Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields.

Impact of Processing and Physicochemical Parameter on Hibiscus sabdariffa Calyxes Biomolecules and Antioxidant Activity: From Powder Production to Reconstitution

Hibiscus sabdariffa is a tropical plant with red calyxes whose anthocyanins, phenols, and antioxidant activity make it attractive to consumers both from a nutritional and medicinal standpoint. Its seasonality, perishability, and anthocyanin instability, led to the setup of stabilization methods comprising drying and powdering. However, its properties can often be altered during these stabilization processes. Treatments such as dehumidified-air-drying, infrared drying, and oven-drying, and their combination showed better quality preservation. Moreover, powder production enables superior biomolecule extractability which can be linked to a higher bioaccessibility. However, the required temperatures for powder production increase the bioactive molecules degradation leading to their antioxidant activity loss. To overcome this issue, ambient or cryogenic grinding could be an excellent method to improve the biomolecule bioavailability and accessibility if the processing steps are well mastered. To be sure to benefit from the final nutritional quality of the powder, such as the antioxidant activity of biomolecules, powders have to offer excellent reconstitutability which is linked to powder physicochemical properties and the reconstitution media. Typically, the finest powder granulometry and using an agitated low-temperature reconstitution media allow for improving anthocyanin extractability and stability. In this review, the relevant physicochemical and processing parameters influencing plant powder features from processing transformation to reconstitution will be presented with a focus on bioactive molecules and antioxidant activity preservation.

Discrimination of Free-Range and Caged Eggs by Chemometrics Analysis of the Elemental Profiles of Eggshell

As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profiles and chemometrics to discriminate between free-range and caged eggs was established. Free-range (n1 = 127) and caged (n2 = 122) eggs were collected from different producing areas in China. The content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel-Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classification of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specificity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. The result indicates that chemometrics analysis of the elemental profiles of eggshells could provide a useful and effective method to discriminate between free-range and caged eggs.

Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three-dimensional-two-dimensional (3D-2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial-spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D-2D hybrid CNN, resulting in a Y-shaped architecture named Y-Net. The results show that the spectral bands selected by the band selection module of Y-Net achieve more reliable classification performance than traditional feature selection methods. Y-Net obtained the best classification accuracy compared to support vector machines, one-dimensional (1D) CNNs, and two-dimensional (2D) CNNs. After the network pruned the trained Y-Net, the model size was reduced to one-third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.

Effect of protein content on the thermal effusivity of foods

The availability of thermophysical properties of both foods and their constituents is of considerable importance to the industry. The thermal effusivity is one of the less explored thermophysical parameters. It governs the penetration of heat into materials and is defined as the square root of the product of thermal conductivity of the material, volume-specific heat capacity, and density. This paper describes the application of a relatively new inverse photopyroelectric method (IPPE) to determine thermal effusivity of dehydrated whey protein isolate and egg white powder versus protein content. In both cases the effusivity values decreased linearly with increasing protein content. One percent increase in protein content of whey protein isolate and egg white lead to 6.5 and 7.2 Ws1/2 m−2 K−1 decrease in effusivity values, respectively.

Classification of different liquid milk by near-infrared spectroscopy and ensemble modeling

Dairy products are necessary components of a healthy diet for human and nowadays, liquid milk become very popular because of its convenience. The identification of a brand of liquid milk is of importance. In this study, near-infrared (NIR) spectroscopy is used for rapid and objective classification of different brands of liquid milk. Chemometric methods including extreme learning machine (ELM) and its ensemble version (EELM) are investigated and compared. A dataset containing 144 samples from 6 brands are collected for experiment. A model-independent filter algorithm, i.e., relief-based feature selection, was used for variable reduction. Principal component analysis (PCA) is used as a tool of exploratory analysis for visualizing the difference among liquid milk samples of different brands. All samples were divided into three subsets, i.e., the training set, validation set and test set, for constructing, optimizing and testing the model, respectively. The model developed by the EELM procedure achieved 100% of classification accuracy, indicating that NIR spectroscopy combined with variable reduction and the EELM algorithm is feasible for classifying the brands of liquid milk.

