Comparative non-destructive classification of partial waxy wheats using near-infrared and Raman spectroscopy

2019 ◽  
Vol 70 (5) ◽  
pp. 437 ◽  
Author(s):  
Dongli Liu ◽  
Yixuan Wu ◽  
Zongmei Gao ◽  
Yong-Huan Yun
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Triticum Aestivum L ◽  
Waxy Wheat ◽  
Waxy Proteins ◽  
Sample Set

Waxy proteins play a key role in amylose synthesis in wheat. Eight lines of common wheat (Triticum aestivum L.) carrying mutations in the three homoeologous waxy loci, Wx-A1, Wx-B1 and Wx-D1, have been classified by near-infrared (NIR) and Raman spectroscopy combined with chemometrics. Sample spectra from wheat seeds were collected by using a NIR spectrometer in the wave rage 1600–2400 nm, and then Raman spectrometer in the wave range 700–2000 cm–1. All samples were split randomly into a calibration sample set containing 284 seeds (~35 seeds per line) and a validation sample set containing the remaining 92 seeds. Classification of these samples was undertaken by discriminant analysis combined with principal component analysis (PCA) based on the raw spectra processed by appropriate pre-treatment methods. The classification results by discriminant analysis indicated that the percentage of correctly identified samples by NIR spectroscopy was 84.2% for the calibration set and 84.8% for the validation set, and by Raman spectroscopy 94.4% and 94.6%, respectively. The results demonstrated that Raman spectroscopy combined with chemometrics as a rapid method is superior to NIR spectroscopy in classifying eight partial waxy wheat lines with different waxy proteins.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths

2020 ◽  
Vol 28 (4) ◽  
pp. 224-235
Author(s):  
Irina M Benson ◽  
Beverly K Barnett ◽  
Thomas E Helser
Keyword(s):  
Discriminant Analysis ◽  
Spatial Variability ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Otolith Chemistry ◽  
K Nearest Neighbor ◽  
Fisheries Science

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

Lighting the Ivory Track: Are Near-Infrared and Chemometrics Up to the Job? A Proof of Concept

2019 ◽  
Vol 73 (7) ◽  
pp. 816-822 ◽  
Author(s):  
Aoife Power ◽  
Sandy Ingleby ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Near Infrared ◽  
Mammalian Species ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Proof Of Concept ◽  
Classification Rate ◽  
Complementary Method ◽  
Infrared Reflection ◽  
Rapid Tool

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

scholarly journals Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Cui-Wen Fan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Pattern Recognition Methods ◽  
Standard Normal ◽  
Cotton Genotypes ◽  
Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

scholarly journals Classification of chili powder (Capsicum annuum L.) antioxidant activity based on near infrared spectra

Food Research ◽  
2021 ◽  
Vol 5 (S2) ◽  
pp. 51-56
Author(s):  
Y.I. Aprilia ◽  
N. Khuriyati ◽  
A.C. Sukartiko
Keyword(s):  
Antioxidant Activity ◽  
Success Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Spectra ◽  
Medium Level ◽  
Chili Powder ◽  
Pre Treatment

Testing the antioxidant activity of chili powder is often destructive; these methods are expensive, complicated, and lengthy analysis time. Meanwhile, information on antioxidant activity is needed by the industry to determine its quality class in rapid and uncomplicated handling. Therefore, this study was aimed to measure the antioxidant activity of chili powder and classify it into three quality classes, namely high, medium, and low, using Near Infrared (NIR) spectroscopy at spectral wavelengths of 1000-2500 nm and combined with chemometric techniques. The antioxidant activity of the sample was evaluated using the DPPH assay. Processing of the data started with outlier detection using Hotelling's T2 , then confirmed using leverage analysis and influence plot. The data were then processed with Smoothing-Savitzky Golay, SNV, and De-Trending. Principal Component Analysis (PCA) was performed for classifying the samples, which were validated with full crossvalidation. The results showed that the antioxidant activity was detected at a range of 1395 - 2390 nm. De-Trending was the best pre-treatment that successfully classified the low and high levels of antioxidant activity with a success rate of 100% and classified a medium level of antioxidant activity with a success rate of 96.97%.

Comparison of Multivariate Calibration and Discriminant Analysis in Evaluating NIR Spectroscopy for Determination of Meat Tenderness

1997 ◽  
Vol 51 (3) ◽  
pp. 350-357 ◽  
Author(s):  
Tormod Næs ◽  
Kjell Ivar Hildrum
Keyword(s):  
Discriminant Analysis ◽  
Reference Values ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Meat Tenderness ◽  
Intermediate Group ◽  
Calibration Methods

Often the primary goal of analytical measurement tasks is not to find good estimates of continuous reference values but rather to determine whether a sample belongs to one of a number of categories or subgroups. In this paper the potential of different statistical techniques in the classification of raw beef samples in tenderness subgroups was studied. The reference values were based on sensory analysis of beef tenderness of 90 samples from bovine M. longissimus dorsi muscles. The sample set was divided into three categories—very tough, intermediate, and very tender—according to degree of tenderness. A training set of samples was used to find the relationship between category and near-infrared (NIR) spectroscopic measurements. The study indicates that classical discriminant analysis has advantages in comparison to multivariate calibration methods [i.e., principal component regression (PCR)], in this application. One reason for this observation seems to be that PCR underestimates high measurement values and overestimates low values. In this way most samples are assigned to the intermediate group of samples, causing a small number of erroneous classifications for the intermediate subgroup, but a large number of errors for the two extreme groups. With the use of PCR the number of correct classifications in the extreme subgroups was as low as 23%, while the use of discriminate analysis increased this number to almost 60%. The number of classifications in correct or neighbor subgroup for the two extreme subgroups was equal to 97%. A “bias-correction” was also attempted for PCR, and this gave results comparable to the best results obtained by discriminant analysis methods. Test sets used NIR analysis of fresh, raw beef samples with different processing. While this spectroscopic approach had previously been shown to be useful with frozen products, it appears unsuitable at this time for fresh beef. However, its marginal analytical utility proved useful in evaluating the two classification approaches employed in this study.

