interferometers and resonators (4%) are also highly promising due to the low detection limits that can be reached using these techniques. In terms of biological recognition elements, 43% of the published works focused on antibodies due to their high affinity and stability, although they could be replaced with molecularly imprinted polymers. This review offers a unique compilation of the most recent work in the specific area of optical biosensing for water monitoring, focusing on both the biological element and the transducer used, as well as the type of target contaminant. Recent technological advances are discussed.

Effective medium theory and its limitations for the description of MoO3films doped with nanoparticles.

The present work analyses the transmittance and reflectance spectra of molybdenum trioxide film doped by silver nanoparticles as a function of angle of incidence and wavelength. As will be seen in this work, at values of angle of incidence below 40 degrees and with volume filling fraction below 1% also, some differences between the two effective medium theories are presented. First, the volume filling fraction is limited for low values (<1%) and second the scattering amplitude cannot be ignored for these cases. The novelty of this work is that the use of the effective medium model (refractive index) shows limitations in the description of the optical properties when it was applied to thin solid films.

Extinction Coefficient Modulation of MoO3 Films Doped with Plasmonic Nanoparticles: From an Effective Medium Theory Description.

This work focused on the application of the effective medium theory to describe the extinction coefficient (Qext) in molybdenum trioxide (MoO3) doped with different kinds of plasmonic nanoparticles, such as silver (Ag), gold (Au), and copper (Cu). Usually, in studies of these materials, it is normal to analyze the transmission or absorption spectra. However, the effect of this type or size of nanoparticles on the spectra is not as remarkable as the effect that is found by analyzing the Qext of MoO3. It was shown that the ß-phase of MoO3 enhanced the intensity response of the Qext when compared to the α-phase of MoO3. With a nanoparticle size of 5 nm, the Ag-doped MoO3 was the configuration that presents the best response in Qext. On the other hand, Cu nanoparticles with a radius of 20 nm embedded in MoO3 was the configuration that presented intensities in Qext similar to the cases of Au and Ag nanoparticles. Therefore, implementing the effective medium theory can serve as a guide for experimental researchers for the application of these materials as an absorbing layer in photovoltaic cells.

Effective medium theory to the description of plasmonic resonances: Role of Au and Ti nanoparticles embedded in MoO3 thin films.

The growing interest in functional transition metal oxides for efficient energy consumption or in the bio-sensing process; indicates that is necessary to develop a new theoretical method that describes experiments. This article presents a new theoretical methodology to characterize molybdenum trioxide (MoO3) thin films doped with resonant gold - nanoparticles (Au - NPs) and non-resonant titanium - nanoparticles (Ti - NPs). The modulation of surface plasmon resonance (SPR) and the implications in the MoO3 transmittance spectrum is described by applying an effective medium theory. The transmittance modulation was modified by variating three parameters, the radius of the NPs, the concentration of the NPs as well as the variation of the MoO3 thin films thickness. It was found that the nanoparticles concentration is the most important parameter in the transmittance modulation. Additionally, the orthorhombic and monoclinic structure of MoO3 was studied, from which it was obtained that the monoclinic structure of the MoO3 doped with Au - NPs favors the reduction in the transmittance values in the visible region which is associated with the increase of the SPR signal. Similar analyses are performed for non-resonant nanoparticles such as Ti, where it was found that optical modulation is not as marked as the case of gold nanoparticles.

Optical sizing of nanoparticles in thin films of nonabsorbing nanocolloids.

We study an optical method to infer the size of nanoparticles in a thin film of a dilute nonabsorbing nanocolloid. It is based on determining the contribution of the nanoparticles to the complex effective refractive index of a suspension from reflectivity versus the angle of incidence curves in an internal reflection configuration. The method requires knowing only approximately the particles' refractive index and volume fraction. The error margin in the refractive index used to illustrate this technique was 2%. The method is applicable to sizing nanoparticles from a few tens of nanometers to about 200 nm in radius and requires a small volume of the sample, in the range of a few microliters. The method could be used to sense nanoparticle aggregation and is suitable to be integrated into microfluidic devices.

Optical Coherent Reflection from a Confined Colloidal Film: Modeling and Experiment.

We study the optical reflectivity of confined colloidal films as a function of the angle of incidence in an internal reflection configuration. Two effective medium models and an extended coherent-scattering model for thin colloidal films are compared against experimental measurements with gold, latex, and titanium dioxide colloids. A derivation of the coherent scattering model for confined colloidal films used in this work is presented in a comprehensive way. The model lies within the framework of the multiple-scattering theory and is valid for any angle of incidence and for colloids of small or large particles compared to the wavelength of light, however, only for small and moderately small particles' volume fraction. Reflectivity versus angle of incidence curves for an opaque colloidal film in an internal reflection configuration show the effects of two critical angles. Within the two critical angles, there is a high sensitivity to the presence of colloidal particles, while the volume of colloidal samples needed is in the microliter range. Upon comparing theory with experiment, no model fitting was done in any case. The experimental setup and its calibration procedure are discussed. The results provide physical insight into applications involving optical properties of colloidal systems.

Analytical modeling of optical reflectivity of random plasmonic nano-monolayers.

In this paper, we compare three different models that have been used to interpret reflectivity measurements of supported monolayers of nanoparticles. Two of them: (i) isotropic Maxwell Garnett and (ii) anisotropic two-dimensional-dipolar model are based on an effective-medium approach, while the third one (iii) coherent-scattering model, lies within the framework of multiple-scattering theory. First, we briefly review, on physical grounds, the foundations of each model and write down the corresponding formulas for the calculation of the reflectivity. In the two-dimensional-dipolar model, the dilute limit of the pair-correlation function (also called hole-correlation function) is always used in the calculation of the effective optical response. Then we use these formulas to plot and analyze graphs of the reflectivity of a monolayer of gold nanoparticles on a glass substrate, as a function of several relevant parameters, for two different commonly used experimental configurations. Finally, we discuss the importance of our results and how they can be used to infer the limits of validity of each model.

Experimental Test of Reflectivity Formulas for Turbid Colloids: Beyond the Fresnel Reflection Amplitudes.

We compare light reflectivity measurements as a function of the angle of incidence for an interface between an optical glass and a turbid suspension of small particles, with theoretical predictions for the coherent reflectance calculated with different available theoretical models. The comparisons are made only in a small range of angles of incidence around the critical angle of the interface between the glass and the matrix of the colloidal suspensions. The experimental setup and its calibration procedure are discussed. We considered two Fresnel-based approximations and another two based on a multiple-scattering approach, and we present results for monodisperse latex colloidal suspensions of polymeric spherical particles in water with particle diameters of 120 and 520 nm, polydisperse titanium dioxide (rutile) particles suspensions in water with a most probable diameter of 404 nm, and suspensions of copper particles in water with diameters of 500 nm. The comparisons between experiment and theory are made without fitting any parameters.