Real-Time and In Situ Evaluation of Phycocyanin Concentration in Spirulina platensis Cultivation System by Using Portable Raman Spectroscopy

Spirulina platensis can synthesize a large amount of phycocyanin, which had been developed as a health food. At the same time, Spirulina can absorb the nitrogen and phosphorus in wastewater and provide for its own growth. Here, we studied the optimal nitrogen and phosphorus supply for the Spirulina production process. For the first time, 405 nm portable Raman spectrometer was used to estimate phycocyanin content for real-time industrial applications. We obtained three Raman characteristic peaks of phycocyanin through density functional theory combined with home-built Raman spectrometer, which were 1272, 1337, and 1432 ${\text{cm}}^{-1}$ . There was a good linear correlation between the sum of the three peak intensities and the PCL concentration (y = 18.887x + 833.530, $R^2=0.890$ ). The least squares support vector machine model based on the characteristic peaks was used to estimate the concentration of phycocyanin and obtained good results with a correlation coefficient of prediction of 0.907 and residual predictive deviation of 3.357. The results can provide decision-making for integration of Spirulina effluent treatment and phycocyanin production and provide references for real-time Spirulina-based biorefinery applications.

Trends in feed evaluation for poultry with emphasis on in vitro techniques

Accurate knowledge of the actual nutritional value of individual feed ingredients and complete diets is critical for efficient and sustainable animal production. For this reason, feed evaluation has always been in the forefront of nutritional research. Feed evaluation for poultry involves several approaches that include chemical analysis, table values, prediction equations, near-infrared reflectance spectroscopy, in vivo data and in vitro digestion techniques. Among these, the use of animals (in vivo) is the most valuable to gain information on nutrient utilization and is more predictive of bird performance. However, in vivo methods are expensive, laborious and time-consuming. It is therefore important to establish in vitro methods that are reliable, rapid and practical to assess the nutritional quality of feed ingredients or complete diets. Accuracy of the technique is crucial, as poor prediction will have a negative impact on bird performance and, increase feed cost and environmental issues. In this review, the relevance and importance of feed evaluation in poultry nutrition will be highlighted and the various approaches to evaluate the feed value of feed ingredients or complete diets will be discussed. Trends in and practical limitations encountered in feed evaluation science, with emphasis on in vitro digestion techniques, will be discussed.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling

Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p < 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol; r = −0.57) and total lifetime of foam (TLTF; r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68; overall r = 0.89) as Model 2 (testing r = 0.90; overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.

Evaluation of the effects of mild heat in bovine milk by time resolved fluorescence

Heat treatment of milk and dairy products are indispensable for the dairy industry. This thermal processing intends to extend shelf life, improve quality of the milk and minimize the health risks associated with milk and dairy products. The use of time-resolved fluorescence techniques to identify conformation and structure changes ok milk fat and proteins could help understand the temperature effects in bovine milk. This study aimed to use fluorescence lifetimes to evaluate the effects of heating fresh cow milk up to 85 °C. We observed different tendencies for fluorescence lifetimes submitted to different heating temperatures. The longer lifetime values decreased for temperatures higher than room temperature until it reached a minimum value near 40 °C and it slowly increased again for temperatures higher than 40 °C, indicating two distinct processes. These results indicate that time-resolved fluorescence can assist on the analysis of heating effects in fluid milk.

