Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa.

Tegillarca granosa (T. granosa) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa. However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa. The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa, with a coefficient of determination (Rp2) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa.

Non-Destructive Measurement of Egg's Haugh Unit by Vis-NIR with iPLS-Lasso Selection.

Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg's freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg's freshness, and ranged 56-91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg's freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg's HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg's freshness.

Recent Advances in Silver and Gold Nanoparticles-Based Colorimetric Sensors for Heavy Metal Ions Detection: A Review.

Silvetr and gold nanoparticles-based colorimetric sensors (Ag/Au-NPs-CSns) allow potential prospects for the development of efficient sensors owing to their unique shape- and size-dependent optical properties. In this review, recent (2020) advances in morphology-controllable synthesis, shape/size-dependent performance, sensing mechanism, challenges and prospects of Ag/Au-NPs-CSns for the detection of heavy metals are discussed. The size/shape-controlled synthesis of innovative Ag/Au-NPs-CSns is reviewed critically and the possible role of different parameters like temperature, time, pH, stabilizing/capping agents, reducing agents and concentration/nature of precursors are discussed. Then, we highlighted how the shape, size, optimum composition, functionalization, stabilizing/capping agents, surface structure and synergism influence the optical properties and sensing efficiency of Ag/Au-NPs-CSns. This review attempted to accentuate the efficiency of novel multimetallic Ag/AuNPs-CSns as compare to their monometallic counterparts and explained how the incorporation of multi-metals affects their performance. Besides, the sensing mechanisms of Ag/Au-NPs-CSns with special reference to recently published studies are discussed. Finally, challenges and prospects in the controllable-synthesis and practical applications of these sensors are studied. This mechanistic approach and timely review can be promisingly considered as a valuable reference and will help fuel new ideas for the development of novel colorimetric sensors. HighlightsA review of recent advances in Ag/Au-NPs-CSns for heavy metal ions detectionMorphology of Ag/Au-NPs-CSns regulate their optical properties/sensing efficiencyPromising Ag/Au-NPs-CSns can be synthesized by adjusting experimental parametersHybrid and functionalized Ag/Au-NPs-CSns have superior sensing performanceSize/shape transformation, aggregation/anti- and oxidation are sensing mechanisms.

Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy.

In order to reduce the uncertainty of the genetic algorithm (GA) in optimizing the near-infrared spectral calibration model and avoid the loss of spectral information of the unselected variables, a strategy of fusing consensus models is proposed to measure the soluble solids content (SSC) in peaches. A total of 266 peach samples were collected at four arrivals, and their interactance spectra were scanned by an integrated analyzer prototype, and then an internal index of SSC was destructively measured by the standard refractometry method. The near-infrared spectra were pre-processed with mean centering and were selected successively with a genetic algorithm (GA) to construct the consensus model, which was integrated with two member models with optimized weightings. One was the conventional partial least square (PLS) optimized with GA selected variables (PLSGA), and the other one was the derived PLS developed with residual variables after GA selections (PLSRV). The performance of PLSRV models showed some useful spectral information related to peaches' SSC and someone performed close to the full-spectral-based PLS model. Among these 10 runs, consensus models obtained a lower root mean squared errors of prediction (RMSEP), with an average of 1.106% and standard deviation (SD) of 0.0068, and performed better than that of the optimized PLSGA models, which achieved a RMSEP of average 1.116% with SD of 0.0097. It can be concluded that the application of fusion strategy can reduce the fluctuation uncertainty of a model optimized by genetic algorithm, fulfill the utilization of the spectral information amount, and realize the rapid detection of the internal quality of the peach.

Characterization and source analysis of heavy metals contamination in microplastics by Laser-Induced Breakdown Spectroscopy.

