Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.     

Fast analysis of complex metallic alloys by double-pulse time-integrated Laser-Induced Breakdown Spectroscopy

Results are reported on the application of double-pulse Laser-Induced Breakdown Spectroscopy (LIBS) for fast analysis of complex metallic alloys. The approach followed for the determination of the composition of the alloys is based on the time-integrated acquisition of LIBS spectra emitted by plasmas induced by collinear double-pulse laser excitation. The spectra are analysed using the Partial Least Squares method, which allows the determination of sample composition even in the presence of strong spectral interferences. The results shown indicate the possibility of measuring the composition of complex metallic alloys in very short times and using relatively cheap LIBS instrumentation.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Determination of phytoplankton composition using absorption spectra

Characterisation of phytoplankton communities in aquatic ecosystems is a costly task in terms of time, material and human resources. The general objective of this paper is not to replace microscopic counts but to complement them, by fine-tuning a technique using absorption spectra measurements that reduces the above-mentioned costs. Therefore, the objective proposed in this paper is to assess the possibility of achieving a qualitative determination of phytoplankton communities by classes, and also a quantitative estimation of the number of phytoplankton cells within each of these classes, using spectrophotometric determination.Samples were taken in three areas of the Spanish Mediterranean coast. These areas correspond to estuary systems that are influenced by both continental waters and Mediterranean Sea waters. 139 Samples were taken in 7-8 stations per area, at different depths in each station. In each sample, the absorption spectrum and the phytoplankton classes (Bacyllariophyceae (diatoms), Cryptophyceae, Clorophyceae, Chrysophyceae, Prasynophyceae, Prymnesophyceae, Euglenophyceae, Cyanophyceae, Dynophyceae and the Synechococcus sp.) were determined.Data were analysed by means of the Partial Least Squares (PLS) multivariate statistical technique. The absorbances obtained between 400 and 750 nm were used as the independent variable and the cell/l of each phytoplankton class was used as the dependent variable, thereby obtaining models which relate the absorbance of the sample extract to the phytoplankton present in it. Good results were obtained for diatoms (Bacillarophyceae), Chlorophyceae and Cryptophyceae.     

Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380–1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on ‘Manual Prawn Mask’ and ‘Automatic Prawn Mask’, respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.     

Measurement of pork freshness using potentiometric sensor

This study evaluated pork freshness using potentiometric solid-state electrodes in order to detect chemical indices such as reduced compounds, organic compounds and sulfides, which are produced during the initial stage of putrefaction in meat. Pt, CuS and Ag₂S electrodes selected as solid-state electrodes have, respectively, been used to detect the organic compounds (regarded as chemical indices of deterioration in meat freshness). The outputs of these electrodes have been analyzed by principal component analysis (PCA) and multiple regression analysis (MRA) in order to find the correlation with the results of viable bacterial counts. By using the potentiometric sensor, the pork freshness was evaluated and the PCA and MRA corresponded to the degree of bacterial increases more simply and rapidly than other methods such as viable bacterial counts or a biosensor.     

Spectrophotometric Simultaneous Determination of Amaranth,Ponceau 4R,Allura Red and Red 2G by Partial Least Squares and Principal Component Regression Multivariate Calibration

ABSTRACT

The multivariate calibration methods, partial least square regression type 1 (PLS 1) and principal component regression (PCR), were proposed for the simultaneous spectrophotometry determination of Amaranth (E-123), Ponceau 4R (E-124), Allura red (E-129) and Red 2G (E-128) in their mixtures. The parameters of the chemometric procedures were optimized and the proposed method was validated with synthetic samples and applied to analyze these dyes in spiked samples of beverages with satisfactory results.     

Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging

A rapid method based on hyperspectral imaging for detection of Escherichia coli contamination in fresh vegetable was developed. E. coli K12 was inoculated into spinach with different initial concentrations. Samples were analyzed using a colony count and a hyperspectroscopic technique. A hyperspectral camera of 400-1000 nm, with a spectral resolution of 5 nm was employed to acquire hyperspectral images of packaged spinach. Reflectance spectra were obtained from various positions on the sample surface and pretreated using Sawitzky-Golay. Chemometrics including principal component analysis (PCA) and artificial neural network (ANN) were then used to analyze the pre-processed data. The PCA was implemented to remove redundant information of the hyperspectral data. The ANN was trained using Bayesian regularization and was capable of correlating hyperspectral data with number of E. coli. Once trained, the ANN was also used to construct a prediction map of all pixel spectra of an image to display the number of E. coli in the sample. The prediction map allowed a rapid and easy interpretation of the hyperspectral data. The results suggested that incorporation of hyperspectral imaging with chemometrics provided a rapid and innovative approach for the detection of E. coli contamination in packaged fresh spinach.     