In-Situ Monitoring and Diagnosing for Process Based on Vibration Sensors

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging

Monitoring the effectiveness of Miscanthus sacchariflorus to meet the basic requirements for environmental remediation projects is an important step in determining its use as a productive bioenergy crop for phytoremediation. Conventional chemical methods for the determination of cadmium (Cd) contents involve time-consuming, monotonous and destructive procedures and are not suitable for high-throughput screening. In the present study, visible and near-infrared hyperspectral imaging technology combined with chemometric methods was used to assess the Cd concentrations in M. sacchariflorus. The total Cd concentrations in different plant tissues were measured using an inductively coupled plasma-mass spectrometer. Partial least-squares regression and least-squares support vector machine were implemented to estimate Cd contents from spectral reflectance. Successive projections algorithm and competitive adaptive reweighted sampling (CARS) methodology were used for selecting optimal wavelength. The CARS-partial least-squares regression model resulted in the most accurate predictions of Cd contents in M. sacchariflorus leaves, with a determination coefficient (R²) of 0.87 and a root mean square error (RMSE) value of 97.78 for the calibration set, and an R² value of 0.91 and a RMSE value of 75.95 for the prediction set. The CARS-least-squares support vector machine model resulted in the most satisfactory predictions of Cd contents in roots, with R² values of 0.95 (RMSE, 0.92 × 10³) for the calibration set and 0.90 (RMSE, 1.64 × 10³) for the prediction set. Finally, the Cd concentrations in different plant tissues were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel. Thus, visible and near-infrared imaging combined with chemometric methods produces a promising technique to evaluate M. sacchariflorus' Cd phytoremediation capability in high-throughput metal-contaminated field applications.

Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology

Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.

Quantitative Determination of Cd in Soil Using Laser-Induced Breakdown Spectroscopy in Air and Ar Conditions

Rapid detection of Cd content in soil is beneficial to the prevention of soil heavy metal pollution. In this study, we aimed at exploring the rapid quantitative detection ability of laser- induced breakdown spectroscopy (LIBS) under the conditions of air and Ar for Cd in soil, and finding a fast and accurate method for quantitative detection of heavy metal elements in soil. Spectral intensity of Cd and system performance under air and Ar conditions were analyzed and compared. The univariate model and multivariate models of partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM) of Cd under the air and Ar conditions were built, and the LS-SVM model under the Ar condition obtained the best performance. In addition, the principle of influence of Ar on LIBS detection was investigated by analyzing the three-dimensional profile of the ablation crater. The overall results indicated that LIBS combined with LS-SVM under the Ar condition could be a useful tool for the accurate quantitative detection of Cd in soil and could provide reference for environmental monitoring.

Assessment of Binding Interaction between Bovine Lactoferrin and Tetracycline Hydrochloride: Multi-Spectroscopic Analyses and Molecular Modeling

In this paper, the interaction between bovine lactoferrin (bLf) and tetracycline hydrochloride (TCH) was researched by microscale thermophoresis (MST), multi-spectroscopic methods, and molecular docking techniques. Normal fluorescence results showed that TCH effectively quenched the intrinsic fluorescence of bLf via static quenching. Moreover, MST confirmed that the combination force between bLf and TCH was very strong. Thermodynamic parameters and molecular docking further revealed that electrostatic forces, van der Waals, and hydrogen bonding forces played vital roles in the interaction between bLf and TCH. The binding distance and energy transfer efficiency between TCH and bLf were 2.81 nm and 0.053, respectively. Moreover, the results of circular dichroism spectra (CD), ultraviolet visible (UV-vis) absorption spectra, fluorescence Excitation-Emission Matrix (EEM) spectra, and molecular docking verified bLf indeed combined with TCH, and caused the changes of conformation of bLf. The influence of TCH on the functional changes of the protein was studied through the analysis of the change of the bLf surface hydrophobicity and research of the binding forces between bLf and iron ion. These results indicated that change in the structure and function of bLf were due to the interaction between bLf and TCH.

Quantitative Analysis of Nutrient Elements in Soil Using Single and Double-Pulse Laser-Induced Breakdown Spectroscopy

Rapid detection of soil nutrient elements is beneficial to the evaluation of crop yield, and it’s of great significance in agricultural production. The aim of this work was to compare the detection ability of single-pulse (SP) and collinear double-pulse (DP) laser-induced breakdown spectroscopy (LIBS) for soil nutrient elements and obtain an accurate and reliable method for rapid detection of soil nutrient elements. 63 soil samples were collected for SP and collinear DP signal acquisition, respectively. Macro-nutrients (K, Ca, Mg) and micro-nutrients (Fe, Mn, Na) were analyzed. Three main aspects of all elements were investigated, including spectral intensity, signal stability, and detection sensitivity. Signal-to-noise ratio (SNR) and relative standard deviation (RSD) of elemental spectra were applied to evaluate the stability of SP and collinear DP signals. In terms of detection sensitivity, the performance of chemometrics models (univariate and multivariate analysis models) and the limit of detection (LOD) of elements were analyzed, and the results indicated that the DP-LIBS technique coupled with PLSR could be an accurate and reliable method in the quantitative determination of soil nutrient elements.