Classification of amino resins and formaldehyde near infrared spectra using K-nearest neighbors

2019 ◽  
Vol 27 (5) ◽  
pp. 345-353
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Urea Formaldehyde ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Ease Of Use ◽  
Synthesis Process ◽  
K Nearest Neighbors ◽  
Synthetic Adhesives

Amino resins are synthetic adhesives that can be divided into three major types: urea–formaldehyde (UF), melamine–urea–formaldehyde (MUF), or melamine–formaldehyde (MF). When less than 5% of melamine is added to a UF resin, the resin is called a melamine-fortified UF (mUF) resin. The extensive application of these resins in wood-based products is due to their many advantages: ease of use, strong bonding, resistance to wear and abrasion, heat resistance, and relatively low price. Several near infrared (NIR) models have been developed for this type of adhesives and have been used in industrial plants. However, the NIR spectroscopy is sensitive to the type of resin (UF, MUF, MF, or mUF) and even to the synthesis process, therefore different NIR models must be constructed per resin basis. This work presents two methods: (a) a method to distinguish the NIR spectra of formaldehyde from the NIR spectra of amino resins, and (b) a method to classify the NIR spectra of amino resins by class of resin. The method for the separation of formaldehyde from amino resins achieved 100% correct classification for the dataset used. This method was based on defining a baseline cutoff for the NIR spectra at which there were no amino resins bonds overlapping formaldehyde bonds. For the classification of amino resins, this work used the methodology of K-nearest neighbors, up to 91 neighbors, and principal component analysis, up to 10 principal components. The best classification method obtained an accuracy of 96.1% and can be used industrially to automatically select the most suitable NIR model for amino resins, helping to reduce the time taken for an NIR analysis and automatically preventing the wrong selection of NIR models by an operator.

scholarly journals Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Lin Mo ◽  
Tong Wu ◽  
Chao Tan
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Projection Algorithm ◽  
Diagnostic Model ◽  
Full Spectrum ◽  
Analytical Strategy ◽  
Linear Discriminant ◽  
Parsimonious Model

Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm−1) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

Classification of Chemically Modified Celluloses Using a Near-Infrared Spectrometer and Soft Independent Modeling of Class Analogies

1997 ◽  
Vol 51 (12) ◽  
pp. 1826-1835 ◽  
Author(s):  
Olof Svensson ◽  
Mats Josefson ◽  
Frans W. Langkilde
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Cellulose Derivatives ◽  
Type I ◽  
Depth Of Penetration ◽  
Chemically Modified ◽  
Infrared Spectrometer ◽  
Reference Measurements ◽  
Sample Set

A method for classification of eleven chemically modified celluloses has been developed with the use of near-infrared (NIR) spectroscopy and soft independent modeling of class analogies (SIMCA). The sample set consisted of 440 different batches from eleven different cellulose derivatives. A full factorial design in temperature and moisture was made for one sample from each class in order to introduce climate variations in the calibration sample set. Principal components analysis (PCA) models were made for each class, and samples not present in the calibration set were classified according to the SIMCA method. Only one type II error (acceptance of an unacceptable sample) was detected in the classification of the different celluloses. The number of type I errors (rejection of an acceptable sample) ranged from 0 to 14%. Subgroups, due to different manufacturers, viscosities, particle sizes, and degrees of substitution, were detected and correctly classified. The sample presentation, focus of the instrument, number of reference measurements, depth of penetration, and selection of training set samples are discussed.

scholarly journals Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

2018 ◽  
Vol 11 (02) ◽  
pp. 1850005 ◽  
Author(s):  
Lijun Yao ◽  
Weiqun Xu ◽  
Tao Pan ◽  
Jiemei Chen
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Large Population ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Moving Window ◽  
Linear Discriminant ◽  
Transgenic Sugarcane ◽  
Polynomial Formula

The moving-window bis-correlation coefficients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and [Formula: see text]-thalassemia with visible and near-infrared (Vis–NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG) smoothing with first-order derivative ([Formula: see text]), third-degree polynomial ([Formula: see text]) and 25 smoothing points ([Formula: see text]). The selected waveband was 736–1054[Formula: see text]nm with MW-BiCC, and the positive and negative validation recognition rates ([Formula: see text]_REC[Formula: see text], [Formula: see text]_REC[Formula: see text] were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 [Formula: see text]-thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with [Formula: see text], [Formula: see text] and [Formula: see text]. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342[Formula: see text]nm). The [Formula: see text]_REC[Formula: see text] and [Formula: see text]_REC[Formula: see text] were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing [Formula: see text]-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

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