The increasing presence of microplastics in marine environment is a critical issue and the plastic-metal contamination has received much attention. However, conventional methods for heavy metal determination are time-consuming, need sample pretreatments, require a strict operation environment, or have high limits of detection. In this study, heavy metals contaminated microplastics samples collected from a remote coral island were quantified and analyzed by using Laser-Induced Breakdown Spectroscopy (LIBS). The characters of the trace metals in microplastics were used to determine the sources of the contaminants, and the potential origins of the metals were demonstrated from the statistical analysis. LIBS is a facile and non-destructive trace analysis technique and the strategy led to rapid and multi-metals detection of individual samples. Heavy metals such as copper (Cu), lead (Pb), iron (Fe), cadmium (Cd), zinc (Zn), manganese (Mn), chromium (Cr) were detected and quantified in the individual microplastics samples. The findings showed that LIBS is a promising strategy for the characterization of microplastics and for the analysis of the source of heavy metals contaminants present in the microplastics particles.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum.

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

SERS-based lateral flow immunoassay for sensitive and simultaneous detection of anti-SARS-CoV-2 IgM and IgG antibodies by using gap-enhanced Raman nanotags.

The lateral flow immunoassay (LFIA) has played a crucial role in early diagnosis during the current COVID-19 pandemic owing to its simplicity, speed and affordability for coronavirus antibody detection. However, the sensitivity of the commercially available LFIAs needs to be improved to better prevent the spread of the infection. Here, we developed an ultra-sensitive surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFIA) strip for simultaneous detection of anti-SARS-CoV-2 IgM and IgG by using gap-enhanced Raman nanotags (GERTs). The GERTs with a 1 nm gap between the core and shell were used to produce the "hot spots", which provided about 30-fold enhancement as compared to conventional nanotags. The COVID-19 recombinant antigens were conjugated on GERTs surfaces and replaced the traditional colloidal gold for the Raman sensitive detection of human IgM and IgG. The LODs of IgM and IgG were found to be 1 ng/mL and 0.1 ng/mL (about 100 times decrease was observed as compared to commercially available LFIA strips), respectively. Moreover, under the condition of common nano-surface antigen, precise SERS signals proved the unreliability of quantitation because of the interference effect of IgM on IgG.

Degradation degree analysis of environmental microplastics by micro FT-IR imaging technology.

The degradation potential of microplastics remains a critical issue for researching marine litter, and it is one of the most important factors that can be used for calculating the persistence time of microplastics in certain conditions. However, there are lack of standard or approved methods for estimating the ageing stage of environmental microplastics. In this study, the potential of spectral-image fusion strategy was investigated to analyze the degradation degree of polyethylene microplastics in natural exposure of coastline. The proposed spectral-image fusion linear model showed a significant ability to classify the degradation degree of environmental microplastics samples with the best accuracy of 97.1% as compared to two single-sensing information-based linear models (with one spectral wavelength of the carbonyl index at 1720 cm-1 or three-channel components from LAB color-space). This is the first attempt to qualitatively measure the degradation degree of the naturally exposed microplastics based on spectral-image fusion model. The proposed fusion model based strategy is an effective tool for predicting the degradation degree of the field exposed microplastics.

Simultaneously and quantitatively analyze the heavy metals in Sargassum fusiforme by laser-induced breakdown spectroscopy.

As a nutritious and popular seafood among consumers, Sargassum fusiforme is susceptible to the toxic heavy metals because of its strong adsorption properties. In this study, laser-induced breakdown spectroscopy (LIBS) coupled with a simple framework (only remove some noise and low-intensity variables, and then combine with PLS algorithm) was used to establish the detection models to simultaneously and quantitatively analyze the content of heavy metals arsenic (As), chromium (Cd), cadmium (Cr), copper (Cu), mercury (Hg), lead (Pb) and zinc (Zn) in Sargassum fusiforme. As comparisons, three classic variable methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and variable importance in projection (VIP) were adopted. The final results showed that six of seven heavy metal models from the TV-PLSR model were optimal. These results demonstrate that the TV-PLSR framework combined with LIBS technique is an effective framework for quantitatively analyzing the heavy metals in Sargassum fusiforme.