Elemental profile and oxygen isotope ratio (δ18O) for verifying the geographical origin of Chinese wines

The elemental profile and oxygen isotope ratio (δ¹⁸O) of 188 wine samples collected from the Changji, Mile, and Changli regions in China were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES) and isotope ratio mass spectrometry (IRMS), respectively. By combining the data of δ¹⁸O and the concentration data of 52 elements, the analysis of variance (ANOVA) technique was firstly applied to obtain the important descriptors for the discrimination of the three geographical origins. Ca, Al, Mg, B, Fe, K, Rb, Mn, Na, P, Co, Ga, As, Sr, and δ¹⁸O were identified as the key explanatory factors. In the second step, the key elements were employed as input variables for the subsequent partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) analyses. Then, cross validation and random data splitting (training set: test set = 70:30, %) were performed to avoid the over-fitting problem. The average correct classification rates of the PLS-DA and SVM models for the training set were both 98%, while for the test set, these values were 95%, 97%, respectively. Thus, it was suggested that the combination of oxygen isotope ratio (δ¹⁸O) and elemental profile with multi-step multivariate analysis is a promising approach for the verification of the considered three geographical origins of Chinese wines.

Quantitative visualization of pectin distribution maps of peach fruits

Pectin content is an important quality index of fruits, as pectin content undergoes significant changes during the peach ripening process. The commonly used carbazole colorimetry method measures only the total content value of each kind of pectin for each pulp sample and cannot provide distribution maps of the pectin contents for the whole fruit pulp. This work used the hyperspectral imaging technique to quantitatively visualize the distribution maps of pectin contents inside peach pulp at the pixel level. The protopectin contents were well predicted, with the best residual predictive deviation of 2.264, whereas the predictions of the water-soluble pectin and the total pectin contents were not satisfied. On the basis of the best predictive model, the distribution maps of the protopectin contents were quantitatively visualized. A histogram of an example protopectin distribution revealed the existence of a wide range of protopectin contents in peach pulp. Our results show that hyperspectral imaging holds promise as a powerful alternative to the carbazole colorimetry method for measuring the spatial variations in the protopectin distribution inside peach pulp. The distribution maps could be used as a maturity indicator to understand and evaluate the ripening process of peach fruit in depth.

Time resolved fluorescence of cow and goat milk powder

Milk powder is an international dairy commodity. Goat and cow milk powders are significant sources of nutrients and the investigation of the authenticity and classification of milk powder is particularly important. The use of time-resolved fluorescence techniques to distinguish chemical composition and structure modifications could assist develop a portable and non-destructive methodology to perform milk powder classification and determine composition. This study goal is to differentiate milk powder samples from cows and goats using fluorescence lifetimes. The samples were excited at 315nm and the fluorescence intensity decay registered at 468nm. We observed fluorescence lifetimes of 1.5±0.3, 6.4±0.4 and 18.7±2.5ns for goat milk powder; and 1.7±0.3, 6.9±0.2 and 29.9±1.6ns for cow's milk powder. We discriminate goat and cow powder milk by analysis of variance using Fisher's method. In addition, we employed quadratic discriminant analysis to differentiate the milk samples with accuracy of 100%. Our results suggest that time-resolved fluorescence can provide a new method to the analysis of powder milk and its composition.

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil.