基于高光谱成像结合分光光度技术的喷墨打印墨水种类鉴别方法

在法庭科学实践中,往往需要通过对文件中字迹墨水的成分分析来精确地判定检材和样本文件的同一性。利用高光谱成像和分光光度技术结合化学计量法,提出了一种对喷墨打印墨水分类的方法。采集14台不同品牌、型号的四色喷墨打印墨水高光谱数据和色度值。计算出平均色度值后进行PCA降维处理和K-Means聚类分析,将样品初步分类。之后应用LightGBM模型、XGBoost模型和SVM模型共三种分类模型,以1:4的比例确定测试集和训练集,对聚类分析结果中每一类别的样品进行逐一鉴别。结果表明,LightGBM和XGBoost对四色样品的分类精度都能达到95%以上,SVM的分类精度为100%。提出的方法能够做到无损、准确、快速地将不同品牌乃至型号的喷墨打印墨水进行区分。     

A review of recent trends in polymer characterization using non-destructive vibrational spectroscopic modalities and chemical imaging

This review focuses on the recent developments in vibrational spectroscopy and chemical imaging (i.e. Raman, Near Infrared, Mid Infrared) to characterize polymers in diverse forms, their behaviour and transient phenomenon. First, important polymeric properties and traditional methods of their characterization are outlined. Then relative advantages & disadvantages have been presented of different characterization methods are presented. This is followed by a detailed review of applications of chemical imaging and spectroscopic techniques in polymer characterization, including the limitations encountered. The article ends with a discussion on the future of chemical imaging with regards to polymer characterization.     

On-line monitoring of all-acrylic emulsion polymerization reactors by Raman spectroscopy

Fourier Transform Raman spectroscopy was used as an on-line sensor in order to monitor high solids content (50 wt%) n-BA/MMA emulsion copolymerization reactions. Due to the similarity of the chemical structure of the monomers, no separate bands could be detected for each monomer, and therefore a multivariate calibration technique was required (Partial Least Squares Regression, PLSR). Using experimental data from several semi-batch reactions independent PLSR models were built for the solids content, cumulative copolymer composition and unreacted amounts of n-BA and MMA. Those models were experimentally validated by monitoring reactions not used for calibration. It is demonstrated that FT-Raman spectroscopy can be successfully applied to on-line monitor emulsion polymerization reactors. This technique also shows a high potential for process control purposes because independent information about several molecular properties can be obtained from a single apparatus.     

Exploring Omics data from designed experiments using analysis of variance multiblock Orthogonal Partial Least Squares

Many experimental factors may have an impact on chemical or biological systems. A thorough investigation of the potential effects and interactions between the factors is made possible by rationally planning the trials using systematic procedures, i.e. design of experiments. However, assessing factors' influences remains often a challenging task when dealing with hundreds to thousands of correlated variables, whereas only a limited number of samples is available. In that context, most of the existing strategies involve the ANOVA-based partitioning of sources of variation and the separate analysis of ANOVA submatrices using multivariate methods, to account for both the intrinsic characteristics of the data and the study design. However, these approaches lack the ability to summarise the data using a single model and remain somewhat limited for detecting and interpreting subtle perturbations hidden in complex Omics datasets.     

A Partial Least-Squares Multivariate Calibration Method for the Simultaneous Spectrophotometric Determination of Spironolactone,Canrenone and Hydrochlorothiazide

Abstract

An application of a partial least squares calibration method for the simultaneous spectrophotometric determination of spironolactone, canrenone and hydrochlorothiazide is suggested. The use of the PLS for the multicomponent spectrophotometric determination is demonstrated on the analysis of several synthetic mixtures. It was shown that is possible to resolve complex mixtures of     

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.     

Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy

Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and aftertaste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting-PLS (IPW-PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW-PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra.     

Ammonium and Phosphate Quantification in Wastewater by Using a Voltammetric Electronic Tongue

Ammonia (NH₄? N) and orthophosphate (PO₄? P) quantification in wastewater treatment plants is important due to their implication in the eutrophication process. A voltammetric electronic tongue as a tool for the prediction of ammonia and orthophosphate concentrations from influent and effluent wastewater is proposed herein. An electrochemical study of the response of the ammonium and orthophosphate ions was performed in order to design a suitable waveform for each electrode. Partial Least Squares analysis was used to obtain a correlation between the data from the tongue and the concentrations of ammonia and orthophosphate measured in the laboratory showing good predictive power.     

Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells

A new setup for Raman spectroscopic wide-field imaging is presented. It combines the advantages of a fiber array based spectral translator with a tailor-made laser illumination system for high-quality Raman chemical imaging of sensitive biological samples.