Rapid Assessment of Exercise State through Athlete's Urine Using Temperature-Dependent NIRS Technology.

Athletes usually take nutritional supplements and perform the specialized training to improve the performance of sport. A quick assessment of their athletic status will help to understand the current physical function of athletes' status and the effect of nutritional supplementation. Human urine, as one of the most important body indicators, is composed of many metabolites, which can provide effective monitoring information for physical conditions. In this study, temperature-dependent near-infrared spectroscopy (NIRS) technology was used to collect the spectra of athlete's urine for evaluating the feasibility of rapidly detecting the exercise state of the basketball player. To obtain the detection results accurately, several chemometrics methods including principal component analysis (PCA), variables selection method of variable importance in projection (VIP), continuous 1D wavelet transform (CWT), and partial least square-discriminant analysis (PLS-DA) were employed to develop a classifier to distinguish the physical status of athletes. The optimal classifying results were obtained by wavelet-PLS-DA classifier, whose average precision, sensitivity, and specificity are all above 0.95, and the overall accuracy of all samples is 0.97. These results demonstrate that temperature-dependent NIRS can be used to rapidly assess the physical function of athlete's status and the effect of nutritional supplementation is feasible. It can be believed that temperature-dependent NIR spectroscopy will obtain applications more widely in the future.

Synthetic spectra generated by boundary equilibrium generative adversarial networks and their applications with consensus algorithms.

One of the major restrictions in spectroscopic analysis is the limited number of calibrations, especially for biological samples. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from the real spectra of limited samples. Thus in this work, a boundary equilibrium generative adversarial network (BEGAN) was proposed to automatically generate synthetic spectra and successfully produce spectra from two datasets. Then, the impact of the diversity ratio was estimated in the aspect of the quality and diversity of the generated spectra by BEGAN, and a negative correlation was found between quality and diversity. Finally, these synthetic spectra are applied in a consensus algorithm named creating diversity partial least squares (CDPLS) to replenish virtual samples in every iteration. Results show that the synthetic spectra generated by BEGAN are of high quality and improve the predictive performance of CDPLS. It can concluded that BEGAN has the potential to generate derived homologous spectra and expand the number of spectra in some small sample sets.

Using Class-Specific Feature Selection for Cancer Detection with Gene Expression Profile Data of Platelets.

A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, small samples, and collinear data. The strategy of one-against-all (OVA) was employed to decompose the multi-classification problem into a series of binary classification problems. The elastic net was used to select class-specific features for the binary classification problems, and the probabilistic support vector machine was used to make the outputs of the binary classifiers with class-specific features comparable. Simulation data and gene expression profile data were intended to verify the effectiveness of the proposed method. Results indicate that the proposed method can automatically select class-specific features and obtain better performance of classification than that of the conventional multi-class classification methods, which are mainly based on global feature selection methods. This study indicates the proposed method is suitable for general multi-classification problems featured with high-dimension, small samples, and collinear data.

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa.

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments.

[Simplification of NIR model for citrus's sugar content based on sensory methods].

The prediction of sugar content (SC) in citrus by near-infrared spectroscopy (NIRS) and sensory test was investigated the validation whether the result of non-destructive determination methods by NIRS can meet the request of consumers' sensory or not, and the simplification of the prediction model of NIRS for citrus's SC with variables selection on the basis of meeting their demands. Result of the latter analyzed by one-way ANOVA shows that there was a significant difference influenced by individual diversity, but not by gender. After excluding the sensuous outliers, root mean standard error of deviation (RMSED) of every participator was calculated and the minimum equaled to 0.633, which was chosen as borderline of NIR model's RMSEP to meet the sensory request Then, combined with spectral preprocessing and variables selection methods, SPA-MLR model was obtained by its robustness with Rp = 0.86, as well as RMSEP = 0.567 for prediction set, furthermore, prediction time just costs 6.8 ms. The achievement that not only meets the customers' sensory, but also simplifies the prediction model can be a good reference for real time application in future.