Determining the composition of ammonia/water mixtures using short-wave near-infrared spectroscopy

This paper proposes a methodology based on short-wave near-infrared spectroscopy to determine the ammonia content of ammonia/water mixtures with ammonia mass fraction in the range 0.35-0.65. Establishing this methodology meant modeling the relationship between the pressure bar (15-25)bar, temperature (20-50)°C and composition of the ammonia-water in the mixture (0.35-0.65 in ammonia mass fraction) with absorbance at 1033nm. The experiments were designed to optimize experimental work. A 2(3) factorial design+3 center points was used to establish and analyze the significance of the variables in the absorbance using analysis of variance (ANOVA). A linear model for absorbance was obtained using the least squares method. The trueness of the results versus the values obtained was assessed using a reference method; density measurement was chosen for this study. The accuracy of the results in terms of root-mean-square deviation (RMSD) was 3.7%. The methodology proposed represents a fast alternative for the "in-situ" measurement of the ammonia composition of ammonia-water mixtures in absorption refrigeration systems.

The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants

Flavonoids are γ-benzopyrone derivatives, which are highly regarded in these researchers for their antioxidant property. In this study, two new signals processing methods been coupled with UV spectroscopy for spectral resolution and simultaneous quantitative determination of Myricetin, Kaempferol and Quercetin as flavonoids in Laurel, St. John's Wort and Green Tea without the need for any previous separation procedure. The developed methods are continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM) methods integrated with UV spectroscopy individually. Different wavelet families were tested by CWT method and finally the Daubechies wavelet family (Db4) for Myricetin and the Gaussian wavelet families for Kaempferol (Gaus3) and Quercetin (Gaus7) were selected and applied for simultaneous analysis under the optimal conditions. The LS-SVM was applied to build the flavonoids prediction model based on absorption spectra. The root mean square errors for prediction (RMSEP) of Myricetin, Kaempferol and Quercetin were 0.0552, 0.0275 and 0.0374, respectively. The developed methods were validated by the analysis of the various synthetic mixtures associated with a well- known flavonoid contents. Mean recovery values of Myricetin, Kaempferol and Quercetin, in CWT method were 100.123, 100.253, 100.439 and in LS-SVM method were 99.94, 99.81 and 99.682, respectively. The results achieved by analyzing the real samples from the CWT and LS-SVM methods were compared to the HPLC reference method and the results were very close to the reference method. Meanwhile, the obtained results of the one-way ANOVA (analysis of variance) test revealed that there was no significant difference between the suggested methods.

Detection of adulteration in acetonitrile using near infrared spectroscopy coupled with pattern recognition techniques

In this paper, near infrared spectroscopy (NIR) in cooperation with the pattern recognition techniques were used to determine the type of neat acetonitrile and the adulteration in acetonitrile. NIR spectra were collected between 400 nm and 2498 nm. The experimental data were first subjected to analysis of principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Then support vector machine (SVM) were applied to develop classification models and the best parameter combination was selected by grid search. Under the best parameter combination, the classification accuracy rates of three types of neat acetonitrile reached 87.5%, and 100% for the adulteration with different concentration levels. The results showed that NIR spectroscopy combined with SVM could be utilized for determining the potential adulterants including water, ethanol, isopropyl alcohol, acrylonitrile, methanol, and by-products associated with the production of acetonitrile.

Rapid analysis of diesel fuel properties by near infrared reflectance spectra

In this study, based on near infrared reflectance spectra (NIRS) of 441 samples from four diesel groups (-10# diesel, -20# diesel, -35# diesel, and inferior diesel), three spectral analysis models were established by using partial least square (PLS) regression for the six diesel properties (i.e., boiling point, cetane number, density, freezing temperature, total aromatics, and viscosity) respectively. In model 1, all the samples were processed as a whole; in model 2 and model 3, samples were firstly classified into four groups by least square support vector machine (LS-SVM), and then partial least square regression models were applied to each group and each property. The main difference between model 2 and model 3 was that the latter used the direct orthogonal signal correction (DOSC), which helped to get rid of the non-relevant variation in the spectra. Comparing these three models, two results could be concluded: (1) models for grouped samples had higher precision and smaller prediction error; (2) models with DOSC after LS-SVM classification yielded a considerable error reduction compared to models without DOSC.

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries

Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380-1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.