Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

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Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Foods ◽

10.3390/foods9111551 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1551

Author(s):

Annalisa De Girolamo ◽

Salvatore Cervellieri ◽

Erminia Mancini ◽

Michelangelo Pascale ◽

Antonio Francesco Logrieco ◽

...

Keyword(s):

Durum Wheat ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Linear Discriminant ◽

Production And Consumption ◽

High Prediction ◽

Validation Set ◽

Performance Results ◽

Prediction Capability

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

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Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

Journal of Chemistry ◽

10.1155/2015/619685 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Hui Chen ◽

Zan Lin ◽

Chao Tan

Keyword(s):

Cancer Diagnosis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Biomedical Analysis ◽

First Derivative ◽

Svm Model ◽

High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520987315 ◽

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.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520935999 ◽

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.

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

BioMed Research International ◽

10.1155/2015/472197 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 2

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.

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Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China’s Consumer Market

Foods ◽

10.3390/foods10122986 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2986

Author(s):

Xianshu Fu ◽

Xuezhen Hong ◽

Jinyan Liao ◽

Qingge Ji ◽

Chaofeng Li ◽

...

Keyword(s):

Near Infrared ◽

Geographical Origin ◽

Geographic Origin ◽

Nir Spectroscopy ◽

Mineral Elements ◽

Mineral Element ◽

Consumer Market ◽

Linear Discriminant ◽

Discrimination Rate ◽

Rapid Discrimination

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R2 value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

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

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545818500050 ◽

2018 ◽

Vol 11 (02) ◽

pp. 1850005 ◽

Cited By ~ 2

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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Nondestructive Discrimination of Grape Seed Varieties Using UV-VIS-NIR Spectroscopy and Chemometrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.236-237.89 ◽

2012 ◽

Vol 236-237 ◽

pp. 89-94 ◽

Cited By ~ 4

Author(s):

Hai Qing Yang ◽

Wei Qiang Luo ◽

Wen Jing Wang

Keyword(s):

Near Infrared ◽

Full Range ◽

Ultra Violet ◽

Nir Spectroscopy ◽

Principal Component ◽

Calibration Method ◽

Grape Seed ◽

Support Vector ◽

Grape Seeds ◽

Linear Discriminant

Classification of grape seed species is a useful tool to obtain seeds with desired quality traits. This study aimed at rapidly and nondestructively discriminating four varieties of grape seeds using ultra violet, visible and near infrared (UV-VIS-NIR) spectroscopy with wavelength range of 2101100 nm. A hundred twenty grape seed samples were divided for calibration (n=80) and validation (n=40). The spectra were subjected to a principal component analysis (PCA) with the leading 10 principal components (PCs) used to build calibration models. The obtained PCs were treated by linear discriminant analysis (LDA), artificial neural network (ANN) and support vector machine (SVM) to build various discrimination models. Validation results showed that the PC-LDA model developed for the full range of UV-VIS-NIR achieved better performance than those developed for partial wavelengths, i.e. UV, VIS, NIR, UV-VIS, and VIS-NIR. The PC-LDA model with 8 PCs achieved best performance with 100% discrimination accuracy. This experiment suggests that the UV-VIS-NIR spectroscopy coupled with PC-LDA calibration method is promising for the nondestructive discrimination of grape seed varieties.

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Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

Czech Journal of Food Sciences ◽

10.17221/1125-cjfs ◽

2008 ◽

Vol 26 (No. 5) ◽

pp. 360-367 ◽

Cited By ~ 21

Author(s):

Q. Chen ◽

J. Zhao ◽

M. Liu ◽

J. Cai

Keyword(s):

Pattern Recognition ◽

Camellia Sinensis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Ann Model ◽

Accurate Identification ◽

Linear Discriminant ◽

Non Invasive ◽

Artificial Neural Network Ann

Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea (<I>Camellia sinensis</I> L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.

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The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.458 ◽

2005 ◽

Vol 13 (2) ◽

pp. 63-68 ◽

Cited By ~ 26

Author(s):

E. Corbella ◽

D. Cozzolino

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Sample Preparation ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Models ◽

Linear Discriminant

This study reports the use of visible (vis) and near infrared (NIR) spectroscopy as a tool to classify honey samples from Uruguay, according to their floral origin. Classification models were developed using principal component analysis, discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Honey samples ( n = 50) from two floral origins, namely Eucalyptus spp. and pasture, were split randomly into even calibration ( n = 25) and validation sets ( n = 25). Both LDA and DPLS models correctly classified, on average, more than 75% of the honey samples belonging to pasture and more than 85% of the honey samples belonging to Eucalyptus spp. These results showed that vis-NIR might be a suitable and alternative method that can easily be implemented by both the industry and retailers to classify samples according their floral origin. Vis-NIR analysis requires little sample preparation and is rapid. However, the relatively limited number of samples involved in the present work led us to be cautious in terms of extrapolating the results of this work to other floral types.

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