Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization.

Problems: Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model.

Aims: Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information.

Rapid analysis the type of customs paper using Micro-NIR spectrometers and machine learning algorithms.

Quick identification of paper types for customs is extremely crucial. Although there are a variety of researches focus on the discrimination of paper, these techniques either require complex preprocessing or large-scale instruments, which are not suitable for customs environments. In this study, we predicted the type of customs paper by using a Micro-NIR spectrometer, and compared the results with Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR).

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types.

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy.

Feature selection of infrared spectra analysis with convolutional neural network.

Data-driven deep learning analysis, especially for convolution neural network (CNN), has been developed and successfully applied in many domains. CNN is regarded as a black box, and the main drawback is the lack of interpretation. In this study, an interpretable CNN model was presented for infrared data analysis.

Non-destructive analysis the dating of paper based on convolutional neural network.

A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.

A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine.

Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range.

High-level Fusion Coupled with Mahalanobis Distance Weighted (MDW) Method for Multivariate Calibration.

Near infrared spectra (NIR) technology is a widespread detection method with high signal to noise ratio (SNR) while has poor modeling interpretation due to the overlapped features. Alternatively, mid-infrared spectra (MIR) technology demonstrates more chemical features and gives a better explanation of the model. Yet, it has the defects of low SNR.

A new hybrid filter/wrapper algorithm for feature selection in classification.

Feature selection can greatly enhance the performance of a learning algorithm when dealing with a high dimensional data set. The filter method and the wrapper method are the two most commonly approaches. However, these approaches have limitations.

Bootstrapping search margin-based nearest neighbor method for qualitative spectroscopic analysis.

Qualitative spectroscopic analysis depends in one way or another on comparing spectra of the specimens to be identified with spectra of "known" or "standard" samples. The k-nearest neighbor (k-NN) method is one of the oldest and simplest techniques for performing such comparisons. In this study, we present a new k-NN algorithm for qualitative spectroscopic analysis, which we refer to as the bootstrapping search margin-based nearest neighbor (BSMNN) method.

A new strategy of applying modeling indicator determined method to high-level fusion for quantitative analysis.

A novel method, named as modeling indicator determined (MID) method, based on two model evaluation parameters i.e., root mean square error of prediction (RMSEP) and ratio performance deviation (RPD), is proposed to employ high-level fusion for quantitative analysis.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of paper relic types.

The paper relic identification is a pending issue to be resolved in the field of cultural heritage. As we all known, heritage paper has significant importance in archaeological research. Nowadays, there are a variety of research methodologies focuses on the analysis of inks for dating documents.

Spectral interval combination optimization (ICO) on rapid quality assessment of plant: a validation study.

A novel spectral variable selection method, named as interval combination optimization (ICO), was proposed in the previous study of us. In the present study, ICO coupled with near infrared (NIR) spectroscopy was applied to the rapid determination of four primary constituents including total sugar, reducing sugar, total nitrogen and nicotine in plant. Partial least squares regressions was performed after ICO algorithm.

An ensemble variable selection method for vibrational spectroscopic data analysis.

Wavelength selection is a critical factor for pattern recognition of vibrational spectroscopic data. Not only does it alleviate the effect of dimensionality on an algorithm's generalization performance, but it also enhances the understanding and interpretability of multivariate classification models. In this study, a novel partial least squares discriminant analysis (PLSDA)-based wavelength selection algorithm, termed ensemble of bootstrapping space shrinkage (EBSS), has been devised for vibrational spectroscopic data analysis.

Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm.

A novel chemometrical method, named as MWS-ECARS, which is based on using the moving window smoothing upon an ensemble of competitive adaptive reweighted sampling, is proposed as the spectral variable selection approach for multivariate calibration in this study. In terms of elimination of uninformative variables, an ensemble of CARS is carried out first and MWS is then performed to search for effective variables around the high frequency variables. The variable subset with the lowest standard error of cross-validation (SECV) is treated as the optimal threshold and the corresponding moving window width is regarded as the optimal window width.

Spectral interval optimization on rapid determination of prohibited addition in pesticide by ATR-FTIR.

Background: Acetamiprid, as a low-toxicity pesticide, has already been extensively used to increase plant production and quality. Although fipronil has been prohibited, it is usually illicitly added to acetamiprid due to its particular insecticidal action and effect, so it is highly desirable to obtain a rapid and effective method to detect its concentration. Mid-infrared spectroscopy (MIR) combined with two variable selection methods, interval combination optimization (ICO) and interval partial least squares (iPLS), were used to determinate the prohibited addition of fipronil.

A modification of the bootstrapping soft shrinkage approach for spectral variable selection in the issue of over-fitting, model accuracy and variable selection credibility.

In this study, we proposed a new computational method stabilized bootstrapping soft shrinkage approach (SBOSS) for variable selection based on bootstrapping soft shrinkage approach (BOSS) which can enhance the analysis of chemical interest from the massive variables among the overlapped absorption bands. In SBOSS, variable is selected by the index of stability of regression coefficients instead of regression coefficients absolute value. In each loop, a weighted bootstrap sampling (WBS) is applied to generate sub-models, according to the weights update by conducting model population analysis (MPA) on the stability of regression coefficients (RC) of these sub-models.

Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data.

Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIR1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA).

Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy.

Rapid and nondestructive near infrared spectroscopy (NIR) methods have been developed for simultaneous qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine in seized samples. This is the first systematic report regarding a qualitative and quantitative procedure of applying NIR for drug analysis. A total of 282 calibration samples and 836 prediction samples were used for the building and validating of qualitative and quantitative models.

Geometric search: A new approach for fitting PARAFAC2 models on GC-MS data.

PARAFAC2 is a powerful decomposition method which is ideally suited for modeling gas chromatography-mass spectrometry (GC-MS) data. However, the most widely used fitting algorithms (alternating least squares, ALS) are very slow which hinders use of the model. In this paper, an iterative method called geometric search is proposed to fit the PARAFAC2 model.

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy.

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.

A novel algorithm for spectral interval combination optimization.

In this study, a new wavelength interval selection algorithm named as interval combination optimization (ICO) was proposed under the framework of model population analysis (MPA). In this method, the full spectra are divided into a fixed number of equal-width intervals firstly. Then the optimal interval combination is searched iteratively under the guide of MPA in a soft shrinkage manner, among which weighted bootstrap sampling (WBS) is employed as random sampling method.

[Multivariate Calibration Combined with Mass Spectrometry for Rapid Analysis].

A mixture of four substances of benzaldehyde, iso-octane, butyl acetate, acetophenone were quantitatively analyzed by mass spectrometry combined with chemometrics. The mass chromatogram data of mixture were proceeded with two methods for quantitative analysis. One is feature selection--Multiple Linear Regression (MLR) and the other is full spectrum--Partial Least Squares (PLS).

The application of directly suspended droplet microextraction for the evaluation of phthalic acid esters in cow's milk by gas chromatography mass spectrometry.

A simple and rapid method was developed for evaluating 16 phthalic acid esters (PAEs) at the μg/kg level in a complex milk matrix using directly suspended droplet microextraction-gas chromatography mass spectrometry (DSDME-GC-MS). The different parameters for extraction and for the DSDME experiment were optimized, including You are free to submit the revised manuscript at a later date as a new submission. 10 g/L trichloroacetic acid concentration, 100 μL cyclohexane micro-droplet organic solvent, 1100 rpm stirring speed, 10 min extraction time and no salt amount.

Distribution assessment and quantification of counterfeit melamine in powdered milk by NIR imaging methods.

This paper presents a rapid calculation method for the imaging process in the identification and quantification of prohibited additives in milk. Data abstraction methods such as principal component analysis (PCA), classical least squares regression (CLS), and alternative least squares regression (ALS) were used. Different multivariate calculations provided possibilities of quantifying near-infrared (NIR) spectral data cube obtained from the surface of the complex